bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spatial profiling of longitudinal glioblastoma reveals consistent changes in cellular architecture, post-treatment
Authors: Ajaib Shoaib , Pollock Steven , Hemmings Gemma , Gusnanto Arief , Chakrabarty Aruna , Ismail Azzam , Wilson Erica , Hunter Bethany , Filby Andrew , McDonald David , Brockman Asa A. , Ihrie Rebecca A. , Stead Lucy F. 
Published: February 2025

AbstractGlioblastoma (GBM), the most aggressive adult brain cancer, comprises a complex tumour microenvironment (TME) with diverse cellular interactions driving progression and pathobiology. How these spatial patterns and interactions evolve with treatment remains unclear. Here, we apply imaging mass cytometry to analyse protein-level changes in paired pre- and post-treatment GBM samples from five patients. We find a significant post-treatment increase in normal brain cells alongside a reduction in vascular cells. Moreover, despite minimal overall change in cellular diversity, interactions among astrocytes, oligodendrocytes, and vascular cells increase post-treatment, suggesting reorganisation of the TME. The GBM TME cells form spatially organized layers driven by hypoxia pre-treatment, but this influence diminishes post-treatment, giving way to less organised layers with organisation driven by reactive astrocytes and lymphocytes. These findings provide insight into treatment-induced shifts in GBM’s cellular landscape, highlighting aspects of the evolving TME that appear to facilitate recurrence and are, therefore, potential therapeutic targets.Key pointsSpatial organisation in primary GBM consist of layers driven by the presence of hypoxiaThe layers in recurrent GBM appear are driven more by the presence of reactive astrocytesIncreased cellular cross-talk in recurrent GBM presents novel therapeutic targets
A Fast Approach for Structural and Evolutionary Analysis Based on Energetic Profile Protein Comparison
Authors: Choopanian Peyman , Andressoo Jaan-Olle , Mirzaie Mehdi 
Published: February 2025

AbstractIn structural bioinformatics, the efficiency of predicting protein similarity, function, and evolutionary relationships is crucial. Our approach proposed herein leverages protein energy profiles derived from a knowledge-based potential, deviating from traditional methods relying on structural alignment or atomic distances. This method assigns unique energy profiles to individual proteins, facilitating rapid comparative analysis for both structural similarities and evolutionary relationships across various hierarchical levels. Our study demonstrates that energy profiles contain substantial information about protein structure at class, fold, superfamily, and family levels. Notably, these profiles accurately distinguish proteins across species, illustrated by the classification of coronavirus spike glycoproteins and bacteriocin proteins. Introducing a separation measure based on energy profile similarity, our method shows significant correlation with a network-based approach, emphasizing the potential of energy profiles as efficient predictors for drug combinations with faster computational requirements. Our key insight is that the sequence-based energy profile strongly correlates with structure-derived energy, enabling rapid and efficient protein comparisons based solely on sequences.
Mechanistic modeling suggests stroma-targeting antibody-drug conjugates as an alternative to cancer-targeting in cases of heterogeneous target expression
Authors: Wood N Ezgi , Cengiz Anil , Gao Ming , Ratushny Alexander V , Straube Ronny 
Published: February 2025

AbstractAntibody-drug conjugates (ADCs) are gaining increasing traction in the treatment of oncological diseases; however, many clinical failures have also been observed. One key factor limiting ADC effectiveness is the heterogeneous expression of their target antigen. While the vast majority of ADCs in clinical development target antigens on cancer cells (cancer-targeting), they can also target antigens expressed on non-cancerous stromal cells in the tumor microenvironment (stroma-targeting). It remains unclear whether targeting cancer cells or stromal cells is more effective in suppressing tumor growth. Here, we present three related mathematical models to evaluate: (1) cancer-targeting ADCs with homogeneous target antigen expression, (2) cancer-targeting ADCs with heterogeneous target antigen expression, and (3) stroma-targeting ADCs. Our simulations suggest that cancer-targeting ADCs can achieve high efficacy when their target antigen is homogenously expressed. However, in cases of heterogeneous antigen expression, cancer-targeting ADCs may lead to an initial reduction in tumor size, followed by regrowth due to the elimination of antigen-positive cells and expansion of antigen-negative cells. This limitation could potentially be overcome by stroma-targeting ADCs, as antigen-positive stromal cells may continue to be recruited into the tumor by the oncogenic factors produced by the remaining cancer cells. Furthermore, we demonstrate that ADCs with more permeable payloads and less stable linkers may offer improved efficacy in the context of heterogeneous target expression.
Higher order synthetic lethals are keys to minimize cancer treatment effects on non-tumor cells
Authors: Manshadi Mehdi Dehghan , Setoodeh Payam , Ramezani Amin , Rajabzadeh Amin Reza , Zare Habil 
Published: February 2025

AbstractMetabolic rewiring in cancer cells facilitates the provision of essential precursors for the unbridled growth of tumors. Exploring these cancer-specific metabolic changes offers potential selective therapeutic strategies. However, targeting a single essential gene in cancer treatment often faces challenges, including resistance, lack of targetable oncogenes, and potential harm to non-tumor cells. Attacking multiple targets is hypothesized as a solution to overcome these issues, e.g., a synthetic lethal set, defined as a minimal combination of non-lethal genetic mutations leading to cell death. This study examined the potential of synthetic lethal sets to identify selective drug targets for 13 cancers and the corresponding non-tumor tissues, utilizing context-specific genome-scale metabolic models. To ensure the minimization of therapeutic side effects, this work introduced the concept ofstrictly-selectivedrug targets (SSDTs) and the harmlessness of identified targets in all 13 different non-tumor tissues was meticulously verified. Accordingly, for 13 types of cancers, over 500 SSDTs were identified, predominantly including higher-order synthetic lethal sets with more than two targets in each set. Interestingly, for specific cancers where single essential or synthetic lethal genes could not provide acceptable solutions, SSDTs were provided by higher-order synthetic lethal sets. Therefore, for the first time, this study successfully showed that leveraging higher-order synthetic lethal sets holds the key to promising strictly-selective solutions. Furthermore, nine quadruple SSDTs were identified which commonly target five different cancers without harming any of the 13 non-tumor tissues. Further experimental validation of these findings is required to select the most promising treatments for clinical studies.
Analytical approach of synchronous and asynchronous update schemes applied to solving biological Boolean networks
Authors: Bensussen Antonio , Arciniega-González J. Arturo , Álvarez-Buylla Elena R. , Martínez-García Juan Carlos 
Published: February 2025

AbstractCharacterizing the minimum, necessary and sufficient components to generate the dynamics of a biological system has always been a priority to understand its functioning. In this sense, the canonical form of biological systems modeled by Boolean networks accurately defines the components in charge of controlling the dynamics of such systems. However, the calculation of the canonical form might be complicated in mathematical terms. In addition, computing the canonical form does not consider the dynamical properties found when using the synchronous and asynchronous update schemes to solve Boolean networks. Here, we analyze both update schemes and their connection with the canonical form of Boolean networks. We found that the synchronous scheme can be expressed by the Chapman-Kolmogorov equation, being a particular case of Markov chains. We also discovered that the canonical form of any Boolean network can be easily obtained by solving this matrix equation. Finally, we found that the update order of the asynchronous scheme generates a set of functions that, when composed together, produce characteristic properties of this scheme, such as the conservation of fixed-point attractors or the variability in the basins of attraction. We concluded that the canonical form of Boolean networks can only be obtained for systems that use the synchronous update scheme, which opens up new possibilities for study.
Empirical Model of Focused Ultrasound-mediated Treatment for Chemotherapy Delivery to Brain Tumors
Authors: Zoofaghari Mohammad , Damrath Martin , Veletic Mladen , Balasingham Ilangko 
Published: February 2025

AbstractFocused ultrasound (FUS) has emerged as a transformative technique for enhancing drug delivery to brain tumors by temporarily and locally disrupting the blood-brain barrier (BBB). Despite significant progress in both preclinical and clinical research, a major challenge remains: the absence of a model that connects the properties of drug particles and FUS sonication parameters to therapeutic effectiveness. In this study, we introduce a novel empirical model that integrates key factors, including drug pharmacodynamics, microbubble kinetics for BBB disruption, intra-brain ultrasound signal propagation, and skull thickness variations. The model defines a new sonication parameter that encapsulates ultrasound signal characteristics and predicts the concentration of therapeutic agents internalized or bound to DNA with an accuracy exceeding 82%. By employing data from previous preclinical studies, this model facilitates the development of precise sonication protocols tailored for clinical applications. These advancements represent a significant step toward personalized FUS-mediated treatments, bridging the gap between experimental research and patient-centered therapies.
Effects of frugivore species pool and seed size on the diversity and functional composition of frugivores visiting fruiting trees
Authors: Naniwadekar Rohit , Gopal Abhishek , Mandal Rintu , Jayanth Arpitha , Sriprasertsil Vatcharavee , Ghuman Sartaj , Page Navendu , Chaplod Saniya , Lad Himanshu , Gadkari Aditya , Chandran Vignesh , Desai Natasha , Kadam Rasika , Strange Bee Choo , Chimchome Vijak , Gale George A. , Joshi Jahnavi 
Published: February 2025

ABSTRACTThe relationship between biodiversity and ecosystem functioning in seed dispersal remains understudied despite its critical role in maintaining plant diversity in the tropics. Field studies on this relationship are often confounded by environmental and phylogenetic variations across species richness gradients. We examined how overall avian frugivore species richness at a site influenced the frugivore richness, visitation rates and functional composition of two key effect traits⸺beak width and hand-wing index⸺on fruiting trees. Across six sites in tropical Asia, spanning a sevenfold gradient in frugivore species richness but with similar forest types and phylogenetically nested frugivore communities, we recorded 34,014 interactions between 134 avian frugivores and 131 plant species. Our results provide some support for the biodiversity-ecosystem functioning relationship, as higher overall frugivore species richness increased the number of frugivore species visiting individual fruiting trees but not the functional composition of frugivores. Seed size had a stronger influence on the frugivore species richness, visitation rates, and the beak size of visiting frugivores, highlighting the dominant role of morphological trait matching in influencing plant-frugivore interactions. Our findings suggest functional redundancy in certain aspects of seed dispersal effectiveness due to density compensation and the presence of key seed disperser lineages in species-poor sites.
gapTrick – Structural characterisation of protein-protein interactions using AlphaFold with multimeric templates
Authors: Chojnowski Grzegorz 
Published: February 2025

AbstractMotivationThe structural characterisation of protein-protein interactions is a key step in understanding the functions of living cells. Models of protein complexes provide important insights into interaction mechanisms, but their interpretation can be difficult if they lack fine structural detail, which is often the case in experimental and predicted models.ResultsHere I show that AlphaFold3 often fails to predict protein complexes that are either weak, or dependent on the presence of a cofactor that is not included in a prediction. To address this problem, I developed gapTrick, an AlphaFold2-based approach that uses multimeric templates to improve prediction reliability. The tool uses AlphaFold2 neural network models trained on single-chain proteins, which reduces training-set bias and allows for an extensive validation using a large benchmark set of protein complexes. I show that gapTrick improves predictions of the weak and incomplete complexes based on low-accuracy templates, such as separate protein chains rigid-body fitted into cryo-EM reconstruction of a complex. I also show that it identifies with very high precision residue-residue interactions that are critical for complex formation. The approach can aid in the interpretation of challenging experimental structures and the computational identification of protein-protein interactions.Availability and implementationThe gapTrick source code is available athttps://github.com/gchojnowski/gapTrickand requires only a standard AlphaFold2 installation to run. The repository also provides a Colab notebook that can be used to run gapTrick without installing it on the user's computer.Contactgchojnowski@embl-hamburg.de
Quantifying biological heterogeneity in nano-engineered particle-cell interaction experiments
Authors: Murphy Ryan J. , Faria Matthew , Osborne James M. , Johnston Stuart T. 
Published: February 2025

AbstractNano-engineered particles are a promising tool for medical diagnostics, biomedical imaging, and targeted drug delivery. Fundamental to the assessment of particle performance arein vitroparticle-cell interaction experiments. These experiments can be summarised with key parameters that facilitate objective comparisons across various cell and particle pairs, such as the particle-cell association rate. Previous studies often focus on point estimates of such parameters and neglect heterogeneity in routine measurements. In this study, we develop an ordinary differential equation-based mechanistic mathematical model that incorporates and exploits the heterogeneity in routine measurements. Connecting this model to data using approximate Bayesian computation parameter inference and prediction tools, we reveal the significant role of heterogeneity in parameters that characterise particle-cell interactions. We then generate predictions for key quantities, such as the time evolution of the number of particles per cell. Finally, by systematically exploring how the choice of experimental time points influences estimates of key quantities, we identify optimal experimental time points that maximise the information that is gained from particle-cell interaction experiments.
scCausalVI disentangles single-cell perturbation responses with causality-aware generative model
Authors: An Shaokun , Cho Jae-Won , Cao Kai , Xiong Jiankang , Hemberg Martin , Wan Lin 
Published: February 2025

AbstractSingle-cell RNA sequencing provides detailed insights into cellular heterogeneity and responses to external stimuli. However, distinguishing inherent cellular variation from extrinsic effects induced by external stimuli remains a major analytical challenge. Here, we present scCausalVI, a causality-aware generative model designed to disentangle these sources of variation. scCausalVI decouples intrinsic cellular states from treatment effects through a deep structural causal network that explicitly models the causal mechanisms governing cell-state-specific responses to external perturbations while accounting for technical variations. Our model integrates structural causal modeling with cross-condition in silico prediction to infer gene expression profiles under hypothetical scenarios. Comprehensive benchmarking demonstrates that scCausalVI outperforms existing methods in disentangling causal relationships, quantifying treatment effects, generalizing to unseen cell types, and separating biological signals from technical variation in multi-source data integration. Applied to COVID-19 datasets, scCausalVI effectively identifies treatment-responsive populations and delineates molecular signatures of cellular susceptibility.Code availabilitySoftware is available athttps://github.com/ShaokunAn/scCausalVI.
Self-iterative multiple instance learning enables the prediction of CD4+T cell immunogenic epitopes
Authors: Shen Long-Chen , Zhang Yumeng , Wang Zhikang , Littler Dene R. , Liu Yan , Tang Jinhui , Rossjohn Jamie , Yu Dong-Jun , Song Jiangning 
Published: February 2025

AbstractAccurately predicting the antigen presentation to CD4+T cells and subsequent induction of immune response is fundamentally important for vaccine development, autoimmune disease treatments, and cancer neoepitope identification. In immunopeptidomics, single-allelic data are highly specific but limited in allele scope, while multi-allelic data contain broader coverage at the cost of weakly labeling. Existing computational approaches either overlook the massive multi-allelic data or introduce label ambiguity due to inadequate modeling strategies. Here, we introduce ImmuScope, a weakly supervised deep-learning framework integrating precise MHC-II antigen presentation, CD4+T cell epitopes, and immunogenicity predictions. ImmuScope leverages self-iterative multiple-instance learning with positive-anchor triplet loss to explore peptide-MHC-II (pMHC-II) binding from weakly labeled multi-allelic data and single-allelic data, comprising over 600,000 ligands across 142 alleles. Moreover, ImmuScope can also interpret the MHC-II binding specificity and motif deconvolution of immunopeptidomics data. We successfully applied ImmuScope to discover melanoma neoantigens, revealing variations in pMHC-II binding and immunogenicity upon epitope mutations. We further employed ImmuScope to assess the effects of SARS-CoV-2 epitope mutations on immune escape, with its predictions aligned well with experimentally determined immune escape dynamics. Overall, ImmuScope provides a comprehensive solution for CD4+T cell antigen recognition and immunogenicity assessment, with broad potential for advancing vaccine design and personalized immunotherapy.
The influence of common testing floor surfaces on force plate data: implications for standardisation
Authors: Smith Laura , Jones Paul 
Published: February 2025

AbstractForce plate testing is commonly used to assess athlete performance, however there is no research into the effect the surface underneath the force plate has on derived variables. The aim of this study was to investigate whether different surfaces underneath a force plate would elicit differences in derived force plate variables using a mechanical testing device. A mechanical testing device was used to assess force reduction, peak force, rate of force development (RFD) and contact time across seven common testing surfaces: vinyl, rubber, Olympic lifting platform, ground (CarpetF) and first floor (CarpetF) carpet, Mondo track and a sprung gymnasium floor (Sprung). Significant differences in force reduction, peak force, RFD, and contact time were found between flooring conditions (p < 0.05), with large to extremely large effect sizes. Sprung flooring exhibited the highest force reduction and lowest peak forces, while CarpetF demonstrated the lowest RFD and longest contact time. These findings highlight the flooring surface underneath the force plate during testing significantly influenced derived variables. Practitioners should exert caution and consideration to force plate testing location and advocate standardisation in flooring surface in order to ensure consistent and accurate results.
Social connections are differentially related to perceived and physiological age acceleration amongst older adults
Authors: Fancourt Daisy , Bloomberg Mikaela , Steptoe Andrew 
Published: February 2025

AbstractHuman social connections are complex ecosystems formed of structural, functional and quality components. Deficits in social connections are associated with adverse age-related health outcomes, but we know little about the ageing-related mechanistic processes underlying this. Using data from 7,047 adults aged 50+ in the English Longitudinal Study of Ageing, we explored associations between diverse aspects of social deficits and both perceived and physiological age acceleration, which provide complementary psycho-behavioural and biological mechanistic explanations. We created and validated a novel physiological ageing index using clinical indicators pertaining to the cardiovascular, respiratory, haematologic, metaboloic and cognitive systems using principal component analysis. Doubly-robust estimations using inverse-probability-weighted regression adjustment estimators showed that living alone, low social integration and high social isolation were risk factors for physiological age acceleration, with those who lived alone on average 1.9 years older than those who lived with others (95% CI 0.9-3.0 years older; 32% greater age acceleration than people who live with others). However, social deficits were not related to accelerations in perceived age. Analyses were robust to multiple sensitivity analyses and maintained four years later. These findings provide important mechanistic insight that helps to explain the relationship between social deficits and age-related morbitidy and mortality outcomes.