bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unraveling the inbreeding depression patterns in a self-pollinated eucalyptus population
Authors: Ferreira Filipe Manoel , Ferreira Vieira Netto João Amaro , Melchert Guilherme Ferreira , Benatti Thiago Romanos , de Matos José Wilacildo , Muniz Fabiana Rezende , Baracuhy Brum Itaraju Junior , do Nascimento André Vieira , Bhering Leonardo Lopes , da Graças Dias Kaio Olimpio , Tambarussi Evandro Vagner 
Published: February 2025

AbstractGenetic improvement greatly contributed to the success of the forest industry in Brazil. While past selection efforts inEucalyptusspp. have yielded satisfactory genetic gains, the response to selection in the last decade has fallen below expectations. Recent research suggests that inbreeding-based selection strategies, well-established in crops such as rice and maize, could be adapted to enhance perennial species such as guava and can be expanded to forest species, such as eucalypt. In this context, 20 eliteEucalyptusspp. genotypes were self-pollinated, producing 30 progenies per family. A total of 600 individuals were planted in an experimental trial and evaluated for growth traits at 3 years of age. Both the progeny and the 20 parent geno-types were genotyped using SNP chips. This research aims to unravel the patterns of inbreeding depression in thisS0:1population ofEucalyptusspp., by examining autozygosity and genomic inbreeding patterns, estimating in-breeding depression (ID) and genetic parameters, and studying the impact of the unbalance between selfed and crossed individuals on the inbreeding depression estimator. The results revealed that dominance variance accounted for a notable portion of the phenotypic variation, demonstrating the significance of non-additive genetic effects in diameter at breast height (DBH).IDwas evident, with reductions inDBHobserved in most families as ho- mozygosity increased. Simulations highlighted that unbalanced sample sizes of selfed and crossbred individuals could bias estimates ofID. By integrating genomic data and advanced quantitative methods, this study brings new information into the genetic consequences of self-pollination in eucalyptus, offering a foundation for managing inbreeding and enhancing genetic gains in perennial breeding.
Partially differentiated enterocytes in ileal and distal-colonic human F508del-CF-enteroids secrete fluid in response to forskolin and linaclotide
Authors: Rong Yan , Zhang Zixin , de Jonge Hugo R. , Lin Ruxian , Yu Huimin , Sarker Rafiq , Boffelli Dario , Zwick Rachel K. , Klein Ophir D. , Tse Ming , Donowitz Mark , Singh Varsha 
Published: February 2025

SUMMARYConstipation causes significant morbidity in Cystic Fibrosis (CF) patients. Using CF patient (F508del) derivedex vivoileal and distal colonic/rectal enteroids as a model and the Forskolin Induced Swelling Assay (FIS), we compared CFTR mediated fluid secretion in human enterocytes across the crypt-villus axis.CFTR expression and FIS decreased as enterocytes differentiated from crypt to become partially differentiated and then mature villus cells. While there was no FIS response in undifferentiated (crypt enterocytes) F508del-CF enteroids, partially differentiated F508del-CF enteroids had a swelling response to forskolin (cAMP) and linaclotide (cGMP) which was ∼48%, and ∼67% of the response in healthy enteroids, respectively and was prevented by a CFTR inhibitor. Also, linaclotide and a general PDE inhibitor independently enhanced combined CFTR-modulator-induced FIS response from partially differentiated F508del-CF enteroids. These findings demonstrate that partially differentiated ileal and distal colonic F508del-CFTR enteroids can be stimulated to secrete fluid by cAMP and cGMP.
CK2 directly controls CARD9 protein homeostasis
Authors: Kelly Aya M. , Merselis Leidy C. , Causton Benjamin , Calder Olivia K. , Chao Hoson , Prack Margaret , Stine Laurel B. , Amako Yuka , Wang Tao , Yesilkanal Ali E. , Condon Kendall J. , Pierce Meekyum O. , Watterson Scott H. , Guernon Jason , Anand Amit , Selvam Sabariya , Dagde Sayali , Holzinger Emily , Piasecki Lauren , Pemberton Travis A. , Fu Emily , Joint McKenna , Sandt Hayden , Singh Ashish K. , Wang Tai , Cobb Denise , Ni Xiaochun , Davies Geraint H. M. , Corin Alan F. , Roy Sophie , Wilson Stephen C. 
Published: February 2025

SUMMARYCARD9 is an attractive target for therapeutic intervention because the human genetic data provides strong evidence for the causal role of CARD9 in both protection and susceptibility to autoimmune disease. Expression quantitative trait loci (eQTLs) link higher CARD9 expression to increased disease risk and lower CARD9 expression to protection. Additionally, a rare allele variant leading to a C-terminal truncation of CARD9 (CARD9Δ11) and subsequent loss-of-function is also protective for inflammatory bowel disease (IBD). The mechanism of CARD9-driven inflammation through scaffold assembly with BCL10 and MALT1 (CBM complex) suggests a durable inflammatory signal driven by increasing levels of CARD9. Therefore, CARD9 depletion is a desired therapeutic strategy for drug discovery, yet a difficult one due to the nature of CARD9 as an adaptor protein target and the limited number of chemical tools available to engage it. Here, we uncover through a protein homeostasis screen that casein kinase 2 (CK2/CSNK2) inhibition leads to cellular CARD9 depletion. Following up with arrayed CRISPR screening, we identify key casein kinase isoforms/subunits responsible for CARD9 depletion. We find that CK2 directly binds to CARD9 and phosphorylates S424/S425 as well as S483/S484. Orthosteric CK2 inhibition prevents CK2 binding to CARD9 and leads to CARD9 destabilization. We show that the interaction between CK2 and CARD9Δ11 is significantly attenuated and not sensitive to CK2-mediated protein stabilization. The CK2-driven CARD9 depletion mechanism is preserved outside of immortalized cell lines and conserved between primary, differentiated mouse and human dendritic cells. Finally, we demonstrate therapeutic proof of conceptin vivousing CK2 inhibition to deplete CARD9 in murine whole blood, peritoneum, and colon. Our study expands the scope of cellular consequences dealt by kinase inhibition, offers an unconventional approach for engaging a therapeutically intractable target, and identifies a novel mechanism that could contribute to disease protection conferred by the CARD9Δ11 allele.
Accurate PROTAC targeted degradation prediction with DegradeMaster
Authors: Liu Jie , Roy Michael , Isbel Luke , Li Fuyi 
Published: February 2025

AbstractMotivationProteolysis-targeting chimeras (PROTACs) are heterobifunctional molecules that can degrade ‘undruggable’ protein of interest (POI) by recruiting E3 ligases and hijacking the ubiquitin-proteasome system. Some efforts have been made to develop deep learning-based approaches to predict the degradation ability of a given PROTAC. However, existing deep learning methods either simplify proteins and PROTACs as 2D graphs by disregarding crucial 3D spatial information or exclusively rely on limited labels for supervised learning without considering the abundant information from unlabeled data. Nevertheless, considering the potential to accelerate drug discovery, it is critical to develop more accurate computational methods for PROTAC-targeted protein degradation prediction.ResultsThis study proposes DegradeMaster, a semi-supervised E(3)-equivariant graph neural network-based predictor for targeted degradation prediction of PROTACs. DegradeMaster leverages an E(3)-equivariant graph encoder to incorporate 3D geometric constraints into the molecular representations and utilises a memory-based pseudo-labeling strategy to enrich annotated data during training. A mutual attention pooling module is also designed for interpretable graph representation. Experiments on both supervised and semi-supervised PROTAC datasets demonstrate that DegradeMaster outperforms state-of-the-art baselines, with substantial improvement of AUROC by 10.5%. Case studies show DegradeMaster achieves 88.33% and 77.78% accuracy in predicting the degradability of VZ185 candidates on BRD9 and ACBI3 on KRAS mutants. Visualization of attention weights on 3D molecule graph demonstrates that DegradeMaster recognises linking and binding regions of warhead and E3 ligands and emphasizes the importance of structural information in these areas for degradation prediction. Together, this shows the potential for cutting-edge tools to highlight functional PROTAC components, thereby accelerating novel compound generation.AvailabilityThe source code and datasets are available athttps://github.com/Jackson117/DegradeMasterandhttps://zenodo.org/records/14715718.
Identifying Sex Differences in Lung Adenocarcinoma Using Multi-Omics Integrative Protein Signaling Networks
Authors: Chen Chen , Saha Enakshi , Fischer Jonas , Ben Guebila Marouen , Fanfani Viola , Shutta Katherine H. , Padi Megha , Glass Kimberly , DeMeo Dawn L. , Lopes-Ramos Camila M. , Quackenbush John 
Published: February 2025

AbstractLung adenocarcinoma (LUAD) exhibits differences between the sexes in incidence, prognosis, and therapy, suggesting underexplored molecular mechanisms. We conducted an integrative multi-omics analysis using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) datasets to contrast transcriptomes and proteomes between sexes. We used TIGER to analyze TCGA-LUAD expression data and found sex-biased activity of transcription factors (TFs); we used PTM-SEA with CPTAC-LUAD proteomics data and found sex-biased kinase activity. We combined these to construct a kinase-TF signaling network and discovered druggable pathways linked to cancer-related processes. We also found significant sex biases in clinically relevant TFs and kinases, including NR3C1, AR, and AURKA. Using the PRISM drug screening database, we identified potential sex-specific drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors. Our findings emphasize the importance of considering sex and using multi-omics network methods to discover personalized cancer therapies.
Assessing Genotype-Phenotype Correlations with Deep Learning in Colorectal Cancer: A Multi-Centric Study
Authors: Gustav Marco , van Treeck Marko , Reitsam Nic G. , Carrero Zunamys I. , Loeffler Chiara M. , Meneghetti Asier Rabasco , Märkl Bruno , Boardman Lisa A. , French Amy J. , Goode Ellen L. , Gsur Andrea , Brezina Stefanie , Gunter Marc J. , Murphy Neil , Hönscheid Pia , Sperling Christian , Foersch Sebastian , Steinfelder Robert , Harrison Tabitha , Peters Ulrike , Phipps Amanda , Kather Jakob Nikolas 
Published: February 2025

AbstractBackgroundDeep Learning (DL) has emerged as a powerful tool to predict genetic biomarkers directly from digitized Hematoxylin and Eosin (H&E) slides in colorectal cancer (CRC). However, few studies have systematically investigated the predictability of biomarkers beyond routinely available alterations such as microsatellite instability (MSI), andBRAFandKRASmutations.MethodsOur primary dataset comprised H&E slides of CRC tumors across five cohorts totaling 1,376 patients who underwent comprehensive panel sequencing, with an additional 536 patients from two public datasets for validation. We developed a DL model using a single transformer model to predict multiple genetic alterations directly from the slides. The model’s performance was compared against conventional single-target models, and potential confounders were analyzed.FindingsThe multi-target model was able to predict numerous biomarkers from pathology slides, matching and partly exceeding single-target transformers. The Area Under the Receiver Operating Characteristic curve (AUROC, mean ± std) on the primary external validation cohorts was:BRAF(0·78 ± 0·01), hypermutation (0·88 ± 0·01), MSI (0·93 ± 0·01),RNF43(0·86 ± 0·01); this biomarker predictability was mirrored across metrics and co-occurrence analyses. However, biomarkers with high AUROCs largely correlated with MSI, with model predictions depending considerably on MSI-associated morphology upon pathological examination.InterpretationOur study demonstrates that multi-target transformers can predict the biomarker status for numerous genetic alterations in CRC directly from H&E slides. However, their pre-dictability is mainly associated with MSI phenotype, despite indications of slight biomarker-inherent contributions to a phenotype. Our findings underscore the need to analyze confounders in AI-based oncology biomarkers. To enable this, we developed a validated model applicable to other cancers and larger, diverse datasets.FundingThe German Federal Ministry of Health, the Max-Eder-Programme of German Cancer Aid, the German Federal Ministry of Education and Research, the German Academic Exchange Service, and the EU.
Transcription factor 19 is an androgen responsive gene that modulates vessel homeostasis and sustains metastatic prostate cancer
Authors: Ercilla Amaia , Crespo Jana R. , Garcia-Longarte Saioa , Fidalgo Marta , Martin-Martin Natalia , Carlevaris Onintza , Astobiza Ianire , Fernández-Ruiz Sonia , Guiu Marc , Bárcena Laura , Mendizabal Isabel , Aransay Ana M. , Graupera Mariona , Gomis Roger R. , Carracedo Arkaitz 
Published: February 2025

AbstractProstate cancer is a prevalent tumor type that, despite being highly curable, progresses to metastatic disease in a fraction of patients, thus accounting for more than 350.000 annual deaths worldwide. In turn, uncovering the molecular insights of metastatic disease is instrumental to improve the survival rate of prostate cancer patients. By means of gene expression metanalysis in multiple prostate cancer patient cohorts, we identified a set of genes that are differentially expressed in aggressive prostate cancer.Transcription factor 19 (TCF19)stood out as an unprecedented epithelial gene upregulated in metastatic disease, with prognostic potential and associated with the activity of androgen receptor. By combining computational and empiric approaches, our data revealed that TCF19 is required for full metastatic capacity and its depletion influences core cancer-related processes, such as vascular permeability, supporting the role of this gene in the dissemination of prostate tumor cells.
scKAN: Interpretable Single-cell Analysis for Cell-type-specific Gene Discovery and Drug Repurposing via Kolmogorov-Arnold Networks
Authors: He Haohuai , Tang Zhenchao , Chen Guanxing , Xu Fan , Hu Yao , Feng Yinglan , Wu Jibin , Huang Yu-An , Huang Zhi-An , Tan Kay Chen 
Published: February 2025

AbstractSingle-cell analysis has revolutionized our understanding of cellular heterogeneity, yet current approaches face challenges in efficiency and interpretability. In this study, we present scKAN, a framework that leverages Kolmogorov-Arnold Networks for interpretable single-cell analysis through three key innovations: efficient knowledge transfer from large language models through a lightweight distillation strategy; systematic identification of cell-type-specific functional gene sets through KAN’s learned activation curves; and precise marker gene discovery enabled by KAN’s importance scores with potential for drug repurposing applications. The model achieves superior performance on cell-type annotation with a 6.63% improvement in macro F1 score compared to state-of-the-art methods. Furthermore, scKAN’s learned activation curves and importance scores provide interpretable insights into cell-type-specific gene patterns, facilitating both gene set identification and marker gene discovery. We demonstrate the practical utility of scKAN through a case study on pancreatic ductal adenocarcinoma, where it successfully identified novel therapeutic targets and potential drug candidates, including Doconexent as a promising repurposing candidate. Molecular dynamics simulations further validated the stability of the predicted drug-target complexes. Our approach offers a comprehensive framework for bridging single-cell analysis with drug discovery, accelerating the translation of single-cell insights into therapeutic applications.
Dynamicµ-PBWT: Dynamic Run-length Compressed PBWT for Biobank Scale Data
Authors: Shakya Pramesh , Sanaullah Ahsan , Zhi Degui , Zhang Shaojie 
Published: February 2025

AbstractDurbin’s positional Burrows-Wheeler transform (PBWT) supports efficient haplotype matching and queries given a panel of haplotypes. It has been widely used for statistical phasing, imputation and identity-by-descent (IBD) detection. However, the original PBWT panel doesn’t support dynamic updates when haplotypes need to be added or deleted from the panel. Dynamic-PBWT (d-PBWT) solved this problem but it is not memory efficient. While the memory constraint problem of the PBWT has been tackled by Syllable-PBWT andµ-PBWT, these are static data structures that do not allow updates. Additionally, Syllable-PBWT only supports long-match query andµ-PBWT only supports set-maximal match query, limiting their functionality in the compressed form. In this paper, we present Dynamicµ-PBWT (which can also be seen as compressed d-PBWT) that is memory efficient and supports dynamic updates. We run-length compress PBWT to achieve better compression rate and store the runs in the self-balancing trees to enable dynamic updates. We show that the number of updates per insertion or deletion in the tree at each site is constant regardless of the number of haplotypes in the panel and the updates can be made without decompressing the index. In addition, we use orders of magnitude less memory than d-PBWT. We also provide a long match query algorithm that can easily be extended back to the originalµ-PBWT. Overall, the flexibility and space-efficiency of Dynamicµ-PBWT makes it a potential index data structure for biobank scale genetic data analyses. The source code for Dynamicµ-PBWT is available athttps://github.com/ucfcbb/Dynamic-mu-PBWT.
Peptide-induced hydration of lipid bilayers modulates packing pattern and conformations of hydrocarbon chains – a potential pathway for peptide translocation?
Authors: Pašalić Lea , Pem Barbara , Jakas Andreja , Čikoš Ana , Groznica Nikolina , Mlinarić Tihana , Accorsi Matilde , Mangiarotti Agustín , Dimova Rumiana , Bakarić Danijela 
Published: February 2025

AbstractCell-penetrating peptides (CPPs) with a cationic-hydrophobic character are recognized as carriers for delivering various therapeutics and diagnostic agents across cell membranes and into the cells. Among the most studied CPPs, nona-arginine (R9) exhibits superior penetration compared to nona-lysine (K9), suggesting that the penetration ability depends not only on charge, distribution and concentration of peptides but also on the lipid membrane composition. However, for heptapeptides composed of arginine (R), lysine (K) and phenylalanine (F) residues, which show some CPPs properties, these interactions remain unexplored. This study sheds light on the adsorption of R5F2/K5F2 on model prokaryotic (PRO) and eukaryotic (EU) lipid membranes containing a zwitterionic lipid (phosphatidylcholine; PC) and an anionic lipid (either phosphatidylglycerol, PG, in the PRO model, or phosphatidylserine, PS in EU) at the 90:10 molar ratio. Using differential scanning calorimetry (DSC) and temperature-dependent UV-Vis spectroscopy, we observed peptide-induced changes in the interfacial water layer that affect the fluidity and rigidity of lipid bilayers. The distinct adsorption behavior of R5F2/K5F2 on PRO and EU lipid bilayers revealed the changes in lipid packing and hydrocarbon chain conformations as exclusively peptide-dependent features. The peptide-induced formation of vacancies in the non-polar bilayer part is consistent with partial leakage observed in giant unilamellar vesicles. The synchronized arrangement could represent a mechanism for the concerted translocation of CPPs, along with their potential cargo across the lipid membrane. This study provides new insights into the peptide-lipid interactions underlying CPPs functionality.
A Benchmarking Study of Random Projections and Principal Components for Dimensionality Reduction Strategies in Single Cell Analysis
Authors: Abdelnaby Mohamed , Moussa Marmar R. 
Published: February 2025

AbstractPrincipal Component Analysis (PCA) has long been a cornerstone in dimensionality reduction for high-dimensional data, including single-cell RNA sequencing (scRNA-seq). However, PCA’s performance typically degrades with increasing data size, can be sensitive to outliers, and assumes linearity. Recently, Random Projection (RP) methods have emerged as promising alternatives, addressing some of these limitations. This study systematically and comprehensively evaluates PCA and RP approaches, including Singular Value Decomposition (SVD) and randomized SVD, alongside Sparse and Gaussian Random Projection algorithms, with a focus on computational efficiency and downstream analysis effectiveness. We benchmark performance using multiple scRNA-seq datasets including labeled and unlabeled publicly available datasets. We apply Hierarchical Clustering and Spherical K-Means clustering algorithms to assess downstream clustering quality. For labeled datasets, clustering accuracy is measured using the Hungarian algorithm and Mutual Information. For unlabeled datasets, the Dunn Index and Gap Statistic capture cluster separation. Across both dataset types, the Within-Cluster Sum of Squares (WCSS) metric is used to assess variability. Additionally, locality preservation is examined, with RP outperforming PCA in several of the evaluated metrics. Our results demonstrate that RP not only surpasses PCA in computational speed but also rivals and, in some cases, exceeds PCA in preserving data variability and clustering quality. By providing a thorough benchmarking of PCA and RP methods, this work offers valuable insights into selecting optimal dimensionality reduction techniques, balancing computational performance, scalability, and the quality of downstream analyses.
Blender tissue cartography: an intuitive tool for the analysis of dynamic 3D microscopy data
Authors: Claussen Nikolas , Regis Cécile , Wopat Susan , Streichan Sebastian 
Published: February 2025

AbstractTissue cartography extracts and cartographically projects surfaces from volumetric biological image data. This turns 3D-into 2D data which is much easier to visualize, analyze, and computationally process. Tissue cartography has proven particularly useful in developmental biology by taking advantage of the sheet-like organization of many biological tissues. However, existing software tools for tissue cartography are limited in the type of geometries they can handle and difficult for non-experts to use and extend. Here, we describeblender_tissue_cartography(btc), a tissue cartography add-on for the popular 3D creation software Blender.btcmakes tissue cartography user-friendly via a graphical user interface and harnesses powerful algorithms from the computer graphics community for biological image analysis. ThebtcGUI enables interactive analysis and visualization without requiring any programming expertise, while an accompanying Python library allows expert users to create custom analysis pipelines. Both the add-on and the Python library are highly modular and fully documented, including interactive Jupyter Notebook tutorials.btcfeatures a general-purpose pipeline for time-lapse data in which the user graphically defines a cartographic projection for a singlekey frame, which is propagated to all other frames via surface-to-surface alignment algorithms. Thebtcdifferential geometry module allows mathematically correcting for cartographic distortion, enabling faithful 3D measurements in 2D cartographic projections, including for vector fields like tissue flow fields. We demonstratebtcon diverse and complex tissue shapes fromDrosophila, stem-cell-based organoids,Arabidopsis, and zebrafish.