bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-invasive approach for endoluminal biopsy coupled with single-cell proteomics allows for immune characterization of intracranial aneurysms
Authors: Antonios J. , Gultekin B. , Theriault B. , Yalcin K. , Miyagishima D. , Adenu-Mensah N. , Sujijantarat N. , Koo A. , Haynes J. , Cedeno P. , Johnson M. , Hebert R. , Matouk C. , Barak T. , Gunel M. 
Published: February 2025

AbstractThe immune regulatory mechanisms driving the stability, growth, and rupture of intracranial aneurysms (IAs) remain incompletely understood. In this study, we employ endoluminal biopsy with single-cell proteomics to comprehensively profile the immune landscape of IAs across their pathologic states. Our findings reveal distinct immune signatures associated with aneurysm states. Stable, i.e. non-growing unruptured, IAs exhibit a balanced immune cell composition. Ruptured IAs are marked by significant neutrophil predominance. Notably, we highlight key immune markers in aneurysm instability, offering new insights into immune drivers of aneurysm progression. These findings provide a foundation for immune-targeted, non-invasive therapeutic strategies aimed at targeting IAs and preventing rupture.
Non-invasive and unbiased assessment of thermogenesis in mice through thermal gradient ring
Authors: Melo Paulo De , Pereira Nayara , Araujo Rafaela Braun , Festuccia William T. , Cunha Thiago Mattar , Leiria Luiz Osório 
Published: February 2025

ABSTRACTAccurately assessing whole-body heat production requires reliable thermometry methods. In mice, common approaches include rectal temperature (RT) measurement, infrared (IR) thermography, and implanted probes. However, factors such as stress, handling, surgery, and variability limit their applicability for evaluating thermogenesis. The Thermal Gradient Ring (TGR), widely used in neuropathic pain and ion channel studies, consists of a circular structure with twelve temperature zones and an integrated IR camera for real-time behavior monitoring. This system allows precise analysis of preferred temperature (PT), heat tolerance, locomotion, and zone occupancy over time, thereby offering a behavioral perspective beyond traditional thermometric methods, which provides only temperature data. In this study, we evaluated TGR as a non-invasive tool for detecting thermogenic changes. Since mice with higher thermogenesis prefer cooler zones, while those with reduced thermogenesis seek warmth, TGR provides a sensitive readout of metabolic behavior. Using models with both enhanced and impaired thermogenesis, we demonstrated TGR’s ability to detect thermogenic status under different conditions. These findings suggest that TGR is a valuable tool for metabolic research, offering a reliable alternative for assessing thermogenesis in mice.
NRGRank: Coarse-grained structurally-informed ultra-massive virtual screening
Authors: Descoteaux Thomas , Najmanovich Rafael 
Published: February 2025

ABSTRACTNRGRank is a coarse-grained structurally-informed software capable of performing ultra-massive virtual screening with accuracy comparable to docking-based methodologies but with 100 fold speed increase. NRGRank is based on a coarse-grained evaluation of pairwise atom-type pseudo-energy interactions that implicitly accounts for compound and side-chain flexibility as well as limited backbone movements. We compare NRGRank to docking-based virtual screening software Glide, Autodock Vina and DOCK 3.7 on the DUD-E virtual screening benchmark using enrichment factors at 1% (EF1). We observe broad variations of EF1 values across targets, structural models and methods. For apo form or AlphaFold2 models, out of a subset of 37 targets from DUD-E, NRGRank has better EF1 values than Glide for 12 and 13 targets respectively. Even in holo form, where the accuracy of docking software increases, NRGRank has better EF1 values than 13, 10 and 5 targets out of 37 compared to AutoDock Vina, DOCK 3.7 and Glide respectively. Comparing the rank of true binders in Glide and NRGRank shows that true binders ranked in the top 1% are complementary between methods irrespective of the target form (AlphaFold, apo or holo). That is, utilizing NRGRank detects binders that are missed by Glide (and presumably other methods), whereas those found by Glide are missed by NRGRank. Furthermore, we observe that most hits found by NRGRank within the top 50 predictions remain once the top 1% of predictions when re-scored with Glide, but the hit rate within the top 50 predictions increase. NRGRank can evaluate one molecule in 0.3 s on average, enabling a modern laptop with 8 cores to screen 1,000,000 molecules in 24 hours, up to two orders of magnitude faster than the reported speed of DOCK 3.7, AutoDock Vina running on GPUs and Glide. NRGRank occupies a unique niche among tools for virtual screening being insensitive to structural inaccuracies but with comparable accuracy as state-of-the-art docking methods and fast as AI-based methods but without the dangers of overfitting as it is based on 780 pseudo-energy parameters. Combined with the fact that NRGRank does not require extensive or expensive computational resources or expert pre-processing of targets, it is unique in making high-performance ultra-massive virtual screening accessible to all.
In vitrocellular phenotypes of cortical neurons from R255XMECP2knock-in mice are improved by either expression of wildtype MeCP2 or ‘read-through’ with G418
Authors: Xu Xin , Merritt Jonathan , Gray Steven J. , Neul Jeffrey , Pozzo-Miller Lucas 
Published: February 2025

ABSTRACTApproximately 60% of individuals with Rett syndrome (RTT) carry a nonsense variant in theMECP2gene; thus, there is an unmet need to identify novel nonsense suppression compound(s) that can restore full length MeCP2 protein levels and function. Here, we characterized neuronal phenotypes in cultured cortical neurons from newborn knock-in mice harboring theMECP2R255X variant. After 2 weeksin vitro, R255X mutant neurons showed smaller cell bodies, shorter dendrites, fewer dendritic branches, and a lower density of excitatory synapses when compared to wildtype (WT) neurons. Transduction of AAV9-MeCP2-GFP in R255X mutant neurons made these cellular phenotypes similar to those in WT neurons, including soma size, dendritic length and branching, and excitatory synapse density. As proof of principle for the potential clinical use of ‘read-through’ compounds, cultured R255X mutant neurons treated with the aminoglycoside G418 for 72hin vitroshowed cell body size and excitatory synapse density similar to WT neurons. We expect these combined approaches will identify effective compounds to suppress translation termination at a premature termination codon, which can be moved to further preclinical functional and behavioral studies in R255XMECP2knock-in mice.Summary StatementExpression of wildtypeMECP2or treatment with G418in vitrorestored cell body size, dendritic length, and dendritic spine density in cortical neurons from R255XMECP2knock-in mice to levels comparable to wildtype neurons.
Agent-based modeling of cellular dynamics in adoptive cell therapy
Authors: Wang Yujia , Casarin Stefano , Daher May , Mohanty Vakul , Dede Merve , Shanley Mayra , Başar Rafet , Rezvani Katayoun , Chen Ken 
Published: February 2025

AbstractAdoptive cell therapies (ACT) leverage tumor-immune interactions to cure cancer. Despite promising phase I/II clinical trials of chimeric-antigen-receptor natural killer (CAR-NK) cell therapies, molecular mechanisms and cellular properties required to achieve clinical benefits in broad cancer spectra remain underexplored. Whilein vitroandin vivoexperiments are required in this endeavor, they are typically expensive, laborious, and limited to targeted investigations. Here, we present ABMACT (Agent-Based Model for Adoptive Cell Therapy), anin silicoapproach employing agent-based models (ABM) to simulate the continuous course and dynamics of an evolving tumor-immune ecosystem, consisting of heterogeneous “virtual cells” created based on knowledge and omics data observed in experiments and patients. Applying ABMACT in multiple therapeutic context indicates that to achieve optimal ACT efficacy, it is key to enhance immune cellular proliferation, cytotoxicity, and serial killing capacity. With ABMACT,in silicotrials can be performed systematically to inform ACT product development and predict optimal treatment strategies.
Universal orthologs infer deep phylogenies and improve genome quality assessments
Authors: Alam Md Nafis Ul , Román-Palacios Cristian , Copetti Dario , Wing Rod A. 
Published: February 2025

AbstractUniversal single-copy orthologs are the most conserved components of genomes. Although they are routinely used for studying evolutionary histories and assessing new assemblies, current methods do not incorporate information from available genomic data. Here, we first determine the influence of evolutionary history on universal gene content in plants, fungi and animals. We find that across 11,098 genomes comprising 2,606 taxonomic groups, 215 groups significantly vary from their respective lineages in terms of their BUSCO (Benchmarking Universal Single Copy Orthologs) completeness. Additionally, 169 groups display an elevated complement of duplicated orthologs, likely as an artifact of whole genome duplication events. Secondly, we investigate the extent of taxonomic congruence in BUSCO-derived whole-genome phylogenies. For 275 suitable families out of 543 tested, sites evolving at higher rates produce at most 23.84% more taxonomically concordant, and at least 46.15% less terminally variable phylogenies compared to lower-rate sites. We find topological differences between BUSCO concatenated and coalescent trees to be marginal and conclude that higher rate sites from concatenated alignments produce the most congruent and least variable phylogenies. Finally, we show that BUSCO misannotations can lead to misrepresentations of assembly quality. To overcome this issue, we filter a Curated set of BUSCOs (CUSCOs) that provide up to 6.99% fewer false positives compared to the standard BUSCO search and introduce novel methods for comparing assemblies using BUSCO synteny. Overall, we highlight the importance of considering evolutionary histories during assembly evaluations and release the phyca software toolkit that reconstructs consistent phylogenies and reports phylogenetically informed assembly assessments.
A comprehensive identification and annotation of mobile LINE-1 retrotransposons across human reference and personal genomes
Authors: Ferrasa Adriano , Herai Roberto H. 
Published: February 2025

AbstractLINE-1 (L1) is a type of mobile genetic element (retrotransposon) that can self-copy into distinct parts of its host genome by dynamically shaping its content. In humans, misregulation of these elements have been associated as the potential cause of distinct types of pathologies and neurodevelopmental disorders, including cancer, Rett syndrome and Aicardi Goutierrez disease. However, the precise annotation of these 6 kb long nucleotide sequence is a challenging task due to the high similarity between their internal regions (coding and non-coding) for distinct L1 families. Current L1 reference annotations such as RepeatMasker still lack sequence details and also provide confounding annotated information, that includes thousands of incomplete L1 sequences annotated as representative full-length, and missing information on their regulatory and coding regions (5’UTR, ORF1, intron, ORF2 and 3’UTR). Additionally, current L1 resources also provide annotations based on consensus reference genomes and does not include individual genome information at allele-level and nucleotide-level resolution. Here, we present L1Farm as a complementary databank and annotation resource that contains a comprehensive analysis and annotation of L1 subfamilies identified in the current human reference genome (build Hg38) and in two individual Asian and Caucasian genomes at allele-level and nucleotide-level resolution. L1 elements sequences were also annotated with genomiclociand ranked according to their full-length content and similarity.
JAK inhibitors remove innate immune barriers to allow viral propagation
Authors: Ravlo Erlend , Ianevski Aleksandr , Skipperstøen Marius Nårstad , Lysvand Hilde , Schjølberg Jørn-Ove , Vapalahti Olli , Smura Teemu , Vauhkonen Hanna , Oksenych Valentyn , Weber Friedemann , Strand Mårten , Evander Magnus , Malmring Janne Fossum , Afset Jan Egil , Bjørås Magnar , Kainov Denis E. 
Published: February 2025

AbstractJanus kinase (JAK) inhibitors are small-molecule therapeutics that reduce inflammation in autoimmune and inflammatory diseases by modulating the JAK-STAT pathway. While effective in alleviating immune-mediated conditions, JAK inhibitors can impair antiviral defences by suppressing interferon (IFN) responses, potentially increasing susceptibility to viral infections. This study investigates the pro-viral mechanism of JAK inhibitors, focusing on baricitinib, across various cell lines, retinal organoids, and viral strains, including a recombinant IFN-sensitive Rift Valley fever (rRVFV), influenza A (rIAV), SARS-CoV-2 (rSARSCoV-2) reporter viruses, and wild type adenovirus (AdV). Our findings demonstrate that baricitinib suppresses transcription of IFN-stimulated genes in non-infected cells (ISGs) which is triggered by type I IFNs produced by infected cells, facilitating viral propagation. The pro-viral effects were influenced by viral load, inhibitor concentration, and structural characteristics of the compound. These results underscore the dual effects of JAK inhibitors: reducing inflammation while potentially exacerbating viral infections. Additionally, the findings highlight opportunities to leverage JAK inhibitors for viral research, vaccine production, and drug screening.
Leveraging AlphaFold2 Structural Space Exploration for Generating Drug Target Structures in Structure-Based Virtual Screening
Authors: Uchikawa Keisuke , Furui Kairi , Ohue Masahito 
Published: February 2025

AbstractIn early drug discovery, computational virtual screening (VS) is vital for selecting candidate compounds and reducing costs. However, the lack of experimentally determined 3D structures has limited the application of structure-based VS. Advances in protein structure prediction, notably AlphaFold2, have begun to address this gap. Yet, studies indicate that direct use of AlphaFold2-predicted structures often leads to suboptimal VS performance—likely because these structures fail to capture ligand-induced conformational changes (apo-to-holo transitions). To overcome this, we propose an approach that explores and modifies the structural space of AlphaFold2 predictions to generate conformations more amenable to VS. Our method deliberately alters the multiple sequence alignment (MSA) by introducing alanine mutations at key residues in the ligand-binding site, thereby inducing significant conformational shifts. The exploration process is guided by iterative ligand docking simulations, with mutation strategies optimized either by a genetic algorithm or via random search. Our evaluation shows that when sufficient active compounds are available, the genetic algorithm significantly enhances VS accuracy. In contrast, with limited active compound data, a random search strategy proves more effective. Moreover, our approach is particularly promising for targets that yield poor screening results when using experimentally determined structures from the PDB. Overall, these findings underscore the practical utility of modified AlphaFold2-derived structures in VS and expand the potential of computationally predicted protein models in drug discovery.
SpaFun: Discovering Domain-specific Spatial Expression Patterns and New Disease-Relevant Genes using Functional Principal Component Analysis
Authors: Jiang Xi , Guo Yanghong , Guo Lei , Zhong Lin , Wang Jiayi , Xiao Guanghua , Li Qiwei , Xu Lin 
Published: February 2025

AbstractSpaFun is a novel, non-model-based method developed to address limitations in existing spatially variable gene (SVG) detection techniques, particularly for large-scale spatially resolved transcriptomics (SRT) datasets. These limitations include computational inefficiency, limited statistical power with increasing data size, and the inability to capture spatial heterogeneity and co-expression patterns among genes. Built on functional principal component analysis (fPCA), SpaFun identifies domain-representative genes (DRGs) with significantly better computational efficiency and greater statistical power while accounting for spatial heterogeneity and co-expression patterns among genes. We applied SpaFun to three SRT datasets and demonstrated that SpaFun outperformed state-of-the-art algorithms for identifying representative genes for tumor regions (e.g., DESeq, edgeR, and limma), as well as recently developed novel algorithms designed for spatial omics to identify the representative genes (e.g., SPARK and CSIDE). This highlights SpaFun’s ability to accurately identify genes most representative of each spatial domain (e.g., tumor, immune, or stroma regions). By uncovering novel disease-relevant genes overlooked by existing algorithms, SpaFun could provide insights into new molecular mechanisms and propose innovative therapeutic strategies to improve patient outcomes.Key PointsSpaFun is the first method dedicated to identifying DRGs, capturing spatially representative expression patterns within annotated tissue regions, setting it apart from all traditional SVG detection methods.It leverages fPCA to model gene expression as a function of spatial location, avoiding reliance on predefined spatial distribution assumptions.The non-model-based framework ensures compatibility with different SRT platforms and experimental designs, making it a scalable and widely applicable tool for SRT research.
Genome modeling and design across all domains of life with Evo 2
Authors: Brixi Garyk , Durrant Matthew G. , Ku Jerome , Poli Michael , Brockman Greg , Chang Daniel , Gonzalez Gabriel A. , King Samuel H. , Li David B. , Merchant Aditi T. , Naghipourfar Mohsen , Nguyen Eric , Ricci-Tam Chiara , Romero David W. , Sun Gwanggyu , Taghibakshi Ali , Vorontsov Anton , Yang Brandon , Deng Myra , Gorton Liv , Nguyen Nam , Wang Nicholas K. , Adams Etowah , Baccus Stephen A. , Dillmann Steven , Ermon Stefano , Guo Daniel , Ilango Rajesh , Janik Ken , Lu Amy X. , Mehta Reshma , Mofrad Mohammad R.K. , Ng Madelena Y. , Pannu Jaspreet , Ré Christopher , Schmok Jonathan C. , John John St. , Sullivan Jeremy , Zhu Kevin , Zynda Greg , Balsam Daniel , Collison Patrick , Costa Anthony B. , Hernandez-Boussard Tina , Ho Eric , Liu Ming-Yu , McGrath Thomas , Powell Kimberly , Burke Dave P. , Goodarzi Hani , Hsu Patrick D. , Hie Brian L. 
Published: February 2025

AbstractAll of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation—from noncoding pathogenic mutations to clinically significantBRCA1variants—without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon–intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.
Detection of mpox clade Ib nucleic-acids in wastewater solids at 147 wastewater treatment plants across the United States
Authors: Boehm Alexandria B. , Wolfe Marlene K. , Bidwell Amanda L. , White Bradley J. , Shelden Bridgette , Duong Dorothea 
Published: February 2025

AbstractWe used a sensitive, specific PCR assay to detect mpox clade Ib DNA in over 3000 wastewater samples collected prospectively across the United States. Mpox clade Ib DNA was detected in one sample from a location with a confirmed case; it was not detected in locations with no confirmed cases.
Enhancing sleep in professional rugby players: observation and sleep interventions
Authors: Goldberg Mathias , Boutin Arnaud , Pairot de Fontenay Benoit , Cohn Joffrey , Michel Valentin , Stauffer Emeric , Debarnot Ursula 
Published: February 2025

AbstractSleep is a crucial factor in recovery and must be integrated into athletes’ training plans for optimal performance and well-being. Although professional athletes are advised to sleep for at least 8 hours, many experience shorter sleep durations or poor sleep quality. Sleep interventions have been recently proposed to improve sleep, but their effects remain unclear. This ecological study aimed to evaluate the sleep of a rugby team and to assess the effects of sleep interventions, including sleep hygiene education and relaxation techniques. Thirty-six male professional rugby players were evaluated during two pre- and post-intervention match weeks using objective (actimetric) and subjective (questionnaires) assessments. At baseline, 34 on 36 athletes slept less than 8h per night. Combining both sleep dimensions, 61.1 % of players were considered poor sleepers. After sleep interventions, subjective sleep quality improved (p= 0.001,η2= 0.22), and athletes went to bed earlier (23:28 ± 00:42 vs. 23:43 ± 00:45 during pre-intervention;p= 0.01,η2= 0.15). Positive effects of sleep interventions were especially observed among poor sleepers as their objective sleep quantity increased (405.2 min pre-intervention vs. 425.9 min post-interventionp= 0.004,η2= 0.28). Sleep interventions, composed of theoretical and practical sessions, improved sleep characteristics and might be implemented in athletes’ daily routine. This study offers a simple and accessible method to assess athletes’ sleep, while providing adapted recommendations to optimize or enhance sleep quality and quantity.Key points61.1% of the rugby players exhibited both poor sleep quality and quantity during the week and weekends.Theoretical and practical sleep interventions elicited objective and subjective sleep improvement, especially among poor sleepers.Sleep assessment and interventions represent an efficient, and feasible method among team-sport players for improving sleep quality.