bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hypoxia-induced histone methylation and NF-κB activation in pancreas cancer fibroblasts promote EMT-supportive growth factor secretion
Authors: Kowalewski Karl M. , Adair Sara J. , Talkington Anne , Wieder Jason J. , Pitarresi Jason R. , Perez-Vale Kia , Chu Britney , Dolatshahi Sepideh , Sears Rosalie , Stanger Ben Z. , Bauer Todd W. , Lazzara Matthew J. 
Published: February 2025

ABSTRACTThe pancreatic ductal adenocarcinoma (PDAC) tumor microenvironment contains hypoxic tissue subdomains and cancer-associated fibroblasts (CAFs) of multiple subtypes that play tumor-promoting and -restraining roles. Here, we demonstrate that hypoxia promotes an inflammatory-like CAF phenotype and that hypoxic CAFs selectively promote epithelial-mesenchymal transition (EMT) in PDAC cancer cells through growth factor-mediated cell crosstalk. By analyzing patient tumor single-cell transcriptomics and conducting an inhibitor screen, we identified IGF-2 and HGF as specific EMT-inducing growth factors produced by hypoxic CAFs. We further found that reactive oxygen species-activated NF-κB cooperates with hypoxia-dependent histone methylation to promote IGF-2 and HGF expression in hypoxic CAFs. In lineage-traced autochthonous PDAC mouse tumors, hypoxic CAFs resided preferentially near hypoxic, mesenchymal cancer cells. However, in subcutaneous tumors engineered with hypoxia fate-mapped CAFs, once-hypoxic re-oxygenated CAFs lacked a spatial correlation with mesenchymal cancer cells. Thus, hypoxia promotes reversible CAF-malignant cell interactions that drive EMT through druggable signaling pathways.One-sentence summaryWe show that hypoxic fibroblasts in pancreas cancer leverage histone methylation and ROS-mediated NF-κB activation to produce growth factors that drive epithelial-mesenchymal transition in malignant cells, demonstrating how tumor stromal features cooperate to initiate a signaling process for disease progression.
Impaired Macrophage DNase Secretion Promotes Neutrophil Extracellular Trap Mediated Defective Efferocytosis in Atherosclerosis
Authors: Dhawan Umesh Kumar , Vartak Tanwi , Englert Hanna , Singhal Aarushi , Chakraborty Rahul , Bhagat Karran Kiran , Vasconcellos Luiz Ricardo C. , McDonnell Ciaran , Connolly Mary , Mulkern Edward , O’Donohoe Martin , Gelderblom Mathias , Renne Thomas , Godson Catherine , Brennan Eoin , Subramanian Manikandan 
Published: February 2025

AbstractNeutrophil extracellular traps (NETs) drive atherosclerosis progression and are associated with adverse clinical outcomes like myocardial infarction and stroke. While the triggers of NETosis in atherosclerotic plaque are well-characterized, the mechanisms underlying NET degradation and clearance remain unclear. Moreover, the impact of impaired NET clearance on atherosclerosis progression have not been elucidated. Here we show that macrophages are critical for the release of DNases, which degrade NETs. We identified endoplasmic reticulum (ER) stress mediated activation of PERK-ATF4 pathway as a key driver of impaired macrophage DNase secretion, leading to delayed NET clearance and their persistence. Elevated NET levels trigger cleavage of the efferocytosis receptor Mertk leading to defective macrophage efferocytosis and exacerbation of plaque necrosis. Human atherosclerotic plaques andLdlr-/-mice treated with ISRIB, a PERK inhibitor, show enhanced DNase secretion and clearance of NETs. Together, the identification of key mechanisms of NET clearance in atherosclerosis offers new therapeutic strategies to stabilize plaques.
GenomeOcean: An Efficient Genome Foundation Model Trained on Large-Scale Metagenomic Assemblies
Authors: Zhou Zhihan , Riley Robert , Kautsar Satria , Wu Weimin , Egan Rob , Hofmeyr Steven , Goldhaber-Gordon Shira , Yu Mutian , Ho Harrison , Liu Fengchen , Chen Feng , Morgan-Kiss Rachael , Shi Lizhen , Liu Han , Wang Zhong 
Published: February 2025

ABSTRACTGenome foundation models hold transformative potential for precision medicine, drug discovery, and understanding complex biological systems. However, existing models are often inefficient, constrained by suboptimal tokenization and architectural design, and biased toward reference genomes, limiting their representation of low-abundance, uncultured microbes in the rare biosphere. To address these challenges, we developedGenomeOcean, a 4-billion-parameter generative genome foundation model trained on over 600 Gbp of high-quality contigs derived from 220 TB of metagenomic datasets collected from diverse habitats across Earth’s ecosystems. A key innovation of GenomeOcean is training directly on large-scale co-assemblies of metagenomic samples, enabling enhanced representation of rare microbial species and improving generalizability beyond genome-centric approaches. We implemented a byte-pair encoding(BPE) tokenization strategy for genome sequence generation, alongside architectural optimizations, achieving up to 150× faster sequence generation while maintaining high biological fidelity. GenomeOcean excels in representing microbial species and generating protein-coding genes constrained by evolutionary principles. Additionally, its fine-tuned model demonstrates the ability to discover novel biosynthetic gene clusters (BGCs) in natural genomes and perform zero-shot synthesis of biochemically plausible, complete BGCs. GenomeOcean sets a new benchmark for metagenomic research, natural product discovery, and synthetic biology, offering a robust foundation for advancing these fields.
The imbalance of nature: The Role of Species Environmental Responses for Ecosystem Stability
Authors: Polazzo Francesco , Hämmig Til , Petchey Owen L. , Pennekamp Frank 
Published: February 2025

AbstractUnderstanding the mechanisms underlying ecosystem stability is crucial in predicting ecological responses to environmental fluctuations. While the diversity-stability relationship has been widely studied, the role of species’ fundamental responses to the environment remains underexplored. Here, we investigate how the distribution of fundamental responses, captured by a novel metric—imbalance—drives ecosystem stability through asynchrony and population stability. Using a microcosm experiment with protist communities, we manipulated species richness and response distributions (defined as interspecific variation in species performance curves) under fluctuating temperature and different nutrient concentrations. Our results show that lower imbalance, achieved through asynchrony or high population stability, causes higher temporal stability, while richness has no effect on stability. Structural equation modelling revealed that imbalance decreases stability indirectly via increasing synchrony and decreasing population stability, explaining 90% of observed variation. Comparing imbalance derived from single versus multispecies communities demonstrates that fundamental species responses are primary drivers of stability, challenging traditional paradigms emphasizing interspecific interactions. This study provides mechanistic links between species’ responses, environmental variability, and ecosystem stability, offering new insights into the responses of ecological systems to environmental change.
Unravelling epigenetic regulation of gene expression with explainable AI - a case study leveraging degron data
Authors: Chhatbar Kashyap , Bird Adrian , Sanguinetti Guido 
Published: February 2025

AbstractTranscriptional regulation involves complex interactions involving chromatin–associated proteins, but disentangling these mechanistically remains challenging. Here, we generate deep learning models to predict RNA Pol–II occupancy from chromatin–associated protein profiles in unperturbed conditions. We evaluate the suitability of Shapley Additive Explanations (SHAP), a widely used explainable AI (XAI) approach, to infer causality and analyse regulatory mechanisms across diverse datasets and to validate these insights using data from degron–based perturbation experiments. Remarkably, genes ranked by SHAP importance predict direct targets of perturbation even from unperturbed data, enabling inference without costly experimental interventions. Our analysis reveals that SHAP not only predicts differential gene expression but also captures the magnitude of transcriptional changes. We validate the cooperative roles of SET1A and ZC3H4 at promoters and distinct regulatory contributions of ZC3H4 at gene bodies in influencing transcription. Cross-dataset validation uncovers unexpected connections between ZC3H4, a component of the Restrictor complex, and INTS11, part of the Integrator complex, suggesting crosstalk mediated by H3K4me3 and the SET1/COMPASS complex in transcriptional regulation. These findings highlight the power of integrating predictive modelling and experimental validation to unravel complex context-dependent regulatory networks and generate novel biological hypotheses.
ggRibo: a ggplot-based single-gene viewer for visualizing Ribo-seq and related omics datasets
Authors: Wu Hsin-Yen Larry , Kaufman Isaiah D. , Hsu Polly Yingshan 
Published: February 2025

ABSTRACTVisualizing periodic Ribo-seq data within genes of interest is a powerful approach to studying mRNA translation, but its application is limited by a lack of robust tools. Here, we introduceggRibo, a user-friendly R package for visualizing individual gene expression, integrating Ribo-seq, RNA-seq, and other genome-wide datasets with flexible scaling options.ggRibopresents the 3-nucleotide periodicity, a hallmark of translating ribosomes, within a gene-structure context, including introns and untranslated regions, enabling the study of novel ORFs, isoform translation, and mechanisms of translational regulation.ggRibocan plot multiple Ribo-seq/RNA-seq datasets from different conditions for comparison. Additionally, it supports the visualization of other omics datasets that could also be presented with single-nucleotide resolution, such as RNA degradome, transcription start sites, and translation initiation sites. Through its intuitive and flexible platform,ggRiboenables parallel comparisons of multi-omic datasets, facilitating a comprehensive understanding of gene expression regulation and promoting hypothesis generation. We demonstrate its utility with examples of upstream ORFs, downstream ORFs, isoform translation, and multi-omic comparison in humans and Arabidopsis. In summary,ggRibois an advanced single-gene viewer that enhances the interpretation of translatome and related genome-wide datasets, offering a valuable resource for studying gene expression regulation.ggRibois available on GitHub (https://github.com/hsinyenwu/ggRibo).
Strange effects of activation dynamics on musculoskeletal trajectory optimization
Authors: van den Bogert Antonie J. (Ton) 
Published: February 2025

SummaryMuscle activation dynamics is usually described with a nonlinear differential equation, such that the activation occurs faster than deactivation. When a muscle excitation input switches rapidly between two values, this produces a “pumping” effect that raises the activation above the mean excitation input. This effect will favor rapid switching strategies when excitation is used in the optimization objective for human movement. The optimal strategy has infinitely fast switching, and is difficult to obtain with direct collocation methods. This problem can be eliminated by using activation, rather than excitation, in the optimization objective.
HSP70 is a chaperone for IL-33 secretion and function in chronic airway disease
Authors: Osorio Omar A. , Kluender Colin E. , Raphael Heather E. , Hassan Ghandi F. , Cohen Lucy S. , Steinberg Deborah F. , Payne Morgan D. , Luo Ethan M. , Hicks Jamie L. , Katz-Kiriakos Ella , Byers Derek E. , Alexander-Brett Jennifer 
Published: February 2025

ABSTRACTRationaleIL-33 is a key driver of type 2 inflammation relevant to airway epithelial biology. However, the mechanisms for IL-33 secretion and regulation in the context of chronic airway disease is poorly understood.ObjectivesWe sought to define how a disease associated isoform IL-33Δ34that escapes nuclear sequestration and is tonically secreted from epithelial cells can be recruited to non-canonical secretory pathways.MethodsIL-33Δ34interaction with HSP70 was assessed and validated by affinity purification, mass-spectrometry and miniTurboID proximity labeling. Secretion and activity reporter assays were used to probe the effect of HSP70 on epithelial IL-33Δ34secretion and receptor binding. Human airway disease biospecimens were analyzed for dysregulation of heat shock pathways revealing modulation of TCP1 complex intermediates.Measurements and Main ResultsWe confirmed that HSP70 interacts directly with IL-33Δ34, recruits the cytokine to a vesicular compartment and enhances stability upon secretion. IL-33, HSP70 and other key mediators of proteostasis were found to be dysregulated in airway disease biospecimens and secreted extracellular vesicles. The IL-33Δ34interactome was characterized and novel secretion modulators were identified.ConclusionsThis study confirms a role for HSP70 in non-canonical IL-33Δ34secretion and function that may be amenable for therapeutic targeting in airway diseases.
HIDE: Hierarchical cell-type Deconvolution
Authors: Völkl Dennis , Mensching-Buhr Malte , Sterr Thomas , Bolz Sarah , Schäfer Andreas , Seifert Nicole , Tauschke Jana , Rayford Austin , Straume Oddbjørn , Zacharias Helena U. , Grellscheid Sushma Nagaraja , Beissbarth Tim , Altenbuchinger Michael , Görtler Franziska 
Published: February 2025

AbstractMotivationCell-type deconvolution is a computational approach to infer cellular distributions from bulk transcriptomics data. Several methods have been proposed, each with its own advantages and disadvantages. Reference based approaches make use of archetypic transcriptomic profiles representing individual cell types. Those reference profiles are ideally chosen such that the observed bulks can be reconstructed as a linear combination thereof. This strategy, however, ignores the fact that cellular populations arise through the process of cellular differentiation, which entails the gradual emergence of cell groups with diverse morphological and functional characteristics.ResultsHere, we propose Hierarchical cell-type Deconvolution (HIDE), a cell-type deconvolution approach which incorporates a cell hierarchy for improved performance and interpretability. This is achieved by a hierarchical procedure that preserves estimates of major cell populations while inferring their respective subpopulations. We show in simulation studies that this procedure produces more reliable and more consistent results than other state-of-the-art approaches. Finally, we provide an example application of HIDE to explore breast cancer specimens from TCGA.AvailabilityA python implementation of HIDE is available at zenodo (doi:10.5281/zenodo.14724906).Supplementary informationSupplementary material is available atBioinformaticsonline.
MDSC depletion during immunization with heat-killedMycobacterium tuberculosisincreases protection against BCG infection
Authors: Aintablian Arpa , Alattar Haisam , Cyran Laura , Schoen Christoph , Du Plessis Nelita , Walzl Gerhard , Schaible Ulrich , Nieuwenhuizen Natalie E. , Lutz Manfred B. 
Published: February 2025

AbstractTuberculosis (TB) is still one of the deadliest infectious diseases globally. Although i.d. BCG immunization offers limited protection, a vaccine based onMycobacterium tuberculosis(Mtb) has yet to be approved. Our previous findings demonstrated that s.c. immunization with heat-killed Mtb significantly increased the number of monocytic myeloid-derived suppressor cells (M-MDSC) in mice. Therefore, we hypothesized that the defense against a subsequent BCG infection would be impaired in Mtb-immunized mice. Surprisingly, mice vaccinated with Mtb were protected against a BCG infection and showed elevated frequencies and activation of DC and mycobacteria-specific T cells despite high frequencies and suppressor activity of M-MDSC. Genetic ablation of CCR2+monocytic cells or pharmacological intervention with all-trans retinoic acid (ATRA) reduced the frequency of Mtb-induced M-MDSC, enhanced frequencies, and activation of dendritic cells (DC) and CD4+T cells, and resulted in decreased bacterial loads in the lung and spleen. These findings offer fresh perspectives on TB vaccination using heat-killed Mtb despite parallel unwanted vaccine-induced M-MDSC. M-MDSC depletion by ATRA further tips the balance towards immunity and should be considered an adjunct host-directed therapy with TB vaccines in humans.
Impact of mating strategies on life-history traits in the alien land snailRumina decollata
Authors: Pizá Julia , Cifola Lara , Perl Melisa , Abafatori Matías , Bonel Nicolás 
Published: February 2025

AbstractClimate change and global transport are driving species introductions worldwide, leading to economic and ecological consequences. Hermaphroditic organisms are able to reproduce with any conspecific, and some can self-fertilize, enhancing their potential for population establishment despite low initial densities in colonization events. This study examines how mating strategies influence the life history traits of the alien land snailRumina decollataby comparing individuals subjected to facultative cross-fertilization or enforced self-fertilization over two laboratory-reared generations. Key life history traits—including size and age at first reproduction, fecundity, hatching time, and juvenile survival—were measured, alongside individual growth and shell morphometry. Self-fertilizing individuals exhibited higher body weight at first clutch but lower fecundity and delayed reproduction compared to cross-fertilizers. Selfing offspring (F2) took longer to hatch and had lower survival rates, indicating significant inbreeding depression. Selfed snails of F1grew faster than outcrossers, but experienced a decline in growth in F2, consistent with inbreeding depression. Conversely, shell shape remained similar between mating treatments. Although selfing imposed fitness costs, 32% of self-fertilizing individuals produced viable offspring, highlighting their ability to establish in new environments. This study improves our understanding of howR. decollata’s reproductive strategies shape life history traits under environmental constraints.SIMPLE SUMMARYSpecies are spreading to new regions due to climate change and global transport, often causing environmental and economic challenges. Hermaphrodite species that can reproduce by cross or self-fertilization can establish populations even when few individuals are present. In our study, we examined how these two reproductive strategies affect a land snail’s growth, reproduction, and survival. We found that self-fertilizing snails grew larger and heavier but laid fewer eggs and reproduced later than those that mated with others. Additionally, their offspring took longer to hatch and had lower survival rates. These results suggest that self-fertilization comes with costs, likely due to inbreeding depression, but it still allows the species to establish in new environments. Despite lower survival, one-third of the self-fertilizing snails produced viable offspring, showing that this strategy can support population growth when mates are scarce. As this species continues to expand into new areas, our findings help explain how it adapts to different environments. Understanding these reproductive strategies is important for predicting its spread and managing its potential impact on ecosystems.
Comparative Extraction of Cellular Features from High-Resolution Volume Imaging
Authors: Mimori-Kiyosue Yuko , Hamaji Tomoko , Hayashi Akiko , Okada Miwa , Higasa Koichiro , Kiyosue Kazuyuki , Gomibuchi Yuki , Yasunaga Takuo , Washio Takashi , Hara Satoshi 
Published: February 2025

AbstractHigh-resolution volume imaging techniques, such as lattice light-sheet microscopy (LLSM), generate vast and complex datasets that demand advanced analytical approaches to uncover biologically meaningful insights. While LLSM’s high spatial and temporal resolution provides critical data for understanding cellular processes, distinguishing subtle differences between cells in distinct states remains challenging. Here, using an adaptive, human-interpretable kernel-based calculation strategy, we developed Machine-Learning-Based Visual Extraction of Structural Features (M-VEST), a method designed to identify and interpret structural differences with high precision. By applying M-VEST to mitotic cells, we uncovered novel functions of the oncogene Aurora kinase A, demonstrating its utility in revealing previously undetected features. Validated using LLSM datasets, M-VEST offers a scalable framework for analyzing large and complex imaging data, advancing insights into cellular dynamics and beyond.
Quantifying the impact of genetic determinants of antibiotic resistance on bacterial lineage dynamics
Authors: Helekal David , Mortimer Tatum D. , Mukherjee Aditi , Palace Samantha G. , Grad Yonatan H. 
Published: February 2025

ABSTRACTThe dynamics of antimicrobial resistance in bacteria are informed by the fitness advantages conferred by genetic determinants of resistance in the presence of antibiotic pressure and the potential fitness costs in its absence. However, frameworks for quantitative estimates of real-world fitness impact have been lacking, given multidrug resistance, multiple pathways to resistance, and uncertainty around drug exposures. Here, we addressed these challenges through analysis of genome sequences from clinical isolates ofNeisseria gonorrhoeaecollected over 20 years from across the United States, together with national data on antibiotic treatment. Using a hierarchical Bayesian phylodynamic model, we quantified the contributions of resistance determinants to strain success. Resistance mutations had a fitness benefit when the cognate antibiotic was in use but did not always incur a fitness cost otherwise. Two fluoroquinolone-resistance conferring mutations at the same site ingyrAhad divergent fitness impact after fluoroquinolones were no longer used for treatment, findings supported byin vitrocompetition experiments. Fitness costs were alleviated by loss of costly resistance determinants and counterbalanced by gain of new fitness-conferring resistance determinants. Quantifying the extent to which the resistance determinants explained each lineage’s dynamics highlighted gaps and pointed to opportunities for investigation into other genetic and environmental drivers. This work thus establishes a method for linking pathogen genomics and antibiotic use patterns to quantify the fitness impact of resistance determinants and the factors shaping ecological trends.