bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nucleotide GPT: Sequence-Based Deep Learning Prediction of Nuclear Subcompartment-Associated Genome Architecture
Authors: Mclaughlin Shae M. , Ahanger Sajad H. , Lim Daniel A. 
Published: December 2024

AbstractThe spatial organization of the genome within the nucleus is partially determined by its interactions with distinct nuclear subcompartments, such as the nuclear lamina and nuclear speckles, which play key roles in gene regulation during development. However, whether these genome-nuclear subcompartment interactions are encoded in the underlying DNA sequence remains poorly understood. The mechanisms for gene regulation are primarily encoded in noncoding DNA sequences, but deciphering how these sequence features control gene expression remains a significant challenge in genomics. Here, we present Nucleotide GPT, a transformer-based model that predicts genomic associations with spatially distinct, physical nuclear subcompartments from DNA sequence alone. Pre-trained on a diverse set of multi-species genomes, we demonstrate Nucleotide GPT’s genomic understanding through evaluation on diverse prediction tasks, including histone modifications, promoter detection, and transcription factor binding sites. When finetuned to predict genome interactions with two separate nuclear subcompartments – the lamina of the inner nuclear membrane and nuclear speckles that lie more interior – Nucleotide GPT achieves an average accuracy of 73.6% for lamina-associated domains (LADs) and 79.4% accuracy for speckle-associated domains (SPADs), averaged across three cortical development cell types. Analysis of the model’s learned representations through Uniform Manifold Approximation and Projection (UMAP) reveals that Nucleotide GPT develops internal embeddings that effectively distinguish LADs from inter-LADs, with predicted probabilities closely corresponding to experimentally determined LAD classifications. When examining these representations in the context of cell type-invariant constitutive LADs (cLADs) compared to cell type-specific LADs, the model assigns lower confidence scores to cell type-specific LADs compared to cLADs that are conserved across neuronal differentiation, suggesting sequence features may play a stronger role in maintaining cLAD associations. Examination of the model’s attention patterns at correctly classified regions suggests that specific sequence elements govern model decision making about nuclear subcompartment associations. Our results demonstrate the utility of transformer architectures for studying three-dimensional (3D) genome organization and substantiate a role for DNA sequence in determining nuclear subcompartment associations.
Two distinct durable human class-switched memory B cell populations are induced by vaccination and infection
Authors: Perugino Cory A. , Liu Hang , Feldman Jared , Marbourg Jess , Guy Thomas V. , Hui Anson , Ingram Nicole , Liebaert Julian , Chaudhary Neha , Tao Weiyang , Jacob-Dolan Catherine , Hauser Blake M. , Mian Zayd , Nathan Anusha , Zhao Zezhou , Kaseke Clarety , Tano-Menka Rhoda , Getz Matthew A. , Senjobe Fernando , Berrios Cristhian , Ofoman Onosereme , Manickas-Hill Zachary , Wesemann Duane R. , Lemieux Jacob E. , Goldberg Marcia B. , Nündel Kerstin , Moormann Ann , Marshak-Rothstein Ann , Larocque Regina C. , Ryan Edward T. , Iafrate John A. , Lingwood Daniel , Gaiha Gaurav , Charles Richelle , Balazs Alejandro B. , Pandit Aridaman , Naranbhai Vivek , Schmidt Aaaron G. , Pillai Shiv 
Published: December 2024

AbstractMemory lymphocytes are durable cells that persist in the absence of antigen, but few human B cell subsets have been characterized in terms of durability. The relative durability of eight non-overlapping human B cell sub-populations covering 100% of all human class-switched B cells was interrogated. Only two long-lived B cell populations persisted in the relative absence of antigen. In addition to canonical germinal center-derived switched-memory B cells with an IgD-CD27+CXCR5+phenotype, a second, non-canonical, but distinct memory population of IgD-CD27-CXCR5+DN1 B cells was also durable, exhibited a uniqueTP63-linked transcriptional and anti-apoptotic signature, had low levels of somatic hypermutation, but was more clonally expanded than canonical switched-memory B cells. DN1 B cells likely evolved to preserve immunological breadth and may represent the human counterparts of rodent extrafollicular memory B cells that, unlike canonical memory B cells, can enter germinal centers and facilitate B cell and antibody evolution.Graphical Abstract
Galectin-9 binding to HLA-DR in dendritic cells controls immune synapse formation and T cell proliferation
Authors: Rodgers Furones Andrea , Brands Thijs , Fedorova Kristina , Wijfjes Zacharias , Classens René , Kroese Lona , Verdoes Martijn , van Mierlo Guido , van Spriel Annemiek B , Querol Cano Laia 
Published: December 2024

AbstractTo initiate T cell-mediated immunity, dendritic cells (DCs) present antigens to specific T cells through the establishment of an immune synapse (IS). While the molecular mechanisms behind the formation of the IS on the T cell side are well understood, how IS components are organized at the DC membrane remain ill-defined. Galectins, a family of β-galactoside binding proteins, modulate immune cell function via the establishment of specific glycan-dependent or independent interactions. Nonetheless, the molecular mechanisms that underlie galectin function are poorly described and very little is known regarding their contribution to DC-mediated T cell activation. Here, we demonstrate that intracellular galectin-9 (gal9) in DCs is required for T cell activation. Murine and human DCs lacking gal9 showed impaired induction of CD4+, but not CD8+, T cell proliferation, suggesting a conserved function for gal9 in modulating DC–T cell interactions. Live-cell imaging revealed that galectin-9-depleted DCs fail to establish stable ISs with T cells, resulting in reduced T cell activation and proliferation. Unbiased co-immunoprecipitation and mass spectrometry identified HLA-II as a gal9 binding partner in DCs, and we observed a marked reduction of HLA-II recruitment to the immune synapse in DCs lacking gal9. Conditional gal9 knockout in DCs led to enhanced tumor growthin vivo, compared to their wild-type (WT) counterparts, underscoring a role for gal9 in T cell-dependent anti-tumor immunity. Collectively, this study provides the first detailed account of gal9-mediated HLA-II organization at the synaptic site of DCs, revealing a novel mechanism by which galectins orchestrate immune receptor positioning from within the cytoplasm to enhance CD4+T cell activation.
Single cell transcriptomics reveal mast cell heterogeneity in the human gut
Authors: Hussein Hind , Modave Elodie , Stakenborg Nathalie , Delfini Marcello , Vandereyken Katy , Sifrim Alejandro , Voet Thierry , Boeckxstaens Guy E. 
Published: December 2024

AbstractGut mast cells are key players in infection and inflammation, as well as in homeostasis. Mast cells regulate gastrointestinal (GI) function by controlling vascular and epithelial permeability, and interacting with the immune system and the enteric nervous system. Mucosal (MCT) and connective tissue (MCTC) mast cells coexist in the gastrointestinal tract, but are located in distinct microanatomical niches. However, little is known about the transcriptional heterogeneity of human intestinal mast cells. Additionally, whether distinct microanatomical niches instruct mast cell phenotypes in the human gut is currently unknown. We therefore performed 10x single cell RNA sequencing on healthy rectal biopsies and on dissected layers of human sigmoid colon. We identified five transcriptionally distinct mast cell subsets (MC1-5) which exhibited a layer-specific distribution in the healthy human colon and distinct cytokine, chemokine, protease, and transcription factor profiles, and were associated with putative immune and neuro-immune functions. This study provides a framework for the integration of mast cell heterogeneity in the study of gastrointestinal disease mechanisms in humans.
Global patterns of species diversity and distribution in the biomedically and biotechnologically important fungal genusAspergillus
Authors: Riedling Olivia L. , David Kyle T. , Rokas Antonis 
Published: December 2024

AbstractAspergillusfungi are key producers of pharmaceuticals, enzymes, and food products and exhibit diverse lifestyles, ranging from saprophytes to opportunistic pathogens. To improve understanding ofAspergillusspecies diversity, identify key environmental factors influencing their geographic distributions, and estimate the impact of future climate change, we trained a random forest machine learning classifier on 30,542 terrestrial occurrence records for 176 species (∼40% of known species in the genus) and 96 environmental variables. We found that regions with high species diversity are concentrated in temperate forests, which suggests that areas with mild seasonal variation may serve as diversity hotspots. Species range estimates revealed extensive variability, both within and across taxonomic sections; while some species are cosmopolitan, others have more restricted ranges. Furthermore, range overlap between species is generally low. The top predictors of mean species richness were the index of cumulative human impact and five bioclimatic factors, such as temperature and temperate vs non-temperate ecoregions. Our future climate analyses revealed considerable variation in species range estimates in response to changing climates; some species ranges are predicted to expand (e.g., the food spoilage and mycotoxin-producingAspergillus versicolor), and others are predicted to contract or remain stable. Notably, the predicted range of the major pathogenAspergillus fumigatuswas predicted to decrease in response to climate change, whereas the range of the major pathogenAspergillus flavuswas predicted to increase and gradually decrease. Our findings reveal how both natural and human factors influenceAspergillusspecies ranges and highlight their ecological diversity, including the diversity of their responses to changing climates, which is of relevance to pathogen and mycotoxin risk assessment.
Disentangling cell type and state transcriptional programs
Authors: Wang Jiayi , Crowell Helena L. , Robinson Mark D. 
Published: December 2024

AbstractSingle-cell omics approaches profile molecular constituents of individual cells. Replicated multi-condition experiments in particular aim at studying how the molecular makeup and composition of cell subpopulations changes at the sample-level. Two main approaches have been proposed for these tasks: firstly, cluster-based methods that group cells into (non-overlapping) subpopulations based on their molecular profiles and, secondly, cluster-free but neighborhood-based methods that identify (overlapping) groups of cells in consideration of cross-condition changes. In either approach, discrete cell groups are subjected to differential testing across conditions; and, a low-dimensional cell embedding, which is in turn derived from a subset of selected features, is required to delineate subpopulations or neighborhoods.We hypothesized that decoupling differences in cell type (i.e., between subpopulations) and cell state (i.e., between conditions) for feature selection would yield an embedding space that captures different aspects of cellular heterogeneity. And, that type-not-state embeddings would arrive at differential testing results that are comparable between clusterand neighborhood-based differential testing approaches. Our study leverages a simulation framework with competing type and state effects, as well as an experimental dataset, to evaluate a set of feature scoring and selection strategies, and to compare results from downstream differential analyses.
Accurate sample deconvolution of pooled snRNA-seq using sex-dependent gene expression patterns
Authors: Twa Guy M. , Phillips Robert A. , Robinson Nathaniel J. , Day Jeremy J. 
Published: December 2024

SummarySingle nucleus RNA sequencing (snRNA-seq) technology offers unprecedented resolution for studying cell type-specific gene expression patterns. However, snRNA-seq poses high costs and technical limitations, often requiring the pooling of independent biological samples and loss of individual sample-level data. Deconvolution of sample identity using inherent features would enable the incorporation of pooled barcoding and sequencing protocols, thereby increasing data throughput and analytical sample size without requiring increases in experimental sample size and sequencing costs. In this study, we demonstrate a proof of concept that sex-dependent gene expression patterns can be leveraged for the deconvolution of pooled snRNA-seq data. Using previously published snRNA-seq data from the rat ventral tegmental area, we trained a range of machine learning models to classify cell sex using genes differentially expressed in cells from male and female rats. Models that used sex-dependent gene expression predicted cell sex with high accuracy (90-92%) and outperformed simple classification models using only sex chromosome gene expression (69-89%). The generalizability of these models to other brain regions was assessed using an additional published data set from the rat nucleus accumbens. Within this data set, model performance remained highly accurate in cell sex classification (89-90% accuracy) with no additional re-training. This work provides a model for future snRNA-seq studies to perform sample deconvolution using a two-sex pooled sample sequencing design and benchmarks the performance of various machine learning approaches to deconvolve sample identification from inherent sample features.
DNA methylation and gene expression trajectories of human postprandial metabolism
Authors: Costeira Ricardo , Ruiz Lidia Daimiel , Gehrmann Thies , Bogaards Fatih , Villicaña Sergio , Sinke Lucy , Raza Yasrab , Tomlinson Max , Christiansen Colette , Heijmans Bastiaan T , Slagboom P Eline , Spector Tim D , Small Kerrin S , Alcala-Diaz Juan F , Rangel-Zuñiga Oriol , López-Miranda Jose , Waldenberger Melanie , Berry Sarah E , Ordovás José M , Bell Jordana T 
Published: December 2024

AbstractHuman postprandial metabolism is characterised by a highly individualised response to food that is predictive of cardiometabolic health and underexplored at the molecular level. We profiled blood DNA methylation (DNAm) and gene expression trajectories before and after a test meal in 225 European participants. We identify DNAm changes at fasting, 30 minutes and 4 hours after meal challenge, including in metabolically relevant genesINPP4A, GHRL, ASIPandABCG1, with changes observed as early as 30mins postprandially. Gene expression trajectories also changed postprandially predominantly at 4 hours, with replication of lipid metabolism (CPT1A)and circadian rhythm (PER1)genes. Genetic variants affect postprandial molecular trajectories at genes linked to obesity (PDE9A)and glucose response (GPT2). Multiple signals associated with postprandial glucose and triglyceride levels, with replication ofCPT1Amethylation. The postprandial DNAm and expression trajectories target metabolically relevant genes, giving insights towards mechanisms underlying inter-individual response to food and cardiometabolic disease risk.
Enhanced data collection in the Canadian Arctic for seabird bycatch information yields highly variable results
Authors: Provencher Jennifer F. , Morrill André , Mallory Mark L. 
Published: December 2024

AbstractIncidental catch of seabirds (bycatch) in fisheries has been identified as a major threat to the conservation of seabird populations globally. Acquiring accurate, detailed data on seabird bycatch is an ongoing challenge to effective integrated ecosystem management of commercial fisheries. This is especially true in the Arctic region where different countries have highly variable reporting and data systems to track and understand seabird bycatch in fisheries. To collect detailed data on seabird bycatch in the Greenland halibut (Reinhardtius hippoglossoides) fishery in northern Canada, we applied two methods that asked for more information than standardly reported in the fishery as a voluntary effort with industry partners. We found that the amount of bird bycatch reported in both enhanced datasheets completed by at-sea observers (ASO) and carcass collections yielded different results when compared to the typical seabird bycatch reporting in the fisheries ASO database. Across three years of data collection (2016, 2018, and 2019), the number of seabirds reported using the enhanced data collection methods were 0.5-11-fold the number from typical ASO database values. In a fourth year of observations (2023), enhanced datasheets from the ASO reported no bycaught fulmars (gulls and terns were reported), but the accompanying photographs showed northern fulmars as bycatch, further supporting that seabird identification is hampering accurate reporting. We then used these data to model how the differences between data sources may fluctuate across years. These large discrepancies between the methods highlight the challenges with obtaining accurate and precise seabird bycatch data needed to implement a meaningful ecosystem approach to the management of the fisheries. Future modelling efforts need to take these differing data sources and variability into account to fully understand the potential population level impacts of fisheries on seabird populations.
Wideband Tuning and Deep-Tissue Spectral Detection of Indium Phosphide Nano-Laser Particles
Authors: Cho Sangyeon , Moon Wonjoon , Martino Nicola , Yun Seok Hyun 
Published: December 2024

AbstractLaser particles (LPs) emitting narrowband spectra across wide spectral ranges are highly promising for high-multiplex optical barcoding. Here, we present LPs based on indium phosphide (InP) nanodisks, operating in the near-infrared wavelength range of 740-970 nm. Utilizing low-order whispering gallery resonance modes in size-tuned nanodisks, we achieved an ultrawide color palette with 27% bandwidth utilization and nanometer-scale linewidth. The minimum laser size was 430 nm in air and 560 nm within the cytoplasm, operating at mode order 4 or 5. We further demonstrated spectral detection of laser peaks with high signal-to-background ratios in highly-scattering media, including 1-cm-thick chicken breast tissue and blood vessels in live mice.
Emotional content effects the precision of visual working memory
Authors: Haghian Zeinab , Vahabie Abdol-Hossein , Rezayat Ehsan 
Published: December 2024

AbstractObjectivesOur working memory (WM) is susceptible to errors influenced by various sources. Recent research has illuminated the intricate relationship between emotional valence and working memory performance. This study aims to comprehensively investigate memory recall biases across different emotional categories.MethodTo explore this relationship, we designed and implemented a delayed-reproduction facial emotion n-back task. Participants were tasked with recalling the emotional valence of target faces across various n-back conditions, selecting their responses from a continuum of 19 morphed faces ranging from sad to happy.ResultsOur findings indicate that participants generally exhibit a negativity bias, struggling more with happy faces. Interestingly, they also perceive happy faces as less happy and sad faces as less sad, suggesting both positive and negative reappraisal in their emotional valence perception. This underscores the complex interplay between emotional valence and cognitive performance. Notably, recall of neutral images remained stable and was unaffected by preceding emotional contexts.ConclusionThese findings demonstrate that emotional content in working memory significantly impacts errors during WM tasks, with more emotionally charged faces leading to a greater drift toward the lower valence axis. This highlights the need for further exploration of how emotional factors influence cognitive processes in working memory.
A gene co-expression network-based analysis of mesenchymal stromal cells reveals novel genes and molecular pathways underlying heterotopic ossification
Authors: Yang Meiling , Shi Yongpeng , Shen Shichao , Yan Bin , Yang Lei , Li Liuxun 
Published: December 2024

AbstractBackgroundHeterotopic ossification (HO) represents a frequently seen refractory disease second to musculoskeletal injury, in which bone tissue ectopically exists within soft tissue, resulting in serious extremity loss-of-function. This work focused on identifying regulating factors and gene network associated with HO pathology.Material and MethodsWe randomized the heterotopic ossification dataset GSE94683 as HO and NON-HO group. Weighted gene co-expression network analysis (WGCNA) was applied in identifying HO related modules. We discovered differentially expressed genes (DEGs) between the two groups. Then, we integrated protein-protein interaction (PPI) network, co-expression network, enrichment analysis, gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) for identifying the HO-related pathways. We also employed GSE126118 test set to investigate hub genes related to HO. Eventually, potential therapeutics for reversing abnormal hub gene levels were predict by DGIdb.ResultsWe discovered twelve HO status modules and 1,483 DEGs in HO samples compared with NON-HO counterparts. Four hub genes were obtained from the overlap of HO related PPI and coexpression networks. Training and test sets were adopted for verification, and three abnormally high hub gene expression in HO were obtained. Functional enrichment of DEGs indicated that these genes were involved in the stem cell pluripotency regulatory pathways, cytokine-cytokine receptor interaction, as well as rap1 pathway. Based on the results from GSEA and GSVA, many hub gene sets were mainly enriched in immune responses.ConclusionWe identified a gene coexpression network associated with regulatory factors in HO, which provided a new perspective on the pathogenesis and provided potential biomarkers or therapeutic targets.