PLoS Computational Biology最新文献

{"title":"Emergence of supercoiling-mediated regulatory networks through the evolution of bacterial chromosome organization.","authors":"Théotime Grohens, Sam Meyer, Guillaume Beslon","doi":"10.1371/journal.pcbi.1013482","DOIUrl":"10.1371/journal.pcbi.1013482","url":null,"abstract":"<p><p>DNA supercoiling-the level of twisting and writhing of the DNA molecule around itself-plays an important role in the regulation of gene expression in bacteria by modulating promoter activity. The level of DNA supercoiling is a dynamic property of the chromosome which varies both at local and global scales, in response to both external factors such as environmental perturbations and internal factors including gene transcription. As such, local variations in supercoiling could in theory couple the expression levels of neighboring genes by creating feedback loops at the transcriptional level. However, the impact of such supercoiling-mediated interactions on the regulation of gene expression still remains uncertain. In this work, we study how this coupling between transcription and supercoiling could shape genome organization and help regulate gene transcription. We present a model of genome evolution in which individuals whose gene transcription rates are coupled to local supercoiling must adapt to two environments that induce different global supercoiling levels. In this model, we observe the evolution of whole-genome regulatory networks that provide control over gene expression by leveraging the transcription-supercoiling coupling, and show that the structure of these networks is underpinned by the organization of genes along the chromosome at several scales. Local variations in DNA supercoiling could therefore help jointly shape both gene regulation and genome organization during evolution.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013482"},"PeriodicalIF":3.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12503335/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145192550","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}

{"title":"Dual process impairments in reinforcement learning and working memory systems underlie learning deficits in physiological anxiety.","authors":"Jennifer D Senta, Sonia J Bishop, Anne G E Collins","doi":"10.1371/journal.pcbi.1012872","DOIUrl":"10.1371/journal.pcbi.1012872","url":null,"abstract":"<p><p>Anxiety has been robustly linked to deficits in frontal executive function including working memory (WM) and attentional control processes. However, although anxiety has also been associated with impaired performance on learning tasks, computational investigations of reinforcement learning (RL) impairment in anxiety have yielded mixed results. WM processes are known to contribute to learning behavior in parallel to RL processes and to modulate the effective learning rate as a function of load. However, WM processes have typically not been modeled in investigations of anxiety and RL. In the current study, we leveraged an experimental paradigm (RLWM) which manipulates the relative contributions of WM and RL processes in a reinforcement learning and retention task using multiple stimulus set sizes. Using a computational model of interactive RL and WM processes, we investigated whether individual differences in physiological or cognitive anxiety impacted task performance via deficits in RL or WM. Elevated physiological, but not cognitive, anxiety scores were strongly associated with worse performance during learning and retention testing across all set sizes. Computationally, higher physiological anxiety scores were significantly related to reduced learning rate and increased rate of WM decay. To highlight the importance of modeling WM contributions to learning, we considered the effect of fitting RL models without WM modules to the data. Here we found that reduced learning performance for higher physiological anxiety was at least partially misattributed to stochastic decision noise in 9 out of 10 RL-only models considered. These findings reveal a dual-process impairment in learning in anxiety that is linked to a more physiological than cognitive anxiety phenotype. More broadly, this work also points to the importance of accounting for the contribution of WM to RL when investigating psychopathology-related deficits in learning.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1012872"},"PeriodicalIF":3.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177867","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}

{"title":"Correction: Design of field trials for the evaluation of transmissible vaccines in animal populations.","authors":"","doi":"10.1371/journal.pcbi.1013530","DOIUrl":"10.1371/journal.pcbi.1013530","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1371/journal.pcbi.1012779.].</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013530"},"PeriodicalIF":3.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177829","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}

{"title":"An explainable covariate compartmental model for predicting the spatio-temporal patterns of dengue in Sri Lanka.","authors":"Yichao Liu, Peter Fransson, Julian Heidecke, Prasad Liyanage, Jonas Wallin, Joacim Rocklöv","doi":"10.1371/journal.pcbi.1013540","DOIUrl":"10.1371/journal.pcbi.1013540","url":null,"abstract":"<p><p>A majority of all infectious diseases manifest some climate-sensitivity. However, many of those sensitivities are not well understood as meteorological drivers of infectious diseases co-occur with other drivers exhibiting complex non-linear influences and feedback. This makes it hard to dissect their individual contributions. Here we apply a novel deep learning Explainable AI (XAI) compartment model with covariate drivers and dynamic feedback to predict and explain the dengue incidence across Sri Lanka. We compare the compartmental Susceptible-Exposed-Infected-Recovered (SEIR) model to a deep learning model without a compartmental structure. We find that the covariate compartmental hybrid model performs better and can describe drivers of the dengue spatiotemporal incidence over time. The strongest drivers in our model in order of importance are precipitation, socio-demographics, and normalized vegetation index. The novel method demonstrated can be used to leverage known infectious disease dynamics while accounting for the influence of other drivers and different population immunity contexts. While allowing for interpretation of the covariate driver influences, the approach bridges the gap between dynamical compartmental and data driven dynamical models.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013540"},"PeriodicalIF":3.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177789","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}

{"title":"MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions.","authors":"Nicola F Müller, Remco R Bouckaert, Chieh-Hsi Wu, Trevor Bedford","doi":"10.1371/journal.pcbi.1013421","DOIUrl":"10.1371/journal.pcbi.1013421","url":null,"abstract":"<p><p>The spread of infectious diseases is shaped by spatial and temporal aspects, such as host population structure or changes in the transmission rate or number of infected individuals over time. These spatiotemporal dynamics are imprinted in the genomes of pathogens and can be recovered from those genomes using phylodynamics methods. However, phylodynamic methods typically quantify either the temporal or spatial transmission dynamics, which leads to unclear biases, as one can potentially not be inferred without the other. Here, we address this challenge by introducing a structured coalescent skyline approach, MASCOT-Skyline, that allows us to jointly infer spatial and temporal transmission dynamics of infectious diseases using Markov chain Monte Carlo inference. To do so, we model the effective population size dynamics in different locations using a non-parametric function, allowing us to approximate a range of population size dynamics. We show, using a range of different viral outbreak datasets, potential issues with phylogeographic methods. We then use these viral datasets to motivate simulations of outbreaks that illuminate the nature of biases present in the different phylogeographic methods. We show that spatial and temporal dynamics should be modeled jointly, even if one seeks to recover just one of the two. Further, we showcase conditions under which we can expect phylogeographic analyses to be biased, particularly different subsampling approaches, as well as provide recommendations on when we can expect them to perform well. We implemented MASCOT-Skyline as part of the open-source software package MASCOT for the Bayesian phylodynamics platform BEAST2.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013421"},"PeriodicalIF":3.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500135/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177847","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}

{"title":"Exploration of potential novel drug targets for rheumatoid arthritis by plasma proteome screening.","authors":"Zhiqiang Ma, Ran Chen, Zibo Feng","doi":"10.1371/journal.pcbi.1013333","DOIUrl":"10.1371/journal.pcbi.1013333","url":null,"abstract":"<p><strong>Background: </strong>Circulating proteins play a critical role in rheumatoid arthritis (RA), yet few have been targeted therapeutically. This study aimed to identify novel protein targets for RA therapy.</p><p><strong>Methods: </strong>We conducted a comprehensive proteome-wide Mendelian Randomization (MR), colocalization analysis, and summary-data-based MR (SMR) to explore potential causal relationships between plasma proteins and RA, with an overall sample size of 1,148,608. The GWAS data on plasma proteins were obtained from the FinnGen study, the UK Biobank Pharma Proteomics Project and Iceland GWAS data. Then, validation of key molecules' differential expression pattern was done using external transcriptomic data from RA patients, while the Drug Signatures Database (DsigDB) was used to identify potential therapeutic drugs. Drugs and target proteins interactions was evaluated with molecular docking and molecular dynamics simulations approaches. Potential side effects of plasma proteins associated with RA were elucidated by phenome-wide association study (Phe-WAS) approach.</p><p><strong>Results: </strong>Genetically predicted levels of 68 plasma proteins were associated with RA. After colocalization and SMR analysis, 6 plasma proteins (FCRL3, SUGP1, TNFAIP3, EHBP1, HAPLN4, and CILP2) have been passed all tests and identified as having potential as therapeutic targets for RA. Further Receiver operating characteristic curve (ROC) analysis indicated that three protiens (CILP2, TNFAIP3 and EHBP) have a good potential as biomarkers for RA. Differential gene analysis showed the downregulation of HAPLN4, FCRL3, EHBP1 and TNFAIP3 in RA, as well as the upregulation of CILP2 in RA. Further Phe-WAS suggested that targeting these proteins may have potential side effects.</p><p><strong>Conclusion: </strong>Our study investigated the causal relationships between plasma proteins and RA, deepening our understanding of the molecular mechanisms and facilitating the development of new therapeutic drugs.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013333"},"PeriodicalIF":3.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12463240/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145150504","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}

{"title":"A comparative computational analysis of IFN-alpha pharmacokinetics and its induced cellular response in mice and humans.","authors":"Priyata Kalra, Bastian Kister, Rebekka Fendt, Mario Köster, Julia Pulverer, Sven Sahle, Lars Kuepfer, Ursula Kummer","doi":"10.1371/journal.pcbi.1013509","DOIUrl":"10.1371/journal.pcbi.1013509","url":null,"abstract":"<p><p>Drug effects are difficult to investigate in detail in vivo. However, a mechanistic understanding of drug action is clearly beneficial for both pharmaceutical development as well as for optimization of treatment designs. We here established a quantitative systems pharmacology (QSP) mouse model which simultaneously describes whole-body pharmacokinetics of murine IFN-α as well as the cellular pharmacodynamic effect through the antiviral response biomarker Mx2. To this end, a dynamic model of intracellular IFN-α signalling in the JAK/STAT pathway was combined with a whole-body physiologically-based pharmacokinetic model of IFN-α in mice. The pharmacodynamic behaviour of the resulting mouse IFN-α QSP model was first compared to a cellular model of the JAK/STAT pathway to compare in vitro and in vivo drug effects and to identify functional differences. It was found that the in vitro drug effect in the cellular model overestimates the in vivo response in mice at least by a factor of two which is partly due to the missing drug clearance in vitro. Also, the drug responses in the in vitro model were time delayed. Interspecies analyses in murine and a previously published human QSP model of IFN-α next show a similar dynamic behavior. However, our models demonstrate eight to 16-fold stronger response levels in mice than in humans due to more efficient interferon binding. Our analysis supports a mechanistic analysis of both upstream pharmacokinetic as well as downstream pharmacodynamic drug effects through the combination of physiological knowledge and quantitative computational models. The study hence shows potential applications for QSP modelling in terms of study planning, for example by choosing physiologically relevant in vitro concentrations. Also, the QSP model allows inter-species comparisons of the effect strength in specific functional readouts, which in humans are otherwise not possible due to the limited sampling possibilities. We expect QSP modelling to play an increasingly important role in drug development and research in the future.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013509"},"PeriodicalIF":3.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500084/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145150486","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}

{"title":"Mathematical modeling of dopamine rhythms and timing of dopamine reuptake inhibitors.","authors":"Tianyong Yao, Ruby Kim","doi":"10.1371/journal.pcbi.1013508","DOIUrl":"10.1371/journal.pcbi.1013508","url":null,"abstract":"<p><p>Dopamine (DA) plays a vital role in mood, alertness, and behavior, with dysregulation linked to disorders such as Parkinson's disease, ADHD, depression, and addiction. In this study, we develop and analyze a reduced mathematical model of dopamine synthesis, release, and reuptake to investigate how daily rhythms influence dopamine dynamics and the efficacy of dopamine reuptake inhibitors (DRIs) used in the treatment of various neuropsychiatric conditions. We simplify a detailed mathematical model of dopamine synthesis, release, and reuptake and demonstrate that our reduced system maintains key dynamical features including homeostatic regulation via autoreceptors. Our model captures core autoregulatory mechanisms and reveals that DRIs can exert substantial time-of-day effects, allowing for dopamine levels to be sustained at elevated levels when administered at circadian troughs. These fluctuations depend sensitively on the timing of DRI administration relative to circadian variations in enzyme activity. We further extend the model to incorporate feedback from local dopaminergic tone, which generates ultradian oscillations in the model independent of circadian regulation. Administration of DRIs lengthens the ultradian periodicity. Our findings provide strong evidence that intrinsic fluctuations in DA should be considered in the clinical use of DRIs, offering a mechanistic framework for improving chronotherapeutic strategies targeting dopaminergic dysfunction.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013508"},"PeriodicalIF":3.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12494296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145150507","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}

{"title":"Elucidating reaction dynamics in a model of human brain energy metabolism.","authors":"Dimitrios G Patsatzis, Efstathios-Al Tingas, Subram Mani Sarathy, Dimitris A Goussis, Renaud Blaise Jolivet","doi":"10.1371/journal.pcbi.1013504","DOIUrl":"10.1371/journal.pcbi.1013504","url":null,"abstract":"<p><p>Energy metabolism is essential to brain function and Bioinformatics, but its study is experimentally challenging. Similarly, biologically accurate computational models are too complex for simple investigations. Here, we analyse an experimentally-calibrated multiscale model of human brain energy metabolism using Computational Singular Perturbation. This approach leads to the novel identification of functional periods during and after synaptic activation, and highlights the central reactions and metabolites controlling the system's behaviour within those periods. We identify a key role for both oxidative and glycolytic astrocytic metabolism in driving the brain's metabolic circuitry. We also identify phosphocreatine as the main endogenous energy supply to brain cells, and propose revising our view of brain energy metabolism accordingly. Our approach highlights the importance of glial cells in brain metabolism, and introduces a systematic and unbiased methodology to study the dynamics of complex biochemical networks that can be scaled, in principle, to metabolic networks of any size and complexity.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013504"},"PeriodicalIF":3.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145138500","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}

{"title":"How to write a scientific paper in fifteen steps.","authors":"John M Drake, Barbara A Han","doi":"10.1371/journal.pcbi.1013505","DOIUrl":"10.1371/journal.pcbi.1013505","url":null,"abstract":"","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013505"},"PeriodicalIF":3.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12459795/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145138443","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}