bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated behavioral profiling using neural networks reveals differences in stress-like behavior between cave and surface-dwellingAstyanax mexicanus
Authors: Padmanaban Naresh , Ambosie Rianna , Choy Stefan , Marcus Shoshanah , Nilsson Simon R.O. , Keene Alex C. , Kowalko Johanna E. , Duboué Erik R.
Published: February 2025

Behavioral stress responses allow animals to quickly adapt to local environments and are critical for survival. Stress responses provide an ideal model for investigating the evolution of complex behaviors due to their conservation across species, critical role in survival, and integration of behavioral and physiological components. The Mexican cavefish (Astyanax mexicanus) has evolved dramatically different stress responses compared to river-dwelling surface fish morphs, providing a model to investigate the neural and evolutionary basis of stress-like responses. Surface morphs inhabit predator-rich environments whereas cave-dwelling morphs occupy predator-free habitats. While these key ecological variables may underlie differences in stress responses, the complexity of the behavioral differences has not been thoroughly examined. By leveraging automated pose-tracking and machine learning tools, we quantified a range of behaviors associated with stress, including freezing, bottom-dwelling, and hyperactivity, during a novel tank assay. Surface fish exhibited heightened stress responses characterized by prolonged bottom-dwelling and frequent freezing, while cavefish demonstrated reduced stress behaviors, marked by greater exploration and minimal freezing. Analysis of F2 hybrids revealed that a subset of behaviors, freezing and bottom-dwelling, co-segregated, suggesting shared genetic or physiological underpinnings. Our findings illustrate the power of computational tools for high-throughput behavioral phenotyping, enabling precise quantification of complex traits and revealing the genetic and ecological factors driving their evolution. This study provides a framework for understanding how integrated behavioral and physiological traits evolve, offering broader insights into the mechanisms underlying the diversification of animal behavior in natural systems.
Clustered VEGF Nanoparticles in Microporous Annealed Particle (MAP) Hydrogel Accelerates Functional Recovery and Brain Tissue Repair after Stroke
Authors: Erning Kevin , Wilson Katrina L. , Smith Cara S. , Nguyen Long , Joesph Neica I. , Irengo Rachel , Cao Lauren Y. , Cumaran Mohanapriya , Shi Yi , Lyu Sihan , Riley Lindsay , Dunn Timothy W. , Carmichael S. Thomas , Segura Tatiana
Published: February 2025

Ischemic stroke, a blockage in the vasculature of the brain that results in insufficient blood flow, is one of the world’s leading causes of disability. The cascade of inflammation and cell death that occurs immediately following stroke drives vascular and functional loss that does not fully recover over time, and no FDA-approved therapies exist that stimulate regeneration post-stroke. We have previously developed a hydrogel scaffold that delivered heparin nanoparticles with and without VEGF bound to their surface to promote angiogenesis and reduce inflammation, respectively. However, the inclusion of the naked heparin nanoparticles warranted concern over the development of bleeding complications. Here, we explore how microporous annealed particle (MAP) scaffolds functionalized with VEGF coated heparin nanoparticles can both reduce inflammation and promote angiogenesis - without the inclusion of free heparin nanoparticles. We show that our updated design not only successfully promotes de novo tissue formation, including the development of mature vessels and neurite sprouting, but it also leads to functional improvement in a photothrombotic stroke model. In addition, we find increased astrocyte infiltration into the infarct site correlated with mature vessel formation. This work demonstrates how our biomaterial design can enhance endogenous regeneration post-stroke while eliminating the need for excess heparin.
Identification of High-Risk Cells in Single-Cell Spatially Resolved Transcriptomics Data Using Deep Transfer Learning
Authors: Chatterjee Debolina , Couetil Justin L. , Liu Ziyu , Dong Tianhan , Huang Kun , Chen Chao , Zhang Jie , Kalwat Michael A. , Johnson Travis S.
Published: February 2025

Background

The examination of high-risk cells and regions in tissue samples from spatially resolved transcriptomics platforms, offers meaningful insights into specific disease processes. For existing methods, while cell types or clusters can be identified and associated with disease attributes, individual cells are unable to be associated in the same manner which may result in failing to identify subsets of cells associated with disease attributes especially if the disease-associated cells cluster together with non-disease-associated cells.

Method

DEGAS (Diagnostic Evidence Gauge of Single-cells) [5], solves the above problem with a sophisticated deep transfer learning algorithm designed to identify high-risk components in single-cell RNA sequencing data from tumor samples. DEGAS employs latent representations of gene expression data and domain adaptation to transfer disease attributes from patients to individual cells. In this research, we present DEGAS’s versatility in adapting to data arising from single cell spatially resolved transcriptomics platforms such as the 10X Genomics Xenium platforms, and Nanostring’s CosMx platform. By integrating spatial location information from the above platforms, DEGAS can not only identify high-risk components in tissue samples but also pinpoint locations within the slides associated with disease status.

Results

We evaluated DEGAS across multiple platforms, including 10X Genomics Xenium and Nanostring CosMx. DEGAS successfully identified high-risk cells and regions, which were validated through known markers. Additionally, DEGAS was applied to our newly generated T2D Xenium dataset and a publicly available melanoma Xenium dataset. We tested DEGAS on publicly available Nanostring CosMx FFPE samples of normal and Hepatocellular Carcinoma tissues, revealing high-risk cells and topologies associated with key pathways. Notably, high-risk regions were predominantly enriched in tumor tissue, with DEGAS uncovering heterogeneity that correlates with aggressive disease markers and cell type diversity.
SurfNet: Reconstruction of Cortical Surfaces via Coupled Diffeomorphic Deformations
Authors: Zheng Hao , Li Hongming , Fan Yong
Published: February 2025

To achieve fast and accurate cortical surface reconstruction from brain magnetic resonance images (MRIs), we develop a method to jointly reconstruct the inner (white-gray matter interface), outer (pial), and midthickness surfaces, regularized by their interdependence. Rather than reconstructing these surfaces separately without taking into consideration their interdependence as in most existing methods, our method learns three diffeomorphic deformations jointly to optimize the midthickness surface to lie halfway between the inner and outer cortical surfaces and simultaneously deforms it inward and outward towards the inner and outer cortical surfaces, respectively. The surfaces are encouraged to have a spherical topology by regularization terms for non-negativeness of the cortical thickness and symmetric cycle-consistency of the coupled surface deformations. The coupled reconstruction of cortical surfaces also facilitates an accurate estimation of the cortical thickness based on the diffeomorphic deformation trajectory of each vertex on the surfaces. Validation experiments have demonstrated that our method achieves state-of-the-art cortical surface reconstruction performance in terms of accuracy and surface topological correctness on large-scale MRI datasets, including ADNI, HCP, and OASIS.1
Optical Brain Pulse Monitoring for Detection of Large Vessel Occlusion Stroke in a Sheep Model
Authors: Sharkey Jessica M. , Teo Elliot J. , Chung Sung W. , Grace Sally A. , Hellerstedt Jack , Petautschnig Sigrid , Prag Hiran A. , Turner Renée J. , Krieg Thomas , Murphy Michael P. , Sorby-Adams Annabel , Dixon Barry
Published: February 2025

Early stroke detection and treatment are critical for improving patient outcomes. Optical brain pulse monitoring (OBPM) uses red and infrared light to capture brain pulse waveforms reflecting arteriole-to-venous pressure levels driving microvascular blood flow. This study assessed OBPM’s potential to detect middle cerebral artery occlusion (MCAo) and reperfusion in a clinically relevant sheep model. Stroke was induced in 11 Merino wethers via 4-hour occlusion of the right MCA, followed by 6 hours of reperfusion. OBPM recordings were taken at baseline, MCAo, early and late reperfusion. The OBPM brain pulse waveform classes were classified based on the presence of arterial or central venous circulation wave features. Magnetic resonance imaging assessed infarct volume at 2 hours post-reperfusion. Invasive brain tissue oxygen and intracranial pressures were also monitored. The OBPM brain pulse waveform classes changed during MCAo and reperfusion (p<0.0001). MCAo was associated brain pulses with venous circulation features (p= 0.0007). Reperfusion was associated with the return of arterial circulation features (p= 0.001). Early reperfusion was also associated with an increase in the brain pulse amplitude (p< 0.05) and the respiratory wave amplitude (p< 0.05). OBPM may aid in early stroke detection and reperfusion assessment following intervention.
Genetic and environmental contributions to ReHo and fALFF in early adolescence vary across brain regions
Authors: Strike Lachlan T , McMahon Katie L , Medland Sarah E , de Zubicaray Greig I
Published: February 2025

Research on genetic and environmental influences on brain function generally focuses on connections between brain areas. A different yet unexplored approach is to examine activity within local brain regions. We investigated the influence of genes and environmental effects on two specific measures of local brain function: Regional Homogeneity (ReHo) and fractional Amplitude of Low-Frequency Fluctuations (fALFF). Participants were drawn from a sample of adolescent twins on two occasions (mean ages 11.5 and 13.2 years,N= 278 and 248). Results showed that genetic and environmental factors influenced brain function in almost all 210 cortical regions examined. Moreover, genetic and common environmental factors influencing ReHo and fALFF values at wave 1 (9-14 years) also influenced values at wave 2 (10-16 years) for many regions. However, the influence of genetic and common environmental factors varied across the cortex, exhibiting different patterns in different regions. Furthermore, we found new (i.e., independent) genetic and environmental influences on brain activity at wave 2, again with regional patterns. Exploratory analyses found weak associations between anxiety and depressive symptoms and local brain function in several regions of the temporal lobe. These findings are consistent with similar studies of other resting-state functional MRI metrics (i.e., functional connectivity).
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

Glioblastoma (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 points

Spatial organisation in primary GBM consist of layers driven by the presence of hypoxia

The layers in recurrent GBM appear are driven more by the presence of reactive astrocytes

Increased 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

In 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

Antibody-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

Metabolic 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.
Reassessing Deep Learning (and Meta-Learning) Computer Vision as an efficient method to determine taphonomic agency in bone surface modifications
Authors: Domínguez-Rodrigo Manuel , Cifuentes-Alcobendas Gabriel , Vegara-Riquelme Marina , Baquedano Enrique
Published: February 2025

Recent critiques of the reliability of deep learning (DL) for taphonomic analysis of bone surface modifications (BSM), such as that presented by Courtenay et al. (2024) based on a selection of earlier published studies, have raised concerns about the efficacy of the method. Their critique, however, overlooked fundamental principles regarding the use of small and unbalanced datasets in DL. By reducing the size of the training and validation sets—resulting in a training set only 20% larger than the testing set, and some class validation sets that were under 10 images—these authors may inadvertently have generated underfit models in their attempt to replicate and test the original studies. Moreover, errors in coding during the preprocessing of images have resulted in the development of fundamentally biased models, which fail to effectively evaluate and replicate the reliability of the original studies. In this study, we do not aim to directly refute their critique, but instead use it as an opportunity to reassess the efficiency and resolution of DL in taphonomic research. We revisited the original DL models applied to three targeted datasets, by replicating them as new baseline models for comparison against optimized models designed to address potential biases. Specifically, we accounted for issues stemming from poor-quality image datasets and possible overfitting on validation sets. To ensure the robustness of our findings, we implemented additional methods, including enhanced image data augmentation, k-fold cross-validation of the original training-validation sets, and a few-shot learning approach using model-agnostic meta-learning (MAML). The latter method facilitated the unbiased use of separate training, validation, and testing sets. The results across all approaches were consistent, with comparable—if not almost identical—outcomes to the original baseline models. As a final validation step, we used images of recently generated BSM to act as testing sets with the baseline models. The results also remained virtually invariant. This reinforces the conclusion that the original models were not subject to methodological overfitting and highlights their nuanced efficacy in differentiating BSM. However, it is important to recognize that these models represent pilot studies, constrained by the limitations of the original datasets in terms of image quality and sample size. Future work leveraging larger datasets with higher-quality images has the potential to enhance model generalization, thereby improving the applicability and reliability of deep learning approaches in taphonomic research.
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

Characterizing 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

Focused 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

The 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

Motivation

The 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.

Results

Here 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 implementation

The 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.

Contact

gchojnowski@embl-hamburg.de
High LET Radiation: A Novel Strategy to Overcome Tumor Microtubes -Mediated Radioresistance in Glioblastoma
Authors: Dokic Ivana , Ciamarone Federica , Hoffmann Dirk , Bojcevski Jovana , Krunic Damir , Tessonnier Thomas , Winkler Frank , Debus Juergen , Venkataramani Varun , Mairani Andrea , Wick Wolfgang , Abdollahi Amir
Published: February 2025

Tumor cell networks formed by tumor microtubes (TMs) may play a key role in the development of therapy resistance in glioblastoma (GB). TM-mediated detoxification from radiation-induced reactive oxygen species (ROS) may infer radioresistance. We hypothesize that high linear energy transfer (LET) radiation, which describes the amount of energy deposited by radiation per unit length, interacts directly with the DNA backbone to induce complex lesions and thus might be less dependent on TM-mediated resistance mechanisms. Therefore, we sought to systematically investigate the impact of LET-induced complex DNA damage on TM formation and GB survival. To this end, the formation of TMs, radiation-induced nuclear DNA damage repair foci (RIF), and GB survival were correlated with a gradual increase in LET using a dose series of clinical protons (low), helium (intermediate), and carbon (high) ion beams. Consistent with conventional photon/X-rays, low-LET proton irradiation promoted TM formation in a dose-dependent manner. In contrast, an anti-correlation between LET and TM induction was found, i.e., a decreased network connectivity with gradual increase of LET and formation of complex DNA damage. Consequently, LET increase correlated with reduced cell survival, with the most pronounced cell killing observed after high-LET carbon irradiation. Moreover, the inverse correlation between LET and TM density was further confirmed for a broad range of LET modulated within the carbon ion irradiation spectrum. This is the first report on the relevance of LET as a novel mean to overcome TM network-mediated radioresistance in GB, with ramifications for the clinical translation of high-LET particle radiotherapy to further improve outcome in this still devastating disease.
Pathogen-Induced Damage in Drosophila: Uncoupling Disease Tolerance from Resistance
Authors: Akyaw Priscilla A , Paulo Tania F , Lafuente Elvira , Sucena Elio
Published: February 2025

Immune response against infections can be divided into mechanisms of resistance that ensure active pathogen elimination, and mechanisms of disease tolerance, which include processes that return the host to physiological homeostasis without involving direct pathogen control. Studies on host immune responses to infection have mostly targeted mechanisms of resistance, and consequently, these are now well-described in both vertebrates and invertebrates. By comparison, the mechanistic basis of disease tolerance is less well understood. This is in part because both processes interact and can be difficult to separate under an infection scenario. Using the highly tractable insect model Drosophila melanogaster exposed to its natural entomopathogen, Pseudomonas entomophila, we aimed to tease apart mechanisms of disease tolerance from those of resistance. To this aim, we reasoned that oral exposure to heat-killed entomopathogenic bacteria should require disease tolerance without relying on resistance. Using this method, we observe that oral exposure to heat-killed P. entomophila causes mortality and reduced fecundity in D. melanogaster. We confirm that this reduction in fitness-related traits depends on the duration of the exposure, is sexually dimorphic, and is dependent on the virulence of the bacterium. We also found the microbiota to play a role, with its presence exacerbating the deleterious effect on host survival. This experimental framework, which may be extended to other systems, can be instrumental towards an understanding of the molecular, genetic, and physiological basis of disease tolerance and its interactions with resistance mechanisms.
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

Nano-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

Single-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 availability

Software 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

Accurately 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

Force 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

Human 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.
Heterochromatin boundaries maintain centromere position, size and number
Authors: Carty Ben L , Dubocanin Danilo , Murillo-Pineda Marina , Dumont Marie , Volpe Emilia , Mikulski Pawel , Humes Julia , Whittingham Oliver , Fachinetti Daniele , Giunta Simona , Altemose Nicolas , Jansen Lars E.T.
Published: February 2025

Centromeres are chromosomal loci that ensure proper chromosome segregation by providing a platform for kinetochore assembly and spindle force transduction during cell division. Human centromeres are defined primarily by a unique chromatin domain featuring the histone H3 variant, Centromere Protein A (CENP-A), that marks a single active centromere locus per chromosome. CENP-A chromatin typically occupies a small subregion of low DNA methylation within multi-megabase arrays of hypermethylated alpha-satellite repeats and constitutive pericentric heterochromatin. However, the mechanisms defining and maintaining precise centromere position and domain size, and the role of the underlying alpha satellite DNA sequence, are poorly characterised. Using an experimentally-induced neocentromere in RPE1 cells, we discovered that the SUV39H1 and H2 methyltransferases tri-methylate H3K9 at neocentromere boundaries to maintain CENP-A domain size independent of DNA methylation or satellite sequences. Furthermore, we found that the CENP-A domain at canonical alpha-satellite-based centromeres is characterized by local depletion of H3K9me3-mediated heterochromatin, coinciding with the DNA methylation dip region. We identified the SETDB1 methyltransferase as key to maintaining H3K9me3 within flanking active higher order alpha satellite arrays while SUV39s and SUZ12 contribute to globally heterochromatinize both alpha satellites and neighbouring repeats. Loss of this heterochromatin boundary results in the progressive expansion of the primary CENP-A domain, erosion of DNA methylation, and the nucleation of new centromeres across alpha satellite sequences. Our study identifies the functional specialization of different H3K9 methyltransferases across centromeric and pericentric domains, crucial for maintaining centromere domain size and number.
The distortion-push mechanism for the γ-subunit rotation in F1-ATPase
Authors: Motohashi Masahiro , Oide Mao , Kobayashi Chigusa , Jung Jaewoon , Muneyuki Eiro , Sugita Yuji
Published: February 2025

F1-ATPase comprises the stator ring consisting of α₃β₃ subunits and the rotor γ subunit. The γ subunit rotation mechanism has been extensively investigated by biochemical analyses, structural studies, single-molecule measurements, and computational studies. Recent cryo-electron microscopy (cryo-EM) structures of F1-ATPase from the thermophilic bacteriumBacillusPS3 (TF1) provide us with further possibilities for a better understanding of the γ-rotation mechanisms. Using cryo-EM structures having the γ-rotation angles close to the binding dwell and catalytic dwell states, we investigate the relationships between the γ subunit rotation, conformational changes of the stator α3β3subunits, and the nucleotide-binding and release. We performed targeted molecular dynamics (MD) simulations with external forces on the α₃β₃ subunits and observed 80° substep rotations of the γ subunit. Then, we optimized the most probable transition pathway through the mean-force string method simulations with 64 images. Finally, using umbrella sampling, we calculated the potential of mean forces along the minimum free energy pathway during the 80° substep rotation. Our MD simulations suggest that 80° substep rotation is divided into the first rotation, resting, and the second rotation. Notably, the first rotation is driven by the distortion of the stator α3β3subunits, and the second rotation is induced mainly by direct β/γ subunit interactions. This model, which we call the distortion-push mechanism, is consistent with the residue-level experimental analysis on F1-ATPase and the atomic structures determined by X-ray crystallography and cryo-EM.
Genes and Pathways Comprising the Human and Mouse ORFeomes Display Distinct Codon Bias Signatures that Can Regulate Protein Levels
Authors: Davis Evan T. , Raman Rahul , Byrne Shane R. , Ghanegolmohammadi Farzan , Mathur Chetna , Begley Ulrike , Dedon Peter C. , Begley Thomas J.
Published: February 2025

Arginine, glutamic acid and selenocysteine based codon bias has been shown to regulate the translation of specific mRNAs for proteins that participate in stress responses, cell cycle and transcriptional regulation. Defining codon-bias in gene networks has the potential to identify other pathways under translational control. Here we have used computational methods to analyze the ORFeome of all unique human (19,711) and mouse (22,138) open-reading frames (ORFs) to characterize codon-usage and codon-bias in genes and biological processes. We show that ORFeome-wide clustering of gene-specific codon frequency data can be used to identify ontology-enriched biological processes and gene networks, with developmental and immunological programs well represented for both humans and mice. We developed codon over-use ontology mapping and hierarchical clustering to identify multi-codon bias signatures in human and mouse genes linked to signaling, development, mitochondria and metabolism, among others. The most distinct multi-codon bias signatures were identified in human genes linked to skin development and RNA metabolism, and in mouse genes linked to olfactory transduction and ribosome, highlighting species-specific pathways potentially regulated by translation. Extreme codon bias was identified in genes that included transcription factors and histone variants. We show that re-engineering extreme usage of C- or U-ending codons for aspartic acid, asparagine, histidine and tyrosine in the transcription factorsCEBPBandMIER1,respectively, significantly regulates protein levels. Our study highlights that multi-codon bias signatures can be linked to specific biological pathways and that extreme codon bias with regulatory potential exists in transcription factors for immune response and development.

Graphical Abstract
Master control genes in the regeneration of rod photoreceptors from endogenous progenitor cells in zebrafish retina
Authors: Shihabeddin Eyad , Santhanam Abirami , Tetenborg Stephan , Aronowitz Alexandra L. , Wei Haichao , Qin Guoting , Cai Chengzhi , Wu Jiaqian , O’Brien John
Published: February 2025

Retinitis Pigmentosa is a chronic retinal degenerative disease characterized by the gradual loss of rod, and later, cone photoreceptors until the individual is completely blind. Regeneration of photoreceptors from endogenous progenitor cells is a possible therapeutic approach, but mammals do not do this naturally. Mammalian models can be induced to generate retinal progenitors from Müller glial cells, but there has been limited success in rod photoreceptor specific regeneration. Unlike mammals, zebrafish have the natural ability to regenerate neurons after injury or disease and can provide insight into the molecular mechanisms of regeneration. In this study, we used a zebrafish model of Retinitis Pigmentosa to investigate the class of progenitors responsible for rod photoreceptor regeneration in the context of chronic disease. Using bioinformatic analyses of single-cell RNA sequencing datasets, we identified master regulator genes responsible for proliferation of retinal progenitors, differentiation of progenitors into rod photoreceptors, and maturation of the new rod photoreceptors. Using transient knockdown of gene expression in adult regenerating retina we determined thate2f1,e2f2,e2f3andaurkbare critical for proliferation of progenitors, andprdm1ais critical for differentiation of progenitors into rod photoreceptors. This study provides a list of master regulators responsible for the specific regeneration of rod photoreceptors during chronic retinal degeneration.

Impact Statement

Identification of master regulating genes that drive the proliferation of progenitor cells and their differentiation specifically into rod photoreceptors provides insight that can be used to develop regenerative therapies for retinal degenerative diseases.