bluuebunny/crossref_metadata_2025_split huggingface dataset:
The activation of fibroblast growth factor receptor 1 provides therapeutic benefit in a mouse model of Alzheimer’s disease
Authors: Hung Wei-Chien , Sun Wei-Chun , Su Tzu-Ching , Lee Yi-Jung , Cheng Irene Han-Juo 
Published: February 2025

AbstractAlzheimer’s disease (AD) is a neurodegenerative disease that is characterized by the accumulation of amyloid plaque and neurofibrillary tangles, ultimately impairing multiple cognitive domains. Both plaques and tangles cause neuronal damage and stimulate inflammatory responses in glial cells. The fibroblast growth factor receptor 1 (FGFR1)-mediated signaling pathways support the function of damaged neurons and modulate inflammatory response. The FGFR1 agonists, including Fibroblast growth factor 1 (FGF1) and FG loop peptide (FGL), have been implicated in multiple disease therapy. Whether FGFR1 agonists can improve pathology and cognitive function in AD remains unknown. Here, we showed that administration of FGF1 and FGL to the AD mouse model reversed spatial memory impairment, enhanced neurogenesis, suppressed reactive astrogliosis, and restricted dystrophic neurite. However, only FGF1 treatment reduced the deposition of senile plaque. In microglial culture studies, FGF1 improves the phagocytosis ability of microglia, which contributes to the clearance of plaques. Together, our findings suggested that FGFR1 agonists alleviate pathology and cognitive impairment via immunomodulatory and improve neuronal health in the AD mouse model.HighlightsFGFR1 agonists alleviate memory deficits in AD mice.FGFR1 agonists increase adult neurogenesis and limit dystrophic neurites in AD mice.FGFR1 agonists increase plaque-associated microgliain vivoand promote phagocytosisin vitro.
Proteomic characterisation of the early rheumatoid arthritis-cardiovascular disease multimorbid axis
Authors: Shukla Rudresh R , Mohammed Amr , Black Nicholas , Erhayiem Bara , Fent Graham , Miller Christopher A , Plein Sven , Plant Darren , Buch Maya H 
Published: February 2025

AbstractBackgroundInflammation contributes to the increased risk of cardiovascular disease (CVD) observed in people with rheumatoid arthritis (RA), with increased prevalence observed from time of diagnosis. Subclinical vascular and myocardial abnormalities can be detected with cardiovascular magnetic resonance (CMR) imaging in otherwise low risk individuals. Identifying associated circulating markers could enable diagnostics and implicate biological pathways of RA-CVD. The study’s objective was to identify blood-based proteins associated with CMR measures of vascular and myocardial abnormality and associated inflammatory and/or cardio-metabolic pathways in a new-onset RA cohort.MethodsSerum samples (baseline/pre-treatment, N = 75 and year 1, N = 71) from CADERA (Coronary Artery Disease Evaluation in Rheumatoid Arthritis) participants, a subgroup of a randomised controlled trial, who underwent CMR, were used to measure 334 proteins across 4 pre-defined Olink panels (Inflammation, Cardiovascular-II, Cardiovascular-III, Cardiometabolic). Bayesian mixed effects regression analyses, unsupervised hierarchical clustering and protein network analyses were applied. Normalised protein expression from 334 Olink proteins were used as exposures in the regression analyses. CMR measures of vascular stiffness (aortic distensibility and stiffness index) and myocardial tissue characteristics (native T1, myocardial extracellular volume and late gadolinium enhancement) were each used as individual outcomes in regression analyses. Associations were considered significant at 95% threshold for credible intervals. An expanded physical protein-protein interaction (PPI) network created using the significant (seed) proteins was subjected to topological and enrichment analysis to identify enriched biological pathways.Results54/334 proteins were associated significantly with CMR measures at baseline (7 - vascular; 48 - myocardial tissue characteristics; Coagulation factor 11 with both). Two proteins, TNFSF13B (B-cell activating factor) and CRTAC1, were associated with CMR measures at baseline, were sensitive to change over time and co-varied with changes in CMR measure. Topological analysis of expanded PPI network revealed GRB2 as most connected (degree score=64; closeness score=118.83) and four key signalling pathways, including JAK-STAT and EGFR tyrosine kinase inhibitor resistance, emerged as significant.ConclusionsThis first proteomic study of treatment-naïve early RA and subclinical cardiovascular pathology identifies proteins that could aid diagnostic test development and implicates signalling pathways in the RA-cardiovascular axis to inform on our understanding of the basis of RA-CVD.
Decoding DNA methylation and non-coding RNAs mediated regulatory landscape of breast tumor and adjacent normal tissues
Authors: Kumar Swapnil , Vindal Vaibhav 
Published: February 2025

AbstractTumorigenesis not only involves perturbations of mRNAs but also microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and DNA methylation (DNAm). Additionally, the tumor-adjacent normal tissues (TANTs) that are used as controls in cancer studies are not completely normal and, if explored systematically, can help to understand tumorigenesis better. In this regard, expression and DNAm profiles of breast tumor tissues (viz., Luminal-A, Luminal-B, Her2+, Basal-like, and Normal-like) and TANTs were used to construct and analyze their integrative networks. These networks included regulations among genes, transcription factors, lncRNAs, and miRNAs, along with gene/lncRNA methylations. This followed the identification of candidate genePCLAFhaving transitivity value one across all six tissue networks. Further, 503 common hubs (388 protein-coding, 100 miRNAs, and 15 lncRNAs), including seven methylated genes (SPI1,STAT5A,RORC,SPDEF,ELF5,HOXB2, andRUNX3), and 149 common bottlenecks (145 protein-coding, three miRNAs, and one lncRNA), including three methylated genes (SPI1,SPDEF, andRUNX3) and one methylated lncRNA (PART1), were identified. Moreover, 145 nodes, including three methylated genes (SPI1,SPDEF, andRUNX3) and three miRNAs (hsa-miR-34a, hsa-miR-149, and hsa-miR-141), were identified as both hubs and bottlenecks (HBs) across all six tissues. However, none of the lncRNAs were found HBs therein. Genes and lncRNAs with higher degrees, betweenness, eigenvector centrality, and pagerank were found more preferably methylated than others. Further, pathway analysis of hubs and bottlenecks across all six tissues revealed their significant involvement in the pathway of Transcriptional misregulation in cancer. Overall, these findings uncover new insights into the epigenetic mechanisms of tumorigenesis.
Co-Substrate Free Valorisation of Lignin Monomers by Assimilation of C1 and C2 By-Products
Authors: Bergen Daniel , Puiggené Òscar , Marcellin Esteban , Speight Robert E. , Nikel Pablo I. , Ebert Birgitta E. 
Published: February 2025

AbstractLignin is an underutilised global resource with significant potential for the production of chemicals that are currently derived from fossil resources. However, biotechnological lignin valorisation faces various challenges including its recalcitrance and the toxicity of aromatic intermediates, products, and by-products like formaldehyde. While biochemical production from lignin-derived monomers has been demonstrated by disrupting native lignin degradation pathways, this approach required co-feeding additional carbon sources such as glucose for growth. This dependence on additional carbon sources can create competition with the food industry and undermine the economic sustainability of the bioprocess. Here, we report growth of a protocatechuate production strain ofPseudomonas putidaEM42 on the by-products fromp-coumarate and ferulate valorisation, achieving carbon efficiencies of up to 78 %. Additional flux balance analysis identified C1 assimilation pathways, including two novel pathways, beneficial for the growth on the formaldehyde by-product from ferulate degradation leading to improved carbon utilisation. This study demonstrates how by-product utilisation from lignin conversion can eliminate the need for co-feeding additional carbon sources, thereby potentially improving the efficiency of lignin valorisation.
Protein–Ligand Affinity Prediction via Jensen–Shannon Divergence of Molecular Dynamics Simulation Trajectories
Authors: Igarashi Kodai , Ohue Masahito 
Published: February 2025

AbstractPredicting the binding affinity between proteins and ligands is a critical task in drug discovery. Although various computational methods have been proposed to estimate ligand target affinity, the method of Yasuda et al. (2022) ranks affinities based on the dynamic behavior obtained from molecular dynamics (MD) simulations without requiring structural similarity among ligand substituents. Thus, its applicability is broader than that of relative binding free energy calculations. However, their approach suffers from high computational costs due to the extensive simulation time and the deep learning computations needed for each ligand pair. Moreover, in the absence of experimental ΔGvalues (oracle), the sign of the correlation can be misinterpreted. In this study, we present improvements to Yasuda et al.’s method. Our contributions are threefold: (1) By introducing the Jensen–Shannon (JS) divergence, we eliminate the need for deep learning-based similarity estimation, thereby significantly reducing computation time; (2) We demonstrate that production run simulation times can be halved while maintaining comparable accuracy; and (3) We propose a method to predict the sign of the correlation between the first principal component (PC1) and ΔGby using coarse ΔGestimations obtained via AutoDock Vina.
Block-Champagne: A novel Bayesian framework for Imaging E/MEG Source
Authors: Feng Zhao , Guan Cuntai , Sun Yu 
Published: February 2025

AbstractEstimating the extents of E/MEG source activities is vital for exploring brain dynamics at high spatiotemporal resolution. However, conventional ESI models exclusively overemphasize on the accurate estimation of the source locations and struggle to reconstruct the extents due to the ill-posed nature of the problem, which requires effective integration of biophysical constraints and proficiency in signal processing methods. In this study, a novel Bayesian framework – Block-Champagne is introduced to provide accurate estimation of extended sources (i.e., both locations and extents). Specifically, a blocksparsity constraint is employed to model the local homogeneity of sources, which can be updated automatically to reconstruct source with arbitrary extents. Furthermore, prior constraints from other modalities, such as fMRI maps, can be incorporated to model interactions between remote sources to further enhance source reconstruction. The performance of Block-Champagne was quantitatively evaluated through simulation experiments, demonstrating its overall superiority under various complex scenarios (i.e., SNR, extent size, & number of extents) compared to benchmark algorithms (including LORETA, EBI-Convex, tsCham, L21-Sissy, & SI-STBF). Moreover, validation results obtained from deep-brain stimulation EEG, epilepsy data, and face-processing multi-modal data further proved the practical feasibility of Block-Champagne. In conclusion, our study reveals the superiority of the proposed BlockChampagne in accurate reconstruction of extended source, positioning Block-Champagne as a highly promising tool for realistic applications where source locations and extents are of equivalent importance.
Boosting metaproteomics identification rates and taxonomic specificity with MS2Rescore
Authors: Van Den Bossche Tim , Declercq Arthur , Gabriels Ralf , Holstein Tanja , Mesuere Bart , Muth Thilo , Verschaffelt Pieter , Martens Lennart 
Published: February 2025

AbstractBackgroundMetaproteomics, the study of the collective proteome within microbial ecosystems, has gained increasing interest over the past decade. However, peptide identification rates in metaproteomics remain low compared to single-species proteomics. A key challenge is the limited sequence resolution of current identification algorithms, which were primarily designed for single-species analyses. To address this, we applied the machine learning-driven MS2Rescore tool to multiple metaproteomics datasets from diverse microbial environments and benchmark studies.ResultsWe demonstrate that MS2Rescore outperforms typical metaproteomics identification workflows. It significantly increases peptide identification rates compared to standard database searches, even when compared to Percolator rescoring. Moreover, it enables lowering the false discovery rate (FDR) to 0.1% with minimal sensitivity loss, a substantial improvement over the 1% or 5% FDR thresholds commonly used in metaproteomics, in turn leading to greater specificity in downstream taxonomic annotation with Unipept.ConclusionsOur findings show that MS2Rescore substantially improves peptide identification sensitivity as well as specificity in metaproteomics, and delivers improved taxonomic specificity. This advancement results in a more reliable downstream taxonomic analysis, reinforcing the potential of machine learning-based rescoring in metaproteomics research.
CombDNF: Disease-specific drug combination predictions with network-based features on clinically validated data
Authors: Hiort Pauline , Renard Bernhard Y. , Baum Katharina 
Published: February 2025

AbstractDrug combinations are increasingly applied to treat a wide range of complex diseases. Drug action and thus also drug combination effects can differ between diseases, e.g., due to molecular differences. Therefore, disease-specific predictions are required for treatments. A plethora of methods based on cell-line screening data in cancer has been proposed. However, their extendability to other diseases is limited, as is their applicability in the clinical context due to the in-vivo-in-vitro gap. In contrast, only few approaches rely on clinically validated data.Here, we propose CombDNF, a novel machine-learning based method for disease-specific drug combination prediction on clinically validated data. CombDNF is trained and predicts both on clinically approved (effective) and clinically reported adverse drug combinations from a broad collection of data sources. It can cope with the highly imbalanced label distribution in drug combination data. Further, CombDNF leverages network-derived features based on drug target and disease gene relationships. To incorporate uncertainty of the network topology it relies on edge weights in the underlying network.We systematically evaluate CombDNF against available state-of-the-art methods in four diseases with different underlying mechanisms and ground truth data characteristics. CombDNF outperforms all state-of-the-art methods in all four diseases by at least 84% in the AUPR. This translates, for example, to an enrichment of effective drug combinations in the top ten hypertension-specific predictions of four, compared to one for the best competing method. In addition, network edge weighting by interaction confidence scores indeed yields improved predictions. Further, we find evidence for biological plausibility of our top ranked drug combinations.We make the training and evaluation pipeline for CombDNF available ready-to-use athttps://github.com/DILiS-lab/CombDNF.
ESMStabP: A Regression Model for Protein Thermostability Prediction
Authors: Ramos Marcus , Jernigan Robert L. , Kilinc Mesih 
Published: February 2025

AbstractAccurately predicting protein thermostability is crucial for numerous applications in biotechnology, pharmaceuticals, and food science. Experimental methods for determining protein melting temperatures are often time-consuming and costly, driving the need for efficient computational alternatives. In this paper, we introduce ESMStabP, an enhanced regression model for predicting protein thermostability. To improve model performance and generalizability, we assembled a significantly larger dataset by combining and cleaning datasets previously utilized in other thermostability models. Building on Deep-StabP, ESMStabP incorporates significant improvements, using embeddings from the ESM2 protein language model and thermophilic classifications. The predictions from ESMStabP outperform DeepStabP and other existing predictors, achieving an R2of 0.95 and a Pearson correlation coefficient (PCC) of 0.97. Despite these improvements, challenges such as dataset availability. This work underscores the critical role of specific layer identification for model development and outlines potential directions for future advancements in protein stability predictions
Melatonin-mediated methylglyoxal homeostasis and regulation of autophagy during seed germination under PEG-induced drought stress in upland cotton
Authors: Dake Deepika , Supriya Laha , Kumar Amarjeet , Gudipalli Padmaja 
Published: February 2025

AbstractMethylglyoxal (MGO), toxic byproduct of glycolysis, acts as a signaling molecule at low levels, but its over-accumulation during drought stress disrupts redox balance and accelerates cell death. Contrarily, melatonin maintains redox balance, particularly during stress. The redox status and MGO level might differ in drought-sensitive and drought-tolerant varieties, so shall the melatonin’s effect. The study evaluated the effect of melatonin-priming on MGO detoxification and autophagy during polyethylene glycol (PEG)-induced drought stress during seed germination in drought-sensitive (L-799) and drought-tolerant (Suraj) varieties of upland cotton. Melatonin-priming increased endogenous melatonin content, reduced MGO accumulation and advanced glycation end-products (AGEs), and downregulated the expression of MGO biosynthesis genes in L-799 under stress. The expression and activities of glyoxalases and non-glyoxalases were upregulated, showing melatonin’s effectiveness in MGO detoxification. Additionally, priming upregulated the expression ofTPI1,PGK5,and PK1and downregulatedHK3expression, allowing better conversion of glucose to pyruvate, leading to reduced MGO in L-799. The downregulation of necrosis-related genes with reduced cell death in L-799 shows the potential of priming in maintaining cell viability under stress. Furthermore, upregulated expression ofSnRK1.1,SnRK2.6genes and KIN10 protein levels, with enhanced autophagy markers (ATGs, MDC-stained bodies, lipidated-ATG8), confirmed improved autophagy in melatonin-primed L-799 under stress. Despite lowered ABA, melatonin-mediated MGO homeostasis likely activated MAPK6, inducing autophagy independent of ABA in stressed plants. Conversely, Suraj, with higher endogenous melatonin and inherent stress tolerance, showed limited response to priming. Thus, the study illustrates melatonin’s role in regulating MGO homeostasis and autophagy under drought stress in cotton.
Triploidy is prominent in the duckweedLemna minorcomplex
Authors: Michael Todd P. , Hoang Phuong T.N. , Abramson Bradley W. , Mamerto Allen , Pasaribu Buntora , Ernst Evan , Allsing Nicholas , Braglia Luca , Petrus Semar , Fuchs Jörg , Schubert Veit , Hartwick Nolan , Wang Megan , Lensink Mariele , Duong Tiffany , Colt Kelly , Bog Manuela , Sree K. Sowjanya , Morello Laura , Appenroth Klaus-J. , Schubert Ingo , Martienssen Robert A. , Lam Eric 
Published: February 2025

AbstractDuckweeds (Lemnaceae Martinov) are aquatic monocotyledonous flowering plants comprising five genera and 35 recognized species, known for being the smallest and fastest-growing flowering plants on Earth. Many species are morphologically indistinguishable due to their highly reduced structures, yet molecular evidence suggests that visually similar clones may represent distinct species or hybrids. For example, clonal accessions of the globally distributedLemna minorin the Landolt Duckweed Collection exhibit genome size variations of several hundred megabases (Mb), raising questions about their taxonomic classification and evolutionary origins. We analyzed 58 presumedL. minorclones to resolve these relationships using a comprehensive suite of methods, including whole-genome sequencing (WGS), flow-cytometric genome size measurements, molecular markers, chromosome counting, and genomicin situhybridization (GISH). Our findings reveal extensive genome plasticity within the “Lemna minorcomplex,” identifying diploid and triploidL. minorclones, as well as di-haploid and triploid interspecific hybrids calledL. × japonica(L. minor×L. turionifera),L. × mediterranea(L. minor×L. gibba), and a novel African-clade distinct from knownL. minorlineages. Triploidy was prevalent, occurring in 29% of the clones, and was associated with enhanced growth under optimal conditions but reduced performance under high light and temperature. These findings highlight the widespread role of triploidy, cryptic species, and hybridization in theL. minorcomplex, emphasizing the importance of multiple approaches for accurately classifying duckweed species and understanding their evolutionary trajectories.
Red SeaSynechococcusexhibits greater resilience to UV-B radiation than pico-eukaryotic phytoplankton
Authors: Overmans Sebastian , Agustí Susana 
Published: February 2025

1.AbstractUV-B radiation can be a significant stressor to primary producers in tropical seas, particularly to organisms residing close to the surface. We investigated the UV-B sensitivity of natural pico-phytoplankton communities in the Red Sea, one of the warmest seas on Earth. The communities were incubated either in full sunlight or under UV-B-reduction conditions. The mean lethal UV-B radiation dose (LRD50) of naturalSynechococcuswas 57 kJ to 73% non-detectable compared to 8 KJ to 45 % non-detectable for pico-eukaryotes. UV-B-inducedFv/Fmchange (ΔFv/Fm) in natural pico-phytoplankton communities ranged from -0.22 to 0.06 (mean: -0.08). Changes inSynechococcusgrowth under UV-B-reduction were more pronounced in cooler waters. The dominant pico-phytoplankton in the Red Sea,Synechococcus, exhibited a strong resistance to UV-B, highlighting their exceptional adaptation to the local conditions. Our results underscore the importance of considering the UV-B sensitivity of natural phytoplankton communities in the context of global change, which may differ from those of cultured strains.
Dynamic Disorder in Chlorophyll Aggregation and Light-Harvesting Complex II in the Plant Thylakoid Membranes using Coarse-Grained Simulations
Authors: Saini Renu , Garg Avinash , Debnath Ananya 
Published: February 2025

AbstractThe dynamics of the aggregated light-harvesting complex (LHCII) associated with its antennae pigments can be crucial for a transition between light harvesting and dissipative states pivotal for non-photochemical quenching (NPQ). To this end, aggregation of chlorophyll-a (CLA) without the LHCII and pigment binding LHCII monomers in the plant thylakoid membranes have been investigated using coarse-grained molecular dynamics simulations at 293 K. Both CLA without the LHCII and pigment-binding LHCII monomers dynamically form and break dimers and higher-order aggregates in thylakoids within the simulation time. The contact lifetime and waiting time distributions of CLA dimers exhibit multiple time scales including most populated fast time scales and less populated slow time scales. The survival probability of CLA dimer in the absence of the LHCII follows a non-exponential decay with multiple residence time scales, leading to a time-dependent rate, unlike conventional rate theory. Such non-exponential decay of survival manifests the emergence of dynamic disorder in CLA without the LHCII resulting from the coupling between time scales of dimer formation and higher-order aggregates. The conformational fluctuations of the LHCII known for inter-CLA coupling variation occur on multiple time scales comparable to the LHCII dimer residence time scales leading to less probable but comparable and more probable slower inter-CLA fluctuations. This indicates the dynamic coupling in the LHCII conformations and their aggregates with the antennae pigments can result in dynamic disorder which will be highly relevant for the light-harvesting efficiency and regulation of NPQ.TOC Graphic