Biophysics reviews最新文献

{"title":"Capturing the illusive ring-shaped intermediates in A<b>β</b>42 amyloid formation.","authors":"Yu Yuan, Xiaozhe Dong, Huan Wang, Feng Gai","doi":"10.1063/5.0222349","DOIUrl":"10.1063/5.0222349","url":null,"abstract":"<p><p>Protein/peptide amyloid fibril formation is associated with various neurodegenerative diseases and, hence, has been the subject of extensive studies. From a structure-evolution point of view, we now know a great deal about the initial and final states of this process; however, we know very little about its intermediate states. Herein, we employ liquid-phase transmission electron microscopy to directly visualize the formation of one of the intermediates formed during the aggregation process of an amyloid-forming peptide. As shown in figure, we find that Aβ42, the amyloid formation of which has been linked to the development of Alzheimer's disease, can populate a ring-shaped intermediate structure with a diameter of tens of nanometers; additionally, the air-liquid interface can \"catalyze\" the formation of amyloid fibrils.</p>","PeriodicalId":72405,"journal":{"name":"Biophysics reviews","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11444734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engineered heart tissue: Design considerations and the state of the art.","authors":"Ilhan Gokhan, Thomas S Blum, Stuart G Campbell","doi":"10.1063/5.0202724","DOIUrl":"10.1063/5.0202724","url":null,"abstract":"<p><p>Originally developed more than 20 years ago, engineered heart tissue (EHT) has become an important tool in cardiovascular research for applications such as disease modeling and drug screening. Innovations in biomaterials, stem cell biology, and bioengineering, among other fields, have enabled EHT technologies to recapitulate many aspects of cardiac physiology and pathophysiology. While initial EHT designs were inspired by the isolated-trabecula culture system, current designs encompass a variety of formats, each of which have unique strengths and limitations. In this review, we describe the most common EHT formats, and then systematically evaluate each aspect of their design, emphasizing the rational selection of components for each application.</p>","PeriodicalId":72405,"journal":{"name":"Biophysics reviews","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11192576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141444070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Macrophages on the wrinkle: Exploring microscale interactions with substrate topography.","authors":"Francesca Cecilia Lauta, Luca Pellegrino, Roberto Rusconi","doi":"10.1063/5.0215563","DOIUrl":"10.1063/5.0215563","url":null,"abstract":"<p><p>Macrophages play pivotal roles in the immune response, participating in both inflammatory and pro-healing processes. Like other cells, macrophages continually survey their microenvironment through mechanosensing, adapting their intracellular organization in response to mechanical signals. In this study, we elucidate how macrophages perceive the topographical cues of wrinkled surfaces through actin-based structures, which align with the main pattern direction, thus modulating cell cytoskeletal dynamics. Given that such alterations may regulate mechanosensitive gene expression programs, exploring cellular responses to biomaterial design becomes crucial for developing biomaterials that mitigate adverse reactions.</p>","PeriodicalId":72405,"journal":{"name":"Biophysics reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141312378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How cytoskeletal crosstalk makes cells move: Bridging cell-free and cell studies.","authors":"James P Conboy, Irene Istúriz Petitjean, Anouk van der Net, Gijsje H Koenderink","doi":"10.1063/5.0198119","DOIUrl":"10.1063/5.0198119","url":null,"abstract":"<p><p>Cell migration is a fundamental process for life and is highly dependent on the dynamical and mechanical properties of the cytoskeleton. Intensive physical and biochemical crosstalk among actin, microtubules, and intermediate filaments ensures their coordination to facilitate and enable migration. In this review, we discuss the different mechanical aspects that govern cell migration and provide, for each mechanical aspect, a novel perspective by juxtaposing two complementary approaches to the biophysical study of cytoskeletal crosstalk: live-cell studies (often referred to as top-down studies) and cell-free studies (often referred to as bottom-up studies). We summarize the main findings from both experimental approaches, and we provide our perspective on bridging the two perspectives to address the open questions of how cytoskeletal crosstalk governs cell migration and makes cells move.</p>","PeriodicalId":72405,"journal":{"name":"Biophysics reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11151447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141263438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of matrix stiffness on hepatic cell function, liver fibrosis, and hepatocellular carcinoma—Based on quantitative data","authors":"Kiyoon Min, Sathish Kumar Karuppannan, G. Tae","doi":"10.1063/5.0197875","DOIUrl":"https://doi.org/10.1063/5.0197875","url":null,"abstract":"Over the past few decades, extensive research has explored the development of supportive scaffold materials for in vitro hepatic cell culture, to effectively mimic in vivo microenvironments. It is crucial for hepatic disease modeling, drug screening, and therapeutic evaluations, considering the ethical concerns and practical challenges associated with in vivo experiments. This review offers a comprehensive perspective on hepatic cell culture using bioscaffolds by encompassing all stages of hepatic diseases—from a healthy liver to fibrosis and hepatocellular carcinoma (HCC)—with a specific focus on matrix stiffness. This review begins by providing physiological and functional overviews of the liver. Subsequently, it explores hepatic cellular behaviors dependent on matrix stiffness from previous reports. For hepatic cell activities, softer matrices showed significant advantages over stiffer ones in terms of cell proliferation, migration, and hepatic functions. Conversely, stiffer matrices induced myofibroblastic activation of hepatic stellate cells, contributing to the further progression of fibrosis. Elevated matrix stiffness also correlates with HCC by increasing proliferation, epithelial-mesenchymal transition, metastasis, and drug resistance of HCC cells. In addition, we provide quantitative information on available data to offer valuable perspectives for refining the preparation and development of matrices for hepatic tissue engineering. We also suggest directions for further research on this topic.","PeriodicalId":72405,"journal":{"name":"Biophysics reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141275605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The magnetocardiogram.","authors":"Bradley J Roth","doi":"10.1063/5.0201950","DOIUrl":"10.1063/5.0201950","url":null,"abstract":"<p><p>The magnetic field produced by the heart's electrical activity is called the magnetocardiogram (MCG). The first 20 years of MCG research established most of the concepts, instrumentation, and computational algorithms in the field. Additional insights into fundamental mechanisms of biomagnetism were gained by studying isolated hearts or even isolated pieces of cardiac tissue. Much effort has gone into calculating the MCG using computer models, including solving the inverse problem of deducing the bioelectric sources from biomagnetic measurements. Recently, most magnetocardiographic research has focused on clinical applications, driven in part by new technologies to measure weak biomagnetic fields.</p>","PeriodicalId":72405,"journal":{"name":"Biophysics reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11139488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced computational approaches to understand protein aggregation","authors":"Deepshikha Ghosh, Anushka Biswas, Mithun Radhakrishna","doi":"10.1063/5.0180691","DOIUrl":"https://doi.org/10.1063/5.0180691","url":null,"abstract":"Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.","PeriodicalId":72405,"journal":{"name":"Biophysics reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140659842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease.","authors":"Jose L. Cadavid, Nancy T. Li, A. McGuigan","doi":"10.1063/5.0179125","DOIUrl":"https://doi.org/10.1063/5.0179125","url":null,"abstract":"Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.","PeriodicalId":72405,"journal":{"name":"Biophysics reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140717409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms.","authors":"Amanda Chang, Xiaodong Wu, Kan Liu","doi":"10.1063/5.0176850","DOIUrl":"10.1063/5.0176850","url":null,"abstract":"<p><p>A key strength of echocardiography lies in its integration of comprehensive spatiotemporal cardiac imaging data in real-time, to aid frontline or bedside patient risk stratification and management. Nonetheless, its acquisition, processing, and interpretation are known to all be subject to heterogeneity from its reliance on manual and subjective human tracings, which challenges workflow and protocol standardization and final interpretation accuracy. In the era of advanced computational power, utilization of machine learning algorithms for big data analytics in echocardiography promises reduction in cost, cognitive errors, and intra- and inter-observer variability. Novel spatiotemporal deep learning (DL) models allow the integration of temporal arm information based on unlabeled pixel echocardiographic data for convolution of an adaptive semantic spatiotemporal calibration to construct personalized 4D heart meshes, assess global and regional cardiac function, detect early valve pathology, and differentiate uncommon cardiovascular disorders. Meanwhile, data visualization on spatiotemporal DL prediction models helps extract latent temporal imaging features to develop advanced imaging biomarkers in early disease stages and advance our understanding of pathophysiology to support the development of personalized prevention or treatment strategies. Since portable echocardiograms have been increasingly used as point-of-care imaging tools to aid rural care delivery, the application of these new spatiotemporal DL techniques show the potentials in streamlining echocardiographic acquisition, processing, and data analysis to improve workflow standardization and efficiencies, and provide risk stratification and decision supporting tools in real-time, to prompt the building of new imaging diagnostic networks to enhance rural healthcare engagement.</p>","PeriodicalId":72405,"journal":{"name":"Biophysics reviews","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10978053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140337882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The thermodynamics of neurodegenerative disease.","authors":"Georg Meisl","doi":"10.1063/5.0180899","DOIUrl":"10.1063/5.0180899","url":null,"abstract":"<p><p>The formation of protein aggregates in the brain is a central aspect of the pathology of many neurodegenerative diseases. This self-assembly of specific proteins into filamentous aggregates, or fibrils, is a fundamental biophysical process that can easily be reproduced in the test tube. However, it has been difficult to obtain a clear picture of how the biophysical insights thus obtained can be applied to the complex, multi-factorial diseases and what this means for therapeutic strategies. While new, disease-modifying therapies are now emerging, for the most devastating disorders, such as Alzheimer's and Parkinson's disease, they still fall well short of offering a cure, and few drug design approaches fully exploit the wealth of mechanistic insights that has been obtained in biophysical studies. Here, I attempt to provide a new perspective on the role of protein aggregation in disease, by phrasing the problem in terms of a system that, under constant energy consumption, attempts to maintain a healthy, aggregate-free state against the thermodynamic driving forces that inexorably push it toward pathological aggregation.</p>","PeriodicalId":72405,"journal":{"name":"Biophysics reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10957229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140208304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}