Pub Date : 2024-08-05eCollection Date: 2024-08-01DOI: 10.1371/journal.pcbi.1011831
Robby Concha-Eloko, Michiel Stock, Bernard De Baets, Yves Briers, Rafael Sanjuán, Pilar Domingo-Calap, Dimitri Boeckaerts
Bacteriophages (phages) are viruses that infect bacteria. Many of them produce specific enzymes called depolymerases to break down external polysaccharide structures. Accurate annotation and domain identification of these depolymerases are challenging due to their inherent sequence diversity. Hence, we present DepoScope, a machine learning tool that combines a fine-tuned ESM-2 model with a convolutional neural network to identify depolymerase sequences and their enzymatic domains precisely. To accomplish this, we curated a dataset from the INPHARED phage genome database, created a polysaccharide-degrading domain database, and applied sequential filters to construct a high-quality dataset, which is subsequently used to train DepoScope. Our work is the first approach that combines sequence-level predictions with amino-acid-level predictions for accurate depolymerase detection and functional domain identification. In that way, we believe that DepoScope can greatly enhance our understanding of phage-host interactions at the level of depolymerases.
{"title":"DepoScope: Accurate phage depolymerase annotation and domain delineation using large language models.","authors":"Robby Concha-Eloko, Michiel Stock, Bernard De Baets, Yves Briers, Rafael Sanjuán, Pilar Domingo-Calap, Dimitri Boeckaerts","doi":"10.1371/journal.pcbi.1011831","DOIUrl":"10.1371/journal.pcbi.1011831","url":null,"abstract":"<p><p>Bacteriophages (phages) are viruses that infect bacteria. Many of them produce specific enzymes called depolymerases to break down external polysaccharide structures. Accurate annotation and domain identification of these depolymerases are challenging due to their inherent sequence diversity. Hence, we present DepoScope, a machine learning tool that combines a fine-tuned ESM-2 model with a convolutional neural network to identify depolymerase sequences and their enzymatic domains precisely. To accomplish this, we curated a dataset from the INPHARED phage genome database, created a polysaccharide-degrading domain database, and applied sequential filters to construct a high-quality dataset, which is subsequently used to train DepoScope. Our work is the first approach that combines sequence-level predictions with amino-acid-level predictions for accurate depolymerase detection and functional domain identification. In that way, we believe that DepoScope can greatly enhance our understanding of phage-host interactions at the level of depolymerases.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05eCollection Date: 2024-08-01DOI: 10.1371/journal.pcbi.1012167
Alex T Keeley, Jeffrey M Lotthammer, Jacqueline F Pelham
Circadian rhythms are ubiquitous across the kingdoms of life and serve important roles in regulating physiology and behavior at many levels. These rhythms occur in ~24-hour cycles and are driven by a core molecular oscillator. Circadian timekeeping enables organisms to anticipate daily changes by timing their growth and internal processes. Neurospora crassa is a model organism with a long history in circadian biology, having conserved eukaryotic clock properties and observable circadian phenotypes. A core approach for measuring circadian function in Neurospora is to follow daily oscillations in the direction of growth and spore formation along a thin glass tube (race tube). While leveraging robust phenotypic readouts is useful, interpreting the outputs of large-scale race tube experiments by hand can be time-consuming and prone to human error. To provide the field with an efficient tool for analyzing race tubes, we present Rhythmidia, a graphical user interface (GUI) tool written in Python for calculating circadian periods and growth rates of Neurospora. Rhythmidia is open source, has been benchmarked against the current state-of-the-art, and is easily accessible on GitHub.
{"title":"Rhythmidia: A modern tool for circadian period analysis of filamentous fungi.","authors":"Alex T Keeley, Jeffrey M Lotthammer, Jacqueline F Pelham","doi":"10.1371/journal.pcbi.1012167","DOIUrl":"10.1371/journal.pcbi.1012167","url":null,"abstract":"<p><p>Circadian rhythms are ubiquitous across the kingdoms of life and serve important roles in regulating physiology and behavior at many levels. These rhythms occur in ~24-hour cycles and are driven by a core molecular oscillator. Circadian timekeeping enables organisms to anticipate daily changes by timing their growth and internal processes. Neurospora crassa is a model organism with a long history in circadian biology, having conserved eukaryotic clock properties and observable circadian phenotypes. A core approach for measuring circadian function in Neurospora is to follow daily oscillations in the direction of growth and spore formation along a thin glass tube (race tube). While leveraging robust phenotypic readouts is useful, interpreting the outputs of large-scale race tube experiments by hand can be time-consuming and prone to human error. To provide the field with an efficient tool for analyzing race tubes, we present Rhythmidia, a graphical user interface (GUI) tool written in Python for calculating circadian periods and growth rates of Neurospora. Rhythmidia is open source, has been benchmarked against the current state-of-the-art, and is easily accessible on GitHub.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05eCollection Date: 2024-08-01DOI: 10.1371/journal.pcbi.1012280
Rune Höper, Daria Komkova, Tomáš Zavřel, Ralf Steuer
The metabolism of phototrophic cyanobacteria is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2 into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803 and its quantitative evaluation using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of experimental analyses, we develop a novel approach to describe light absorption and light utilization within the framework of FBA. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable of predicting quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototrophic microorganisms.
{"title":"A quantitative description of light-limited cyanobacterial growth using flux balance analysis.","authors":"Rune Höper, Daria Komkova, Tomáš Zavřel, Ralf Steuer","doi":"10.1371/journal.pcbi.1012280","DOIUrl":"10.1371/journal.pcbi.1012280","url":null,"abstract":"<p><p>The metabolism of phototrophic cyanobacteria is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2 into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803 and its quantitative evaluation using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of experimental analyses, we develop a novel approach to describe light absorption and light utilization within the framework of FBA. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable of predicting quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototrophic microorganisms.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cells exhibit various morphological characteristics due to their physiological activities, and changes in cell morphology are inherently accompanied by the assembly and disassembly of the actin cytoskeleton. Stress fibers are a prominent component of the actin-based intracellular structure and are highly involved in numerous physiological processes, e.g., mechanotransduction and maintenance of cell morphology. Although it is widely accepted that variations in cell morphology interact with the distribution and localization of stress fibers, it remains unclear if there are underlying geometric principles between the cell morphology and actin cytoskeleton. Here, we present a machine learning system that uses the diffusion model to convert the cell shape to the distribution and alignment of stress fibers. By training with corresponding cell shape and stress fibers datasets, our system learns the conversion to generate the stress fiber images from its corresponding cell shape. The predicted stress fiber distribution agrees well with the experimental data. With this conversion relation, our system allows for performing virtual experiments that provide a visual map showing the probability of stress fiber distribution from the virtual cell shape. Our system potentially provides a powerful approach to seek further hidden geometric principles regarding how the configuration of subcellular structures is determined by the boundary of the cell structure; for example, we found that the stress fibers of cells with small aspect ratios tend to localize at the cell edge while cells with large aspect ratios have homogenous distributions.
{"title":"Diffusion model predicts the geometry of actin cytoskeleton from cell morphology.","authors":"Honghan Li, Shiyou Liu, Shinji Deguchi, Daiki Matsunaga","doi":"10.1371/journal.pcbi.1012312","DOIUrl":"10.1371/journal.pcbi.1012312","url":null,"abstract":"<p><p>Cells exhibit various morphological characteristics due to their physiological activities, and changes in cell morphology are inherently accompanied by the assembly and disassembly of the actin cytoskeleton. Stress fibers are a prominent component of the actin-based intracellular structure and are highly involved in numerous physiological processes, e.g., mechanotransduction and maintenance of cell morphology. Although it is widely accepted that variations in cell morphology interact with the distribution and localization of stress fibers, it remains unclear if there are underlying geometric principles between the cell morphology and actin cytoskeleton. Here, we present a machine learning system that uses the diffusion model to convert the cell shape to the distribution and alignment of stress fibers. By training with corresponding cell shape and stress fibers datasets, our system learns the conversion to generate the stress fiber images from its corresponding cell shape. The predicted stress fiber distribution agrees well with the experimental data. With this conversion relation, our system allows for performing virtual experiments that provide a visual map showing the probability of stress fiber distribution from the virtual cell shape. Our system potentially provides a powerful approach to seek further hidden geometric principles regarding how the configuration of subcellular structures is determined by the boundary of the cell structure; for example, we found that the stress fibers of cells with small aspect ratios tend to localize at the cell edge while cells with large aspect ratios have homogenous distributions.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05eCollection Date: 2024-08-01DOI: 10.1371/journal.pcbi.1012337
Anton Suvorov, Daniel R Schrider
A phylogenetic tree represents hypothesized evolutionary history for a set of taxa. Besides the branching patterns (i.e., tree topology), phylogenies contain information about the evolutionary distances (i.e. branch lengths) between all taxa in the tree, which include extant taxa (external nodes) and their last common ancestors (internal nodes). During phylogenetic tree inference, the branch lengths are typically co-estimated along with other phylogenetic parameters during tree topology space exploration. There are well-known regions of the branch length parameter space where accurate estimation of phylogenetic trees is especially difficult. Several novel studies have recently demonstrated that machine learning approaches have the potential to help solve phylogenetic problems with greater accuracy and computational efficiency. In this study, as a proof of concept, we sought to explore the possibility of machine learning models to predict branch lengths. To that end, we designed several deep learning frameworks to estimate branch lengths on fixed tree topologies from multiple sequence alignments or its representations. Our results show that deep learning methods can exhibit superior performance in some difficult regions of branch length parameter space. For example, in contrast to maximum likelihood inference, which is typically used for estimating branch lengths, deep learning methods are more efficient and accurate. In general, we find that our neural networks achieve similar accuracy to a Bayesian approach and are the best-performing methods when inferring long branches that are associated with distantly related taxa. Together, our findings represent a next step toward accurate, fast, and reliable phylogenetic inference with machine learning approaches.
{"title":"Reliable estimation of tree branch lengths using deep neural networks.","authors":"Anton Suvorov, Daniel R Schrider","doi":"10.1371/journal.pcbi.1012337","DOIUrl":"10.1371/journal.pcbi.1012337","url":null,"abstract":"<p><p>A phylogenetic tree represents hypothesized evolutionary history for a set of taxa. Besides the branching patterns (i.e., tree topology), phylogenies contain information about the evolutionary distances (i.e. branch lengths) between all taxa in the tree, which include extant taxa (external nodes) and their last common ancestors (internal nodes). During phylogenetic tree inference, the branch lengths are typically co-estimated along with other phylogenetic parameters during tree topology space exploration. There are well-known regions of the branch length parameter space where accurate estimation of phylogenetic trees is especially difficult. Several novel studies have recently demonstrated that machine learning approaches have the potential to help solve phylogenetic problems with greater accuracy and computational efficiency. In this study, as a proof of concept, we sought to explore the possibility of machine learning models to predict branch lengths. To that end, we designed several deep learning frameworks to estimate branch lengths on fixed tree topologies from multiple sequence alignments or its representations. Our results show that deep learning methods can exhibit superior performance in some difficult regions of branch length parameter space. For example, in contrast to maximum likelihood inference, which is typically used for estimating branch lengths, deep learning methods are more efficient and accurate. In general, we find that our neural networks achieve similar accuracy to a Bayesian approach and are the best-performing methods when inferring long branches that are associated with distantly related taxa. Together, our findings represent a next step toward accurate, fast, and reliable phylogenetic inference with machine learning approaches.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05eCollection Date: 2024-08-01DOI: 10.1371/journal.pcbi.1012338
Michael B Sohn, Cynthia Monaco, Steven R Gill
In many omics data, including microbiome sequencing data, we are only able to measure relative information. Various computational or statistical methods have been proposed to extract absolute (or biologically relevant) information from this relative information; however, these methods are under rather strong assumptions that may not be suitable for multigroup (more than two groups) and/or longitudinal outcome data. In this article, we first introduce the minimal assumption required to extract absolute from relative information. This assumption is less stringent than those imposed in existing methods, thus being applicable to multigroup and/or longitudinal outcome data. We then propose the first normalization method that works under this minimal assumption. The optimality and validity of the proposed method and its beneficial effects on downstream analysis are demonstrated in extensive simulation studies, where existing methods fail to produce consistent performance under the minimal assumption. We also demonstrate its application to real microbiome datasets to determine biologically relevant microbes to a specific disease/condition.
{"title":"An optimal normalization method for high sparse compositional microbiome data.","authors":"Michael B Sohn, Cynthia Monaco, Steven R Gill","doi":"10.1371/journal.pcbi.1012338","DOIUrl":"10.1371/journal.pcbi.1012338","url":null,"abstract":"<p><p>In many omics data, including microbiome sequencing data, we are only able to measure relative information. Various computational or statistical methods have been proposed to extract absolute (or biologically relevant) information from this relative information; however, these methods are under rather strong assumptions that may not be suitable for multigroup (more than two groups) and/or longitudinal outcome data. In this article, we first introduce the minimal assumption required to extract absolute from relative information. This assumption is less stringent than those imposed in existing methods, thus being applicable to multigroup and/or longitudinal outcome data. We then propose the first normalization method that works under this minimal assumption. The optimality and validity of the proposed method and its beneficial effects on downstream analysis are demonstrated in extensive simulation studies, where existing methods fail to produce consistent performance under the minimal assumption. We also demonstrate its application to real microbiome datasets to determine biologically relevant microbes to a specific disease/condition.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05eCollection Date: 2024-08-01DOI: 10.1371/journal.pcbi.1011431
Priscilla E Greenwood, Lawrence M Ward
Synchronous neural oscillations are strongly associated with a variety of perceptual, cognitive, and behavioural processes. It has been proposed that the role of the synchronous oscillations in these processes is to facilitate information transmission between brain areas, the 'communication through coherence,' or CTC hypothesis. The details of how this mechanism would work, however, and its causal status, are still unclear. Here we investigate computationally a proposed mechanism for selective attention that directly implicates the CTC as causal. The mechanism involves alpha band (about 10 Hz) oscillations, originating in the pulvinar nucleus of the thalamus, being sent to communicating cortical areas, organizing gamma (about 40 Hz) oscillations there, and thus facilitating phase coherence and communication between them. This is proposed to happen contingent on control signals sent from higher-level cortical areas to the thalamic reticular nucleus, which controls the alpha oscillations sent to cortex by the pulvinar. We studied the scope of this mechanism in parameter space, and limitations implied by this scope, using a computational implementation of our conceptual model. Our results indicate that, although the CTC-based mechanism can account for some effects of top-down and bottom-up attentional selection, its limitations indicate that an alternative mechanism, in which oscillatory coherence is caused by communication between brain areas rather than being a causal factor for it, might operate in addition to, or even instead of, the CTC mechanism.
{"title":"Attentional selection and communication through coherence: Scope and limitations.","authors":"Priscilla E Greenwood, Lawrence M Ward","doi":"10.1371/journal.pcbi.1011431","DOIUrl":"10.1371/journal.pcbi.1011431","url":null,"abstract":"<p><p>Synchronous neural oscillations are strongly associated with a variety of perceptual, cognitive, and behavioural processes. It has been proposed that the role of the synchronous oscillations in these processes is to facilitate information transmission between brain areas, the 'communication through coherence,' or CTC hypothesis. The details of how this mechanism would work, however, and its causal status, are still unclear. Here we investigate computationally a proposed mechanism for selective attention that directly implicates the CTC as causal. The mechanism involves alpha band (about 10 Hz) oscillations, originating in the pulvinar nucleus of the thalamus, being sent to communicating cortical areas, organizing gamma (about 40 Hz) oscillations there, and thus facilitating phase coherence and communication between them. This is proposed to happen contingent on control signals sent from higher-level cortical areas to the thalamic reticular nucleus, which controls the alpha oscillations sent to cortex by the pulvinar. We studied the scope of this mechanism in parameter space, and limitations implied by this scope, using a computational implementation of our conceptual model. Our results indicate that, although the CTC-based mechanism can account for some effects of top-down and bottom-up attentional selection, its limitations indicate that an alternative mechanism, in which oscillatory coherence is caused by communication between brain areas rather than being a causal factor for it, might operate in addition to, or even instead of, the CTC mechanism.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05eCollection Date: 2024-08-01DOI: 10.1371/journal.pcbi.1012321
Andrew A Schmidt, Alexander Y Grosberg, Anna Grosberg
Understanding muscle contraction mechanisms is a standing challenge, and one of the approaches has been to create models of the sarcomere-the basic contractile unit of striated muscle. While these models have been successful in elucidating many aspects of muscle contraction, they fall short in explaining the energetics of functional phenomena, such as rigor, and in particular, their dependence on the concentrations of the biomolecules involved in the cross-bridge cycle. Our hypothesis posits that the stochastic time delay between ATP adsorption and ADP/Pi release in the cross-bridge cycle necessitates a modeling approach where the rates of these two reaction steps are controlled by two independent parts of the total free energy change of the hydrolysis reaction. To test this hypothesis, we built a two-filament, stochastic-mechanical half-sarcomere model that separates the energetic roles of ATP and ADP/Pi in the cross-bridge cycle's free energy landscape. Our results clearly demonstrate that there is a nontrivial dependence of the cross-bridge cycle's kinetics on the independent concentrations of ATP, ADP, and Pi. The simplicity of the proposed model allows for analytical solutions of the more basic systems, which provide novel insight into the dominant mechanisms driving some of the experimentally observed contractile phenomena.
了解肌肉收缩机制是一项长期挑战,其中一种方法是创建肌节模型--横纹肌的基本收缩单元。虽然这些模型成功地阐明了肌肉收缩的许多方面,但却无法解释功能现象(如僵直)的能量学,特别是它们对参与横桥循环的生物大分子浓度的依赖性。我们的假设认为,横桥循环中 ATP 吸附和 ADP/Pi 释放之间的随机时间延迟要求采用一种建模方法,即这两个反应步骤的速率由水解反应总自由能变化的两个独立部分控制。为了验证这一假设,我们建立了一个双丝随机机械半节模型,将 ATP 和 ADP/Pi 在横桥循环自由能图谱中的能量作用分开。我们的研究结果清楚地表明,横桥循环的动力学与 ATP、ADP 和 Pi 的独立浓度存在着不小的关系。所提出模型的简易性允许对更基本的系统进行分析求解,这为我们深入了解驱动实验观察到的某些收缩现象的主导机制提供了新的视角。
{"title":"A novel kinetic model to demonstrate the independent effects of ATP and ADP/Pi concentrations on sarcomere function.","authors":"Andrew A Schmidt, Alexander Y Grosberg, Anna Grosberg","doi":"10.1371/journal.pcbi.1012321","DOIUrl":"10.1371/journal.pcbi.1012321","url":null,"abstract":"<p><p>Understanding muscle contraction mechanisms is a standing challenge, and one of the approaches has been to create models of the sarcomere-the basic contractile unit of striated muscle. While these models have been successful in elucidating many aspects of muscle contraction, they fall short in explaining the energetics of functional phenomena, such as rigor, and in particular, their dependence on the concentrations of the biomolecules involved in the cross-bridge cycle. Our hypothesis posits that the stochastic time delay between ATP adsorption and ADP/Pi release in the cross-bridge cycle necessitates a modeling approach where the rates of these two reaction steps are controlled by two independent parts of the total free energy change of the hydrolysis reaction. To test this hypothesis, we built a two-filament, stochastic-mechanical half-sarcomere model that separates the energetic roles of ATP and ADP/Pi in the cross-bridge cycle's free energy landscape. Our results clearly demonstrate that there is a nontrivial dependence of the cross-bridge cycle's kinetics on the independent concentrations of ATP, ADP, and Pi. The simplicity of the proposed model allows for analytical solutions of the more basic systems, which provide novel insight into the dominant mechanisms driving some of the experimentally observed contractile phenomena.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-02eCollection Date: 2024-08-01DOI: 10.1371/journal.pcbi.1011854
Fengdi Zhao, Xin Ma, Bing Yao, Qing Lu, Li Chen
Single-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility (DA) analysis. However, the data characteristics of scATAC-seq such as excessive zeros and large variability of chromatin accessibility across cells impose a unique challenge for DA analysis. Existing statistical methods focus on detecting the mean difference of the chromatin accessible regions while overlooking the distribution difference. Motivated by real data exploration that distribution difference exists among cell types, we introduce a novel composite statistical test named "scaDA", which is based on zero-inflated negative binomial model (ZINB), for performing differential distribution analysis of chromatin accessibility by jointly testing the abundance, prevalence and dispersion simultaneously. Benefiting from both dispersion shrinkage and iterative refinement of mean and prevalence parameter estimates, scaDA demonstrates its superiority to both ZINB-based likelihood ratio tests and published methods by achieving the highest power and best FDR control in a comprehensive simulation study. In addition to demonstrating the highest power in three real sc-multiome data analyses, scaDA successfully identifies differentially accessible regions in microglia from sc-multiome data for an Alzheimer's disease (AD) study that are most enriched in GO terms related to neurogenesis and the clinical phenotype of AD, and AD-associated GWAS SNPs.
单细胞 ATAC-seq 测序数据(scATAC-seq)已被广泛用于研究单细胞水平的染色质可及性。scATAC-seq数据分析的一个重要应用是差异染色质可及性(DA)分析。然而,scATAC-seq 的数据特征(如过多的零和细胞间染色质可及性的巨大变异性)给 DA 分析带来了独特的挑战。现有的统计方法侧重于检测染色质可及区域的平均差异,而忽略了分布差异。基于对细胞类型间存在分布差异的实际数据的探索,我们引入了一种名为 "scaDA "的新型复合统计检验,它基于零膨胀负二项模型(ZINB),通过同时检测丰度、流行度和离散度来进行染色质可及性的差异分布分析。得益于离散度缩小和平均值与流行度参数估计的迭代改进,ScaDA 在一项综合模拟研究中取得了最高的功率和最佳的 FDR 控制,证明了它优于基于 ZINB 的似然比检验和已发表的方法。除了在三项真实 sc-multiome 数据分析中显示出最高的功率之外,scaDA 还成功地从一项阿尔茨海默病(AD)研究的 sc-multiome 数据中识别出了小胶质细胞中的不同可访问区域,这些区域在与神经发生和 AD 临床表型相关的 GO 术语以及与 AD 相关的 GWAS SNP 中最为富集。
{"title":"scaDA: A novel statistical method for differential analysis of single-cell chromatin accessibility sequencing data.","authors":"Fengdi Zhao, Xin Ma, Bing Yao, Qing Lu, Li Chen","doi":"10.1371/journal.pcbi.1011854","DOIUrl":"10.1371/journal.pcbi.1011854","url":null,"abstract":"<p><p>Single-cell ATAC-seq sequencing data (scATAC-seq) has been widely used to investigate chromatin accessibility on the single-cell level. One important application of scATAC-seq data analysis is differential chromatin accessibility (DA) analysis. However, the data characteristics of scATAC-seq such as excessive zeros and large variability of chromatin accessibility across cells impose a unique challenge for DA analysis. Existing statistical methods focus on detecting the mean difference of the chromatin accessible regions while overlooking the distribution difference. Motivated by real data exploration that distribution difference exists among cell types, we introduce a novel composite statistical test named \"scaDA\", which is based on zero-inflated negative binomial model (ZINB), for performing differential distribution analysis of chromatin accessibility by jointly testing the abundance, prevalence and dispersion simultaneously. Benefiting from both dispersion shrinkage and iterative refinement of mean and prevalence parameter estimates, scaDA demonstrates its superiority to both ZINB-based likelihood ratio tests and published methods by achieving the highest power and best FDR control in a comprehensive simulation study. In addition to demonstrating the highest power in three real sc-multiome data analyses, scaDA successfully identifies differentially accessible regions in microglia from sc-multiome data for an Alzheimer's disease (AD) study that are most enriched in GO terms related to neurogenesis and the clinical phenotype of AD, and AD-associated GWAS SNPs.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11324137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141879305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-02eCollection Date: 2024-08-01DOI: 10.1371/journal.pcbi.1012297
Ye Chen, Peter Beech, Ziwei Yin, Shanshan Jia, Jiayi Zhang, Zhaofei Yu, Jian K Liu
Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a crucial investigation in neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding-Neuropixels dataset and utilize the capabilities of deep learning neural network models to study neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. Our study reveals that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within the visual cortex and subcortical nuclei, in contrast to a relatively reduced encoding activity within hippocampal neurons. Strikingly, our results unveil a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings corroborate existing knowledge in visual coding related to artificial visual stimuli and illuminate the functional role of these deeper brain regions using dynamic stimuli. Consequently, our results suggest a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding quality of dynamic natural visual scenes represented by neural responses, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.
{"title":"Decoding dynamic visual scenes across the brain hierarchy.","authors":"Ye Chen, Peter Beech, Ziwei Yin, Shanshan Jia, Jiayi Zhang, Zhaofei Yu, Jian K Liu","doi":"10.1371/journal.pcbi.1012297","DOIUrl":"10.1371/journal.pcbi.1012297","url":null,"abstract":"<p><p>Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a crucial investigation in neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding-Neuropixels dataset and utilize the capabilities of deep learning neural network models to study neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. Our study reveals that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within the visual cortex and subcortical nuclei, in contrast to a relatively reduced encoding activity within hippocampal neurons. Strikingly, our results unveil a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings corroborate existing knowledge in visual coding related to artificial visual stimuli and illuminate the functional role of these deeper brain regions using dynamic stimuli. Consequently, our results suggest a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding quality of dynamic natural visual scenes represented by neural responses, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11324145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141879303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}