PLoS Computational Biology最新文献

Fetal cardiac arrhythmias can lead to stillbirth, but direct studies on the human fetal heart are challenging. To address this, we developed a computational model of human fetal ventricular myocyte (hfVM) electrophysiology, focusing on early gestation (10 weeks). This model incorporates major ionic currents, including fetal-specific T-type calcium and funny currents, and is calibrated using mRNA expression data and experimental measurements. The hfVM model replicates key electrophysiological features, such as a shorter action potential duration and a more positive resting membrane potential compared to adult cells. Global sensitivity analysis reveals that the resting membrane potential is primarily influenced by the funny current and IK1, while action potential repolarisation depends mainly on IKr. Additionally, the sarcoplasmic reticulum contributes to calcium release, but less so than in adults; instead, the T-type calcium current and the sodium-calcium exchanger are more prominent in initiating calcium transients. This is the first human fetal ventricular myocyte model available for studying fetal cardiac physiology, pathology, and potential pharmacological interventions. It provides novel insights into the dominant ion channels governing fetal electrophysiology and calcium dynamics, offering a foundation for understanding arrhythmias and guiding therapeutic strategies.

Nucleoid compaction in bacteria is commonly attributed to cytoplasmic crowding, DNA supercoiling, and nucleoid-associated proteins (NAPs). In most bacterial species, including E. coli, these effects condense the chromosome into a subcellular region and largely exclude ribosomes to the surrounding cytoplasm. In contrast, many Mycoplasma-including the Mycoplasma-derived synthetic cell JCVI-Syn3A-exhibit a cell-spanning nucleoid with ribosomes distributed throughout. Because Mycoplasma are evolutionarily distant from model bacteria like E. coli and have undergone extensive genome reduction, Syn3A is a natural testbed for genotype-to-'physiotype'-to-phenotype, in which genome-encoded composition reshapes cell-scale organization. Here we show that this organization can arise from Syn3A's unusually high abundance of positively charged proteins. We develop a coarse-grained model that explicitly and physically represents a sequence-accurate chromosome together with ribosomes and cytoplasmic proteins at physiological size, charge, and abundance. With DNA and ribosomes alone, the cell-spanning nucleoid relaxes toward a compacted state that sterically excludes ribosomes, indicating missing physics beyond polymer mechanics and excluded volume. When we include electrostatic interactions by assigning effective charges to each biomolecule, positively charged proteins dynamically enrich around ribosomes and DNA, partially screening ribosome-DNA repulsion. This charge shielding enables ribosomes to penetrate the nucleoid mesh and stabilizes a cell-spanning nucleoid consistent with experiment. This behavior is robust across parameter sweeps: DNA stiffness, heterogeneous mesh size, and crowding favor compaction, whereas electrostatics and size polydispersity promote expansion, with consequences for migration pathways within the nucleoid and thus transcription-translation dynamics. The framework is parameterized directly from genomic and proteomic composition and is transferable to other bacteria.

PlasticEnz is a new open-source tool for detecting plastic-degrading enzymes (plastizymes) in metagenomic data by combining sequence homology-based search with machine learning techniques. It integrates custom Hidden Markov Models, DIAMOND alignments, and polymer-specific classifiers trained on ProtBERT embeddings to identify candidate depolymerases from user-provided contigs, genomes, or protein sequences. PlasticEnz supports 11 plastic polymers with ML classifiers for PET and PHB, achieving F1 > 0.7 on an independent test set. Applied to plastic-exposed microcosms and field metagenomes, the tool recovered known PETases and PHBases, distinguished plastic-contaminated from pristine environments, and clustered predictions with validated reference enzymes. PlasticEnz is fast, scalable, and user-friendly, providing a robust framework for exploring microbial plastic degradation potential in complex communities.

Egocentric representations of the environment have historically been relegated to being used only for simple forms of spatial behaviour such as stimulus-response learning. However, in the many cases that critical aspects of policies are best defined relative to the self, egocentric representations can be advantageous. Furthermore, there is evidence that forms of egocentric representation might exist in the wider hippocampal formation. Nevertheless, egocentric representations have yet to be fully incorporated as a component of modern navigational methods. Here we investigate egocentric successor representations (SRs) and their combination with allocentric representations. We build a reinforcement learning agent that combines an egocentric SR with a conventional allocentric SR to navigate complex 2D environments. We demonstrate that the agent learns generalisable egocentric and allocentric value functions which, even when only additively composed, allow it to learn policies efficiently and to adapt to new environments quickly. Our work shows the benefit for egocentric relational structure to be captured, as well as allocentric. We offer a new perspective on how cognitive maps could usefully be composed from multiple simple maps representing associations between state features defined in different reference frames.

Glioblastoma multiforme (GBM) is the most aggressive form of brain cancer with the very poor survival and high recurrence rate. Tumor-associated neutrophils (TANs) play a pivotal role in regulation of the tumor microenvironment. In this study, we developed a new mathematical model of the critical GBM-TAN interaction in the heterogeneous brain tissue. The model reveals that the dual and complex role of TANs (either anti-tumorigenic N1 and the pro-tumorigenic N2 type) regulates the phenotypic trajectory of the evolution of tumor growth and the invasive patterns in white and gray matter via mediators such as IFN-β and TGF-β. We investigated the effect of normalizing the immune environment on glioma growth by applying a therapeutic antibody and developed several strategies for eradication of tumor cells by neutrophil-mediated transport of nanoparticles. We also developed a strategy of combination therapy (surgery + Trojan neutrophils) for effective control of the infiltration of the glioma cells in one hemisphere before crossing the corpus callosum (CC) in order to prevent recurrence in the other hemisphere. This alternative approach compared to the extended resection of the glioma including CC or butterfly GBM may provide the greater anti-tumor efficacy and reduce side effects such as cognitive impairment.

Spatial transcriptomics enables the measurement of gene expression in intact tissues. Despite this, reconstructing anatomically accurate spatial domains remains challenging, primarily due to expression sparsity, complex tissue architecture that is characterized by sharp boundaries and long-range continuity, and weak spatial signals. Traditional pipelines typically rely on expression-driven clustering and spatial smoothing, which underperform at boundaries and in sparse regions while neglecting morphological information. To address these challenges, AugGCL is proposed, an augmented graph-convolutional learning framework that enhances spatial structure decoding and gene expression reconstruction through targeted augmentation of both gene and image data. A key component of AugGCL is neighborhood information aggregation mechanism, which integrates expression similarity and spatial proximity to construct a weighted graph and an enhanced expression matrix, addressing sparsity without sacrificing boundary clarity. Additionally, a two stream weighted graph convolutional network jointly models refined gene features and image-derived morphological information, with image-aware auxiliary reconstructions enhancing weak spatial signals and sharpening boundaries. On datasets from the human dorsolateral prefrontal cortex, breast cancer, and mouse embryo, AugGCL outperforms baseline methods across multiple metrics, showing robustness and generalization across a range of datasets. Downstream analysis validated the reliability of the method, confirming its effectiveness in cell annotation, functional enrichment, and mechanistic studies. AugGCL generates clearer spatial domains and significantly advances the application of spatial transcriptomics in tissue structure and disease research.

Injuries are an unfortunate but inevitable fact of life, leading to an evolutionary mandate for powerful homeostatic processes of recovery and recuperation. The physiological responses of the body and the immune system must be coordinated with behaviour to allow protected time for this to happen, and to prevent further damage to the affected bodily parts. Reacting appropriately requires an internal control system that represents the nature and state of the injury and specifies and withholds actions accordingly. We bring the formal uncertainties embodied in this system into the framework of a partially observable Markov decision process. We discuss nociceptive phenomena in light of this analysis, noting particularly the counter-intuitive behaviours associated with injury investigation, and the propensity for transitions from normative, tonic, to pathological, chronic pain states. Importantly, these simulation results provide a quantitative account and enable us to sketch a much needed roadmap for future theoretical and experimental studies on injury, tonic pain, and the transition to chronic pain.

Proteins function through dynamic interactions with other proteins in cells, forming complex networks fundamental to cellular processes. While high-resolution and high-throughput methods have significantly advanced our understanding of how proteins interact with each other, the molecular details of many important protein-protein interactions are still poorly characterized, especially in non-mammalian species, including Drosophila. Recent advancements in deep learning techniques have enabled the prediction of molecular details in various cellular pathways at the network level. In this study, we used AlphaFold2 Multimer to examine and predict protein-protein interactions from both physical and functional datasets in Drosophila. We found that functional associations contribute significantly to high-confidence predictions. Through detailed structural analysis, we also found the importance of intrinsically disordered regions in the predicted high-confidence interactions. Our study highlights the importance of disordered regions in protein-protein interactions and demonstrates the importance of incorporating functional interactions in predicting physical interactions between proteins. We further compiled an interactive web interface to present these predictions, facilitating functional exploration, comparative analysis, and the generation of mechanistic hypotheses for future studies.

Mitosis is a complex self-organising process that achieves high fidelity separation of duplicated chromosomes into two daughter cells through capture and alignment of chromosomes to the spindle mid-plane. Chromosome movements are driven by kinetochores (KTs), multi-protein machines that attach chromosomes to microtubules (MTs), and through those attachments both control and generate directional forces. Using lattice light sheet microscopy imaging and automated near-complete tracking of kinetochores at fine spatio-temporal resolution, we produce a detailed atlas of kinetochore metaphase-anaphase dynamics in untransformed human cells (RPE1). Such data allows dynamic models to be reverse engineered and biological hypotheses to be addressed. We determined the support from this dataset for 17 models of metaphase dynamics using Bayesian inference, demonstrating (1) substantial sister asymmetry that generates transverse organisation of the metaphase plate (MPP), (2) substantial spatial organisation of KT dynamic properties within the MPP, and (3) significant time dependence of the K-fiber mechanical parameters whereby K-fiber forces tune over the last 5 mins of metaphase towards a set point, referred to as the anaphase ready state. These spatio-temporal trends are robust to perturbation of the spindle assembly pathway (nocodazole washout treatment), suggesting that the underlying processes generating kinetochore heterogeneity are intrinsic to mitosis and possibly play a role in ensuring high-fidelity segregation.

Accurate real-time estimation of the effective reproduction number (Rt) is critical for infectious disease surveillance and response. In vector-borne diseases like dengue, temperature strongly influences disease transmission by affecting generation times. However, most existing Rt estimation methods assume a fixed generation interval, leading to biased estimates and unreliable assessments of transmission risk in settings with fluctuating temperatures. In this study, we proposed and evaluated a novel framework to estimate a temperature-dependent reproduction number (td-Rt) that dynamically updates the generation interval distribution based on observed temperature data. We obtained real-time estimates of td-Rt through an adapted Bayesian recursive filtering process. Using real and simulated data for a temperature-sensitive disease (dengue), we evaluated the performance of td-Rt against the typically used temperature-independent reproduction number (ti-Rt) and angular reproduction number ([Formula: see text]), which does not require specification of the generation interval. Simulated data was generated under varying patterns of underlying Rt and temperature datasets. Performance was evaluated by classification accuracy, defined by the proportion of instances where estimated Rt correctly identified whether the true Rt was above or below 1. We found that td-Rt generally outperformed ti-Rt and [Formula: see text] in classifying periods of epidemic growth. td-Rt achieved the highest classification accuracy in 54 of 72 simulation scenarios, with accuracy ranging from 37.1%-95.9%. td-Rt accuracy was highest in scenarios with greater temperature variability, surpassing other methods by up to 20%. With Singapore dengue case data, td-Rt and [Formula: see text] signals showed 75% similarity, highlighting [Formula: see text]'s potential as a complementary measure that is less sensitive to model assumptions. These findings highlight the importance of accounting for temperature in real-time transmissibility estimates, as temperature-driven variations in generation time can introduce model misspecification and bias. Incorporating temperature is especially crucial for climate-sensitive diseases like dengue. Future work could extend this framework to other pathogens and additional transmission-relevant covariates.