bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated transcriptomic, metabolomic, and lipidomic analyses reveal a unique lipid profile of regulatory T cells upon activation
Authors: Sato Yohei , Yura Misa , Chandler Alana , Hanawa Yamato , Tsubota Akihito 
Published: January 2025

SummaryRegulatory T cells (Tregs) exhibit stable FOXP3 expression and regulate the immune response through suppressive activity. Their unique metabolic properties include reduced glycolysis and increased oxidative phosphorylation. We combined transcriptomic, metabolomic, and lipidomic analyses to dissect the metabolic dynamics of Tregs upon activation. Combined metabolomic and lipidomic analyses showed that freshly isolated and activated Tregs had distinct metabolomic and lipidomic properties, respectively. Activated Tregs contained omega-3 long-chain polyunsaturated fatty acid (PUFA)-rich diglycerides and triglycerides. These were supported by transcriptomics data, showing upregulation of PPAR-alpha and PPAR-gamma. Activated Tregs exhibited increased ceramide production, consistent with the upregulation of ceramide synthase and sphingomyelin synthase. Confocal microscopy revealed that Tregs were enriched in lysosomes and peroxisomes compared with effector T cells upon activation. Our data confirm the unique metabolic properties of Tregs, especially those characterized by omega-3 long-chain PUFA-rich triglycerides and ceramides, together with enriched lysosomes and peroxisomes, which correspond to metabolic alterations.
scMusketeers: Addressing imbalanced cell type annotation and batch effect reduction with a modular autoencoder
Authors: Collin Antoine , Pelletier Simon J. , Fierville Morgane , Droit Arnaud , Precioso Frédéric , Bécavin Christophe , Barbry Pascal 
Published: January 2025

AbstractThe growing number of single-cell gene expression atlases available offers a conceptual framework for improving our understanding of physio-pathological processes. To take full advantage of this revolution, data integration and cell annotation strategies need to be improved, in particular to better detect rare cell types and by better controlling batch effects in experiments. scMusketeers is a deep learning model that optimises the representation of latent data and solves both challenges. scMusketeers features three modules: (1) an autoencoder for noise and dimensionality reductions; (2) a focal loss classifier to enhance rare cell type predictions; and (3) an adversarial domain adaptation (DANN) module for batch effect correction. Benchmarking against state-of-the-art tools, including the UCE foundation model, showed that scMusketeers performs on par or better, particularly in identifying rare cell types. It also allows to transfer cell labels from single-cell RNA sequencing to spatial transcriptomics. With its modular and adaptable design, scMusketeers offers a versatile framework that can be generalized to other large-scale biological projects requiring deep learning approaches, establishing itself as a valuable tool for single-cell data integration and analysis.
Pre-oviposition development of the brown anole (Anolis sagrei)
Authors: Weberling Antonia , Shylo Natalia A. , Kircher Bonnie K. , Wilson Hannah , McClain Melainia , Marchini Marta , Starr Katherine , Sanger Thomas J. , Hollfelder Florian , Trainor Paul 
Published: January 2025

AbstractThe brown anole,Anolis sagrei, has emerged as a representative squamate species during the past several decades. Novel functional tools have been established to manipulate embryogenesis through genome editing or the introduction of small molecule inhibitors, and their effective use requires a thorough understanding of early anole embryogenesis. To enable precise and reproducible staging of anole embryos, we need knowledge of the progression of anole embryogenesis and morphogenesis. While post-oviposition development has been described, the pre-oviposition period remains to be explored. Here, we provide the first staging series of pre-oviposition development for the brown anole. Analysing the follicles and embryos through brightfield imaging, SEM, STEM, histology, and DAPI staining, we define 26 distinct developmental stages. Our dataset reveals that peri-gastrulation morphogenesis up to the initiation of neurulation diverges significantly from chicken, the common representative model of reptile embryogenesis. Furthermore, we followed heart development, neural crest cell migration and central nervous system development through immunofluorescence analyses and provide new comparative insights into the morphogenesis of each of these organ systems over time. Our work establishes the brown anole as a squamate model organism for cross clade evolutionary studies of early embryogenesis.
Probing the structural heterogeneity of Pup ligase PafA using H/D exchange mass spectrometry
Authors: Plourde Alicia , Ogata-Bean Jacquelyn C. , Vahidi Siavash 
Published: January 2025

ABSTRACTThe Pup-proteasome system (PPS) is a unique bacterial proteolytic pathway found in some bacterial species, including inMycobacterium tuberculosis, that plays a vital role in maintaining proteome integrity and survival during infection. Pupylation is the process of tagging substrates with Pup for degradation and is catalyzed by PafA, the sole Pup ligase in bacteria. However, how PafA interacts with diverse targets and its oligomeric state remain poorly understood. Although X-ray crystal structures have characterized PafA as a domain-swapped dimer, it is widely regarded as functionally active in its monomeric form. It remains to be established whether PafA dimerizes in solution, and how dimerization influences its function. In this study, we employed hydrogen-deuterium exchange mass spectrometry (HDX-MS) alongside complementary biophysical techniques to explore the oligomeric states and conformational dynamics of PafA. We show that recombinantly-produced PafA exists in a monomeric and a domain-swapped dimeric state in solution. While PafAdimeris stabilized by nucleotide, it predominately resides in a catalytically inactive conformation. Our HDX-MS highlighted regions throughout the N- and C-terminal domains that facilitate the PafA dimerization process. HDX-MS also revealed nucleotide binding induces global conformational changes on the PafAmonomer, underscoring the structural plasticity of this promiscuous enzyme. Our findings provide insight into the structure-function-dynamics relationship of PafA, shedding light on how its oligomeric states and conformational flexibility might influence its role in the PPS.
A novel SARS-CoV-2-derived infectious vector system
Authors: Elfayres Ghada , Xiao Yong , Pan Qinghua , Liang Chen , Barbeau Benoit , Berthoux Lionel 
Published: January 2025

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of COVID-19. The development of antiviral drugs for COVID-19 has been hampered by the requirement of a biosafety level 3 (BSL3) laboratory for experiments related to SARS-CoV-2, and by the lack of easy and precise methods for quantification of infection. Here, we developed a SARS-CoV-2 viral vector composed of all four SARS-CoV-2 structural proteins constitutively expressed in lentivirally transduced cells, combined with an RNA replicon deleted for SARS-CoV-2 structural protein genes S, M and E, and expressing a luciferase-GFP fusion protein. We show that, after concentrating viral stocks by ultracentrifugation, the SARS-CoV-2 viral vector is able to infect two human cell lines expressing receptors ACE2 and TMPRSS2. Both luciferase activity and GFP fluorescence were detected, and transduction was remdesivir-sensitive. We also show that this vector is inhibited by three type I interferons (IFN-I) subtypes. Although improvements are needed to increase infectious titers, this vector system may prove useful for antiviral drug screening and SARS-CoV-2-related investigations.
ParaSurf: A Surface-Based Deep Learning Approach for Paratope-Antigen Interaction Prediction
Authors: Papadopoulos Angelos-Michael , Axenopoulos Apostolos , Iatrou Anastasia , Stamatopoulos Kostas , Alvarez Federico , Daras Petros 
Published: January 2025

AbstractMotivationIdentifying antibody binding sites, is crucial for developing vaccines and therapeutic antibodies, processes that are time-consuming and costly. Accurate prediction of the paratope’s binding site can speed up the development by improving our understanding of antibody-antigen interactions.ResultsWe present ParaSurf, a deep learning model that significantly enhances paratope prediction by incorporating both surface geometric and non-geometric factors. Trained and tested on three prominent antibody-antigen benchmarks, ParaSurf achieves state-of-the-art results across nearly all metrics. Unlike models restricted to the variable region, ParaSurf demonstrates the ability to accurately predict binding scores across the entire Fab region of the antibody. Additionally, we conducted an extensive analysis using the largest of the three datasets employed, focusing on three key components: (1) a detailed evaluation of paratope prediction for each Complementarity-Determining Region loop, (2) the performance of models trained exclusively on the heavy chain, and (3) the results of training models solely on the light chain without incorporating data from the heavy chain.Availability and ImplementationSource code for ParaSurf, along with the datasets used, preprocessing pipeline, and trained model weights, are freely available athttps://github.com/aggelos-michael-papadopoulos/ParaSurf.Contactangepapa@iti.gr,axenop@iti.grSupplementary informationSupplementary data are available atBioinformaticsonline.
The resource elasticity of control
Authors: Solomyak Levi , Emanuel Aviv , Eldar Eran 
Published: January 2025

AbstractThe ability to determine how much the environment can be controlled through our actions has long been viewed as fundamental to adaptive behavior. While traditional accounts treat controllability as a fixed property of the environment, we argue that real-world controllability often depends on the effort, time and money we are able and willing to invest. In such cases, controllability can be said to beelasticto invested resources. Here we propose that inferring this elasticity is essential for efficient resource allocation, and thus, elasticity misestimations result in maladaptive behavior. To test these hypotheses, we developed a novel treasure hunt game where participants encountered environments with varying degrees of controllability and elasticity. Across two pre-registered studies (N=514), we first demonstrate that people infer elasticity and adapt their resource allocation accordingly. We then present a computational model that explains how people make this inference, and identify individual elasticity biases that lead to suboptimal resource allocation. Finally, we show that overestimation of elasticity is associated with elevated psychopathology involving an impaired sense of control. These findings establish the elasticity of control as a distinct cognitive construct guiding adaptive behavior, and a computational marker for control-related maladaptive behavior.
Immuno-moodulin is Differentially Expressed in T Cells and Plasma in Obsessive-Compulsive Disorder Patients
Authors: Blacksell Isobel Alice , Vismara Matteo , Lim Christine Maria , Dell’Osso Bernardo , Pallanti Stefano , Hollander Eric , Vendruscolo Michele , D’Addario Claudio , Cooper Dianne , D’Acquisto Fulvio 
Published: January 2025

AbstractImmuno-moodulin (Imood), a recently discovered protein expressed in T cells, is associated with anxiety-like behavior in mice. However, its mechanism of action in modulating neuroimmune interactions remains unclear. To investigate this problem, we characterized Imood in human blood and immune cells using neutralizing monoclonal antibodies, revealing its nature as an intrinsically disordered protein (IDP) with unique expression patterns. Our findings indicate that Imood is predominantly expressed intracellularly in peripheral blood mononuclear cells (PBMCs), particularly T lymphocytes, but is absent in polymorphonuclear cells. Upon T-cell activation, Imood exhibits distinct mobilization patterns with increased surface expression. Bioinformatics analysis identified a strong propensity for oligomerization and liquid-liquid phase separation. We also found that T cells from patients with Obsessive-Compulsive-Disorder (OCD) displayed significantly elevated surface Imood expression compared to healthy controls, as well as an altered level of Imood polymerization in the plasma. Taken together, these results elucidate the expression patterns and structural properties of Imood in human immune cells, which open new avenues for OCD diagnostics, and prompt further study for understanding the aetiology of OCD and related disorders.
USP1/UAF1 targets polyubiquitinated PCNA with an exo-cleavage mechanism that enriches for monoubiquitinated PCNA
Authors: Keijzer Niels , Sakoltchik Jan , Majumder Kaustav , van Lil Nina , El Oualid Farid , Fish Alexander , Sixma Titia K. 
Published: January 2025

AbstractDNA damage tolerance (DDT) is an important pathway that allows our cells to bypass DNA lesions during replication. DDT is orchestrated by ubiquitination of PCNA: Monoubiquitination (PCNA-Ub) initiates recruitment of TLS polymerases but also serves as substrate for K63-linked polyubiquitination that leads to HR-mediated bypass mechanisms. Recent work on USP1/UAF1 inhibition revealed that K48-linked chains are also formed on PCNA, resulting in its proteasomal degradation. USP1/UAF1 is established as deubiquitinating enzyme (DUB) for PCNA-Ub, but little is known about deubiquitination of chains on PCNA.Here we show that USP1/UAF1 cleaves both K48 and K63-linked ubiquitin chains on PCNA efficiently, using an exo-cleavage mechanism. Kinetic analysis reveals that USP1/UAF1 prefers cleaving the ubiquitin-ubiquitin bond over cleavage of the ubiquitin-PCNA isopeptide bond and therefore treats poly- and monoubiquitinated PCNA as different substrates. A cryo-EM structure of USP1/UAF1 with a K63-diubiquitin and structure-based mutagenesis reveals that its mechanistic preference is maintained in evolution. Its kinetic mechanism results in relative enrichment of monoubiquitinated PCNA that could initially promote TLS over HR-like bypass. Taken together, these results suggest that USP1/UAF1 could be important in temporary protection of PCNA against K48- and K63-linked polyubiquitination and highlight this DUB as a potential regulator of DDT pathway choice.
Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning
Authors: Kommu Sindhura , Wang Yizhi , Wang Yue , Wang Xuan 
Published: January 2025

AbstractMotivationSingle-cell RNA sequencing (scRNA-seq) data offers unprecedented opportunities to infer gene regulatory networks (GRNs) at a fine-grained resolution, shedding light on cellular phenotypes at the molecular level. However, the high sparsity, noise, and dropout events inherent in scRNA-seq data pose significant challenges for accurate and reliable GRN inference. The rapid growth in experimentally validated transcription factor-DNA binding data (e.g., ChIP-seq) has enabled supervised machine learning methods, which rely on known gene regulatory interactions to learn patterns, and achieve high accuracy in GRN inference by framing it as a gene regulatory link prediction task. This study addresses the gene regulatory link prediction problem by learning informative vectorized representations at the gene level to predict missing regulatory interactions. However, a higher performance of supervised learning methods requires a large amount of known TF-DNA binding data, which is often experimentally expensive and therefore limited in amount. Advances in large-scale pre-training and transfer learning provide a transformative opportunity to address this challenge. In this study, we leverage large-scale pre-trained models, trained on extensive scRNA-seq datasets and known as single-cell foundation models (scFMs). These models are combined with joint graph-based learning to establish a robust foundation for gene regulatory link prediction.ResultsWe propose scRegNet, a novel and effective framework that leverages scFMs with joint graph-based learning for gene regulatory link prediction. scRegNet achieves state-of-the-art results in comparison with nine baseline methods on seven scRNA-seq benchmark datasets. In addition, scRegNet is more robust than the baseline methods on noisy training data.AvailabilityThe source code is available athttps://github.com/sindhura-cs/scRegNet.
Exploring The Formation And Permeability Of Plasmodesmata In The Liverwort,Marchantia polymorpha
Authors: Hsu Chia-Yun , Hsu Chia-Hsuan , Chang Hui-Yu , Lu Kuan-Ju 
Published: January 2025

AbstractPlasmodesmata are cell-wall-embedded channels that evolved in the common ancestor of land plants to increase cell-to-cell communication. Whether all the fundamental properties of plasmodesmata emerged and were inherited in all land plants at the same time is unknown. Here we show that the bryophyteMarchantia polymorpha(a non-vascular plant) forms mostly simple plasmodesmata in early-developing gemmae. The complexity of plasmodesmata increases during gemma maturation, and complex plasmodesmata with enlarged cavities are majorly observed in thalli. In contrast to vascular plants, whose simple plasmodesmata can transport monomeric fluorescent proteins, plasmodesmata inMarchantia polymorphalimited their permeability before the juvenile-to-adult transition. In support, callose, a known polysaccharide regulating plasmodesmata permeability in vascular plants, accumulated in most of theMarchantia polymorphatissues examined. Furthermore, we found that in the apical meristematic region, plasmodesmata allowed the transport of monomeric fluorescent proteins, and this relaxation might correlate with the lower accumulation of callose. Taken together, our study suggests that certain plasmodesmata properties, such as complexity progression and callose accumulation, may have evolved before the divergence between vascular and non-vascular plants.
Age and sex effects on blood retrotransposable element expression levels: Findings from the population-based Rhineland Study
Authors: Talevi Valentina , Lee Hang-Mao , Liu Dan , Beyer Marc D. , Salomoni Paolo , Breteler Monique M.B. , Aziz N. Ahmad 
Published: January 2025

AbstractRetrotransposable elements (RTEs) have been implicated in the pathogenesis of several age-associated diseases. Although model systems indicate that age- and sex-dependent loss of heterochromatin increases RTE expression, data from large human studies are lacking. Here we assessed the expression levels of 795 blood RTE subfamilies in 2467 participants of the population-based Rhineland Study. We found that the expression of more than 98% of RTE subfamilies increased with both chronological and biological age. Moreover, the expression of heterochromatin regulators involved in RTE silencing were negatively related to the expression of 690 RTE subfamilies. Finally, we observed sex differences in 42 RTE subfamilies, with higher expression in men. The genes mapped to sex-related RTEs were enriched in immune response-related pathways. Importantly, we validated our key findings in an independent population-based cohort. Our findings indicate that RTEs and their repressors are markers of aging, and that their dysregulation is linked to inflammation, especially in men.Graphical abstract
Task-Optimized Machine Learning for High-Accuracy Alzheimer’s Diagnosis from Handwriting Data
Authors: Seyedi HosseiniNian Zahra , Tajari Ahmadreza , Barati B Behrouz , Baharlouei Sina 
Published: January 2025

AbstractTraining complex models on Alzheimer’s Disease (AD) datasets is challenging due to the costly process of extracting features from a wide range of patient tasks. Developing high-performance AD detection models that rely on a small number of tasks can help reduce dataset acquisition costs and improve the interpretability of the AD detection model. To address this, we propose a two-stage forward-backward feature selection approach to identify the most relevant tasks and features for predicting AD with high accuracy. We evaluate a range of machine learning methods, including Extreme Gradient Boosting (XGBoost), Random Forest, K-Nearest Neighbors, Support Vector Machine, Multi-Layer Perceptron, and Logistic Regression, to determine the best classification model for feature selection and downstream prediction tasks. Given the limited sample size, we assess model performance using Leave-One-Out-Cross-Validation (LOOCV) to ensure robust results. Our method was compared with multiple state-of-the-art approaches for feature selection. The results of our analysis indicate that combining our proposed methods for feature selection with the XGBoost classifier, using only four tasks, produces a model that is both more interpretable and high-performing compared to other approaches. This suggests focusing on these four tasks, rather than collecting extensive task data from patients, can yield a reliable predictor for diagnosis of AD with an accuracy of 91.37%, 93.94% recall, 89.77% precision, and 91.32% F1 score - surpassing other classification methods. This research represents a significant advancement in the efficiency and reliability of AD diagnosis, improving patient prognosis and offering potential benefits to healthcare systems.
Deuterium-reinforced polyunsaturated fatty acids protect against muscle atrophy induced by type 1 diabetes in mice
Authors: Eshima Hiroaki , Tomoaki Ishihara , Tabuchi Ayaka , Kano Yutaka , Kurokawa Kenji , Shchepinov Mikhail S. 
Published: January 2025

AbstractHIGHLIGHTSD-PUFA diet prevents muscle atrophy in STZ-induced diabetic mice.D-PUFA diet prevents muscle weakness depending on increased calcium release in STZ-induced diabetic mice.D-PUFA diet may show a trend to decrease blood glucose in STZ-induced diabetic mice.D-PUFA diet does not alter ferroptosis-related protein profiles including ACSL4, LPCAT3, ALOX12, and Gpx4.Oxidative stress and reactive oxygen species (ROS) have been linked to muscle atrophy and weakness. Diabetes increases the oxidative status of lipoproteins in nearly all tissues, including muscle tissues, but the role of lipid ROS on diabetes-induced muscle atrophy is not fully understood. Deuterium reinforced polyunsaturated fatty acids (D-PUFA) are more resistant to ROS-initiated chain reaction of lipid peroxidation than regular hydrogenated PUFA (H-PUFA). In this study, we tested the hypothesis that D-PUFA would protect muscle atrophy induced by diabetes driven by an accumulation of lipid hydroperoxides (LOOH). C57BL/6J mice were dosed with H-PUFA or D-PUFA for four weeks through dietary supplementation and then injected with streptozotocin (STZ) to induce insulin-deficient diabetes. After two weeks, muscles tissues were analyzed for individual muscle mass, force generating capacity and cross-sectional area. Skeletal muscle fibers from diabetic mice exhibited increased total ROS and LOOH. This was abolished by the D-PUFA supplementation regardless of accumulated iron. D-PUFA were found to be protective against muscle atrophy and weakness from STZ-induced diabetes. Prevention of muscle atrophy and weakness by D-PUFA might be independent of ACSL4/LPCAT3/15-LOX pathway. These findings provide novel insights into the role of LOOH in the mechanistic link between oxidative stress and diabetic myopathy and suggest a novel therapeutic approach to diabetes-associated muscle weakness.