bluuebunny/crossref_metadata_2025_split huggingface dataset:
Complementary Volume Electron Microscopy-based approaches reveal ultrastructural changes in germline intercellular bridges ofD. melanogaster
Authors: Kolotuev Irina , Williams Abigayle , Kizilyaprak Caroline , Pellegrino Stephanie , Lewellyn Lindsay 
Published: February 2025

ABSTRACTIntercellular bridges are essential to connect developing germline cells. TheDrosophila melanogasteregg chamber is a powerful model system to study germline intercellular bridges, or ring canals (RCs). RCs connect the developing oocyte to supporting nurse cells, and defects in their stability or growth lead to infertility. Despite their importance, it has been technically difficult to use electron microscopy-based approaches to monitor changes in RC structure during oogenesis. Here, we describe the application of a complementary set of volume EM-based approaches to visualize ultrastructural changes in the germline RCs. The combination of array tomography (AT) and focused ion beam (FIB) scanning electron microscopy (SEM) has allowed us to gain insight into previously unappreciated aspects of RC structure. We were able to quantify differences in RC size and thickness within and between germ cell clusters at different developmental stages. Within a cluster, RC size correlates with lineage; the largest RCs were formed during the first division, and the smallest RCs were formed during the fourth mitotic division. We observed the formation of membrane interdigitations in the vicinity of RCs much earlier than previously reported, and reconstruction of a RC from a mid-stage EC provided insight into the 3D orientation of these extensive cell-cell contacts. Our imaging also revealed a novel membrane structure that appeared to line the interior of the RC lumen. Although the focus was on ultrastructural changes in the germline RCs, our dataset contains valuable details of additional cell types and structures, including the fusome, the germline stem cells and their niche, and the migrating border cells. This imaging framework could be applied to other tissues or samples that face similar technical challenges, where the small structure of interest is located within a large sample volume.
Population-scale gene expression analysis reveals the contribution of expression diversity to the modern wheat improvement
Authors: Zhang Zhimeng , Ma Shengwei , Yin Mou , Zhao Caihong , Zhao Xinyu , Yu Yang , Wang Haojie , Li Xuanzhao , Si Yaoqi , Niu Jianqing , Xie Jingzhong , Wang Limin , Wu Jiajie , Zhang Yanming , Zheng Qi , Zheng Shusong , Jiang Ni , Liu Xigang , Ling Hong-Qing , He Fei 
Published: February 2025

AbstractChanges in gene expression are crucial for crop breeding, yet population genomics has primarily focused on sequence polymorphisms rather than gene expression diversity. The strategy of using single genome reference for RNA-seq analysis could not handle introgression bias, especially for hexaploidy wheat. Here, we conducted RNA-seq for 328 wheat lines, including representative diverse landraces and elite cultivars from China and the United States, to investigate the role of gene expression variation in shaping agronomic traits. Using pan-genome resources, we identified 20,615 more transcripts than using the ‘Chinese Spring’ reference genome alone. We constructed a pan-gene atlas regulatory map through eQTL analysis, demonstrating that genes introgressed from wild relatives were under tight genetic control. Genes responding to environmental stress show higher activity after introgressed into the wheat genome, demonstrating how long-term breeding selection impacted the gene expression regulation of targeted introgression. Multi-omics modeling identified 231 high-confidence candidate genes for 34 field agronomic traits and the seedling resistance phenotypes of 8 powdery mildew isolates. More than one fifth of those candidates have no homolog in ‘Chinese Spring’ reference genome. By utilizing the indexed KN9204 EMS library, 80% candidates showed significant trait difference between wild type and mutant lines. Furthermore, directional shifts in genes of which expression were changed by breeding improvement demonstrated distinct adaptations to local environments. Our study constructed a pan-gene atlas to correct the reference bias of reads mapping in RNA-seq studies and revealed the expression patterns of introgressed genes within the wheat genome and their regulatory mechanisms, which highlighted the impact of breeding selection on gene expression of the world’s most important crop.
Label Noise in Pathological Segmentation is Overlooked, Leading to Potential Overestimation of Artificial Intelligence Models
Authors: Harada Kenji , Nomura Yuichiro , Komura Daisuke , Ishikawa Shumpei , Sakashita Shingo 
Published: February 2025

AbstractArtificial intelligence (AI) has transformed medical imaging, driving advancements in radiology and endoscopy. Semantic segmentation, a pixel-level technique crucial for delineating pathological features, has become a cornerstone of digital pathology. Pathology segmentation AI models are often trained using annotations generated by pathologists. Despite the meticulous care typically exercised, these annotations frequently contain empirical label noise. However, the specific types of label noise in pathology data and their impact on AI model training remain inadequately explored. This study systematically investigated the effects of label noise on the performance of pathology segmentation models. Using publicly available datasets and a breast cancer semantic segmentation dataset, modules were developed to simulate four types of artificial label noise at varying intensity levels. These datasets were used to train deep learning models with encoder-decoder architectures, and their performance was evaluated using metrics such as the Dice coefficient, precision, recall, and intersection over union. The results indicated that models were highly susceptible to overfitting label noise, particularly boundary-dependent noise such as dilation and shrinkage. Discrepancies were identified between apparent performance scores obtained under real-world conditions and true performance scores derived using clean test data. This overestimation risk was most pronounced for datasets containing boundary-altering noise. Furthermore, random combinations of noise types and levels significantly impaired model generalization. This study underscores the critical importance of addressing label noise in pathology datasets. It is proposed that future efforts focus on developing standardized methods for quantifying and mitigating label noise, along with creating robust benchmarks using noise-inclusive datasets. Enhancing annotation quality and addressing label noise can improve the reliability and generalizability of AI in pathology, facilitating broader clinical adoption.
Decoding Host-Pathogen Interactions inStaphylococcus aureus: Insights into Allelic Variation and Antimicrobial Resistance Prediction Using Artificial Intelligence and Machine Learning based approaches
Authors: Ghosh Joyeta , Taneja Jyoti , Kant Ravi 
Published: February 2025

AbstractThis novel study leveraged advanced machine learning techniques to elucidate the molecular mechanisms of antimicrobial resistance (AMR) in 300Staphylococcus aureusisolates across six critical antibiotics. Employing a diverse array of deep learning and ensemble models, we conducted an in-depth analysis of genetic markers and allelic variations to characterize resistance determinants. Our investigation revealed that the XGBoost ensemble model demonstrated the most exceptional performance, achieving a remarkable 95% test accuracy, 100% training accuracy, and an unprecedented ROC AUC of 0.9855.Comparative analysis of multiple machine learning approaches, including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), Decision Tree, and Stochastic Gradient Descent (SGD) models, provided detailed insights into resistance prediction. The SHAP (SHapley Additive exPlanations) analysis unveiled critical genetic markers, with “cat_allele_Cluster_1015_Allele_8” emerging as the most influential feature driving resistance predictions. Notably, the models exhibited varying performance across different antibiotics, with consistently high accuracy and F1-scores for ciprofloxacin, clindamycin, gentamicin, and sulfamethoxazole/trimethoprim.Our findings not only demonstrate the potential of advanced machine learning techniques in predicting antimicrobial resistance but also provide crucial insights into the molecular mechanisms underlyingS. aureusdrug resistance. By identifying key genetic determinants and their relative importance, this study offers a sophisticated approach to understanding resistance patterns, potentially guiding future diagnostic strategies, targeted therapies, and antimicrobial stewardship practices in clinical settings.
Lancet2: Improved and accelerated somatic variant calling with joint multi-sample local assembly graphs
Authors: Musunuri Rajeeva Lochan , Zhu Bryan , Clarke Wayne E. , Hooper William , Chu Timothy , Shelton Jennifer , Corvelo André , Chung Dickson , Sundar Shreya , Novak Adam M , Paten Benedict , Zody Michael C. , Robine Nicolas , Narzisi Giuseppe 
Published: February 2025

AbstractHere, we present Lancet2, an open-source somatic variant caller designed to improve detection of small variants in short-read sequencing data. Lancet2 introduces significant enhancements, including: 1) Improved variant discovery and genotyping through partial order multiple sequence alignment of assembled haplotype contigs and re-alignment of sample reads to the best supporting allele. 2) Optimized somatic variant scoring with Explainable Machine Learning models leading to better somatic filtering throughout the sensitivity scale. 3) Integration with Sequence Tube Map for enhanced visualization of variants with aligned sample reads in graph space. When benchmarked against enhanced two-tech truth sets generated using high-coverage short-read (Illumina) and long-read (Oxford Nanopore) data from four well characterized matched tumor/normal cell lines, Lancet2 outperformed other industry-leading tools in variant calling performance, especially for InDels. In addition, significant runtime performance improvements compared to Lancet1 (∼10x speedup and 50% less peak memory usage) and most other state of the art somatic variant callers (at least 2x speedup with 8 cores or more) make Lancet2 an ideal tool for accurate and efficient somatic variant calling.
Odor-evoked respiratory responses throughout development in sighted and blind mice
Authors: Bouguiyoud N. , Litaudon P. , Frasnelli J. , Garcia S. , Messaoudi B. , Mouly AM. , Al Aïn S. , Courtiol E. 
Published: February 2025

AbstractCongenital blindness affects olfactory function depending on developmental stage. However, when studying the ontogeny of olfactory abilities, not all behaviors are expressed at every age making the longitudinal comparisons difficult. Odor-evoked respiratory responses, which are unlearned and do not require complex motor coordination, may serve as sensitive measures of olfactory abilities throughout ontogeny. Using a non-invasive measure of respiration in an olfactory perceptual paradigm, we assessed odor-evoked respiratory responses in a model of congenital blindness at 3 ages, infant, juvenile and adult, in the same mice and in both males and females. We demonstrated the differential outcome of two respiratory parameters (i.e. frequency and amplitude) in a mouse model of congenital blindness. We showed that blind mice have similar olfactory abilities than sighted mice throughout ontogeny but display enhanced sniffing frequency and amplitude, starting at the juvenile age for the latter one, that may help them better explore their environment. We also demonstrated that respiratory frequency is a robust index of age and of olfactory detection, habituation and discrimination at all ages. On the other side, respiratory amplitude does not provide a proxy of olfactory performance at all ages, however, it does highlight differences between sexes and phenotypes associated with visual deprivation. To conclude, our data highlight that respiratory parameters can be used as a complementary approach to assess olfactory performance throughout development and provide an index of olfactory plasticity.
PHYTOCHROME INTERACTING FACTORS are required to coordinate microtubule dynamics and differential cell growth during Arabidopsis apical hook opening
Authors: Burachik Natalia , Vacs Paula , Franco Santin , Mazzella María Agustina , González-Schain Nahuel 
Published: February 2025

AbstractBackgroundDifferential cell growth is a key strategy that enables plants to adjust their morphology in response to internal and external cues. Apical hook opening in dark- grown Arabidopsis seedlings serves as an excellent model to study how coordinated cell expansion generates organ bending. Several works highlight the influence of hormone signaling, cell wall mechanics, and environmental cues on cortical microtubule (cMT) arrangement in bending tissues. However, the precise genetic hierarchy governing these processes remains largely unknown.ResultsHere, we reveal a two-step growth rate during apical hook opening, in which an initial phase of differential cell expansion is followed by a phase of more balanced growth. We show that cMT re-arrangement precedes and predicts cell expansion patterns, suggesting a central role in coordinating differential growth. Our findings indicate that PIF transcription factors regulate this process, aspifqmutants fail to align cMT reorganization with growth dynamics, resulting in premature hook opening. RNA- seq analysis further supports a role for PIFs in coordinating cell wall remodeling and microtubule-associated gene expression, including kinesins and cell wall-modifying enzymes.ConclusionsOur study uncovers a PIF-dependent regulatory mechanism that orchestrates cytoskeletal dynamics and cell expansion to control apical hook opening.
Conservation and discovery of regulatory motifs across oomycetes through comparative genomic analysis
Authors: Bharti Sakshi , Thines Marco 
Published: February 2025

AbstractPromoter sequences contain specific transcription factor (TFs) binding sites that regulate gene expression. While the conservation of TFs in pathogen development and infection among oomycetes is known, little is understood about TFs bind to conserved promoter regions across species. This study employs a robust comparative computational genomics approach to identify the TFs binding to orthologous DNA motifs in oomycetes. By integrating high-confidence TF binding site (TFBS) profiles,in-silicomotif discovery, sequence conservation analysis and protein sequence similarity searches, the study revealed conserved regulatory mechanisms in oomycetes. The multi-layered computational framework identified two major TF classes in oomycetes: Cys2-His2 (C2H2) zinc finger proteins and winged helix repressor proteins, binding to orthologous motifs regulating gene clusters involved in epigenetic regulation, effectors, intracellular trafficking, host cell wall degrading enzymes, RNA processing and cytoskeletal organization. Structural comparisons indicate high sequence similarity between oomycete TFs and well-characterized eukaryotic TFs, supporting the predictive power of the computational approach. Moreover, motif plasticity analysis across developmental phases revealed conserved and phase-specific motifs emphasizing dynamic transcriptional regulation during infection and colonization. The presence of highly conserved motifs across multiple oomycete species suggests strong evolutionary selection pressure on key regulatory elements. The results provide a computational foundation for future experimental validation, guiding functional characterization of transcriptional regulation in oomycetes. This study highlights the potential ofin-silicoTFBS discovery for understanding gene regulation, paving the way for targeted experimental approaches such as ChIP-seq or electrophoretic mobility shift assays (EMSA).
Collisional cross-section prediction for multiconformational peptide ions with IM2Deep
Authors: Devreese Robbe , Nameni Alireza , Declercq Arthur , Terryn Emmy , Gabriels Ralf , Impens Francis , Gevaert Kris , Martens Lennart , Bouwmeester Robbin 
Published: February 2025

AbstractPeptide collisional cross-section (CCS) prediction is complicated by the tendency of peptide ions to exhibit multiple conformations in the gas phase. This adds further complexity to downstream analysis of proteomics data, for example for identification or quantification through feature finding. Here, we present an improved version of IM2Deep that is trained on a carefully curated dataset to predict CCS values of multiconformational peptides. The training data is derived from a large and comprehensive set of publicly available datasets. This comprehensive training dataset together with a tailored architecture allows for the accurate CCS prediction of multiple peptide conformational states. Furthermore, the enhanced IM2Deep model also retains high precision for peptides with a single observed conformation. IM2Deep is publicly available under a permissive open source license athttps://github.com/compomics/IM2Deep.
The geometry of pMHC-coated nanoparticles and T cell receptor clusters governs the sensitivity-specificity trade-off in T cell response
Authors: Richez Louis , Khadra Anmar 
Published: February 2025

AbstractT cells must reliably discriminate between foreign-derived antigens that require an adaptive immune response from non-specific self-antigens that do not. This discrimination is highly specific to the affinity of the bond between ligand and T cell receptors (TCRs), as well as highly sensitive to the concentration of ligand. In this study, we examined these features of T cell mediated immunity in the context of multivalent ligand-receptor interactions between clusters of TCRs with pMHC-coated nanoparticles (NPs). Using Monte Carlo simulations of NP-T cell surface interactions, we compared the effect of TCR clustering on the dose-response profiles of various NP designs. These simulations revealed a trade-off between sensitivity and specificity, mediated by the spatial clustering of TCRs and the geometry of the NP. In particular, large clusters of TCRs were more sensitive to both NP valence and ligand concentration at the expense of antigen specificity. Conversely, uniformly distributed TCR landscapes were better suited to affinity-based ligand discrimination, while sacrificing sensitivity to ligand concentration. These features of NP-mediated T cell activation depended significantly on NP size and valence rather than on the average ligand concentration. Furthermore, we demonstrated how kinetic proofreading mechanisms may help compensate for the limitations associated with TCR clustering. These findings thus highlight the importance of interacting geometries of NP design and TCR landscape in modulating the specificity and sensitivity of the T cell response.SignificanceT cells rely on surface T cell receptors (TCRs) to recognize foreign antigens presented as peptide-major histocompatibility complex (pMHC) molecules. TCR clustering is crucial for T cell activation, though its full role remains not entirely clear. Using Monte Carlo simulations, we demonstrate that TCR clustering profoundly influences both surface binding dynamics of multivalent pMHC-coated nanoparticles, used in autoimmune disease therapies, as well as downstream intracellular signals leading to T cell activation. Our findings thus provide important insights into the role of interaction geometries in shaping T cell response, with implications for optimizing nanoparticle design to enhance their therapeutic efficacy.
Dynamic Lipidome Reorganization in Response to Heat Shock Stress
Authors: Solano Luis , Keshet Uri , Reinschmidt Andrew , Chavez Yonny , Hulsy William Drew , Fiehn Oliver , Nikolaidis Nikolas 
Published: February 2025

AbstractThe heat shock response (HSR) is a conserved cellular mechanism critical for adaptation to environmental and physiological stressors, with broad implications for cell survival, immune responses, and cancer biology. While the HSR has been extensively studied at the proteomic and transcriptomic levels, the role of lipid metabolism and membrane reorganization remains underexplored. Here, we integrate mass spectrometry-based lipidomics with RNA sequencing to characterize global lipidomic and transcriptomic changes in HeLa cells exposed to three conditions: control, heat shock (HS), and HS with eight hours of recovery. Heat shock-induced extensive lipid remodeling, including significant increases in fatty acids, glycerophospholipids, and sphingolipids, with partial normalization during recovery. Transcriptomic analysis identified over 2,700 upregulated and 2,300 downregulated genes under heat shock, with GO enrichment suggesting potential transcriptional contributions to lipid metabolism. However, transcriptional changes alone did not fully explain the observed lipidomic shifts, suggesting additional layers of regulation. Joint pathway analysis revealed enrichment in glycerophospholipid and sphingolipid metabolism, while network analysis identified lipid transport regulators (STAB2, APOB), stress-linked metabolic nodes (KNG1), and persistent sphingolipid enrichment during recovery. These findings provide a comprehensive framework for understanding lipid-mediated mechanisms of the HSR and highlight the importance of multi-omics integration in stress adaptation and disease biology.
EPIraction - an atlas of candidate enhancer-gene interactions in human tissues and cell lines
Authors: Nurtdinov Ramil N. , Guigó Roderic 
Published: February 2025

AbstractEnhancer-gene maps are essential to understand gene regulation. The identification of enhancers contributing to regulating a given gene, however, is challenging as they may lie far from the gene and act in a tissue-specific manner. Here we present EPIraction, an enhancer-gene atlas in the human genome. As H3K27ac is a typical mark of enhancer function, EPIraction relies on an exhaustive collection of 1,538 H3K27ac ChIP-Seq datasets. We used these data together with other epigenomic data to measure enhancer activity. To predict enhancer-gene interactions we first applied the conventional Activity By Contact algorithm to score enhancer-gene pairs in individual tissues. Then, we re-scored the interaction rewarding those present in biologically similar tissues. We used our predictions to annotate the candidate target genes for 1,664,956 SNPs from 3,693 UK Biobank and 760 FinnGen GWAS traits. The EPIraction predictions can be accessed through the portal athttps://epiraction.crg.es. The portal can be employed to produce annotations for user-provided lists of SNPs or genomic intervals.
Chronic sleep curtailment expediates brain aging by activating the complement and coagulation cascades in mice
Authors: Jha Pawan K. , Valekunja Utham K. , Reddy Akhilesh B. 
Published: February 2025

ABSTRACTChronic sleep insufficiency is prevalent in modern society and has been associated with age-related neurodegenerative diseases. Loss of sleep accelerates the progression of neurodegeneration in animal models of neurological diseases. Here, we study whether chronic sleep curtailment leads to brain aging in wild-type animals without a genetic predisposition. We used a wild-type mouse model to simulate modern-day conditions of restricted sleep and compared the brain (cortex) proteome of young sleep-restricted animals with different aged control groups. We report the alteration of 149 proteins related to sleep and 1269 related to age with 96 proteins common between them. Through pathway analysis of proteins common to sleep restriction and aging, we discovered that the complement and coagulation cascade pathways were enriched by alterations of complement component 3 (C3), alpha-2-macroglobulin (A2M), fibrinogen alfa and beta chain (FGA and FGB). This is the first study indicating the possible role of the complement and coagulation pathways in brain aging and by chronic sleep restriction (CSR) in mice.