bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tetragonia tetragonioides extract prevented high-fat-diet-induced obesity and changed hepatic and adipose transcriptomic signatures in C57BL/6J male mice
Authors: Oh Hea Ry , Park Yong Hyun , Hong Hye Ryeong , Kim Hyun Jin , Park Jinbong , Han Yohan , Ko Seong-Gyu , Shin Eui Cheol , Kim Tae Gyun , Cho Hyung Taek , Pan Jeong Hoon , Shin Hyo Ri , Shim Youn Young , Reaney Martin J T , Cho Tae Jin , Hong Ji Youn , Kim Young Jun , Han Bok Kyung , Lee Geung-Joo , Lee Kangwook , Do Seon Gil , Kim Jae Kyeom 
Published: January 2025

Abstract
Obesity, often driven by high-fat diets (HFDs), is a major global health issue, necessitating effective preventive measures. Tetragonia tetragonoides, a plant with known medicinal properties, has not been extensively studied for its effects on HFD-induced obesity and related genetic changes in mice. This study explores the impact of T. tetragonoides extract (TTE; 300 mg/kg) on obesity-related traits in C57BL/6J male mice, with a focus on transcriptomic changes in the liver and white adipose tissue (WAT). Over 8 weeks, TTE supplementation led to significant reductions in obesity-related phenotypes and modulated gene expression altered by HFD. Key genes like Cd180 and MUPs, linked to immune responses and lipid metabolism, were notably influenced by TTE. The study highlighted TTE’s effects on lipid metabolism pathways in the liver and immune processes in WAT, underscoring its potential as an anti-obesity agent, while advocating for further research into its bioactive components.
PbsNRs: predict the potential binders and scaffolds for nuclear receptors
Authors: Zheng Genhui , Wu Dingfeng , Wei Xiuxia , Xu Dongpo , Mao Tiantian , Yan Deyu , Han Wenhao , Shang Xiaoxiao , Chen Zikun , Qiu Jingxuan , Tang Kailin , Cao Zhiwei , Qiu Tianyi 
Published: January 2025

Abstract
Nuclear receptors (NRs) are a class of essential proteins that regulate the expression of specific genes and are associated with multiple diseases. In silico methods for prescreening potential NR binders with predictive binding ability are highly desired for NR-related drug development but are rarely reported. Here, we present the PbsNRs (Predicting binders and scaffolds for Nuclear Receptors), a user-friendly web server designed to predict the potential NR binders and scaffolds through proteochemometric modeling. The utility of PbsNRs was systemically evaluated using both chemical compounds and natural products. Results indicated that PbsNRs achieved a good prediction performance for chemical compounds on internal (ROC-AUC = 0.906, where ROC is Receiver-Operating Characteristic curve and AUC is the Area Under the Curve) and external (ROC-AUC = 0.783) datasets, outperforming both compound–ligand interaction tools and NR-specific predictors. PbsNRs also successfully identified bioactive chemical scaffolds for NRs by screening massive natural products. Moreover, the predicted bioactive and inactive natural products for NR2B1 were experimentally validated using biosensors. PbsNRs not only aids in screening potential therapeutic NR binders but also helps discover the essential molecular scaffold and guide the drug discovery for multiple NR-related diseases. The PbsNRs web server is available at http://pbsnrs.badd-cao.net.
Supervised analysis of alternative polyadenylation from single-cell and spatial transcriptomics data with spvAPA
Authors: Zhang Qinglong , Kang Liping , Yang Haoran , Liu Fei , Wu Xiaohui 
Published: January 2025

Abstract
Alternative polyadenylation (APA) is an important driver of transcriptome diversity that generates messenger RNA isoforms with distinct 3′ ends. The rapid development of single-cell and spatial transcriptomic technologies opened up new opportunities for exploring APA data to discover hidden cell subpopulations invisible in conventional gene expression analysis. However, conventional gene-level analysis tools are not fully applicable to APA data, and commonly used unsupervised dimensionality reduction methods often disregard experimentally derived annotations such as cell type identities. Here, we proposed a supervised analytical framework termed spvAPA, specifically used for APA analysis from both single-cell and spatial transcriptomics data. First, an iterative imputation method based on weighted nearest neighbor was designed to recover missing APA signatures, by integrating both gene expression and APA modalities. Second, a supervised feature selection method based on sparse partial least squares discriminant analysis was devised to identify APA features distinguishing cell types or spatial morphologies. Additionally, spvAPA improves the visualization of high-dimensional data for discovering novel cell subtypes, which considers APA features and dual modalities of gene expression and APA. Evaluations across nine single-cell and spatial transcriptomics datasets demonstrate the effectiveness and applicability of spvAPA. spvAPA is available at https://github.com/BMILAB/spvAPA.
PhyloMix: enhancing microbiome-trait association prediction through phylogeny-mixing augmentation
Authors: Jiang Yifan , Liao Disen , Zhu Qiyun , Lu Yang Young 
Published: January 2025

Abstract

Motivation
Understanding the associations between traits and microbial composition is a fundamental objective in microbiome research. Recently, researchers have turned to machine learning (ML) models to achieve this goal with promising results. However, the effectiveness of advanced ML models is often limited by the unique characteristics of microbiome data, which are typically high-dimensional, compositional, and imbalanced. These characteristics can hinder the models’ ability to fully explore the relationships among taxa in predictive analyses. To address this challenge, data augmentation has become crucial. It involves generating synthetic samples with artificial labels based on existing data and incorporating these samples into the training set to improve ML model performance.

Results
Here, we propose PhyloMix, a novel data augmentation method specifically designed for microbiome data to enhance predictive analyses. PhyloMix leverages the phylogenetic relationships among microbiome taxa as an informative prior to guide the generation of synthetic microbial samples. Leveraging phylogeny, PhyloMix creates new samples by removing a subtree from one sample and combining it with the corresponding subtree from another sample. Notably, PhyloMix is designed to address the compositional nature of microbiome data, effectively handling both raw counts and relative abundances. This approach introduces sufficient diversity into the augmented samples, leading to improved predictive performance. We empirically evaluated PhyloMix on six real microbiome datasets across five commonly used ML models. PhyloMix significantly outperforms distinct baseline methods including sample-mixing-based data augmentation techniques like vanilla mixup and compositional cutmix, as well as the phylogeny-based method TADA. We also demonstrated the wide applicability of PhyloMix in both supervised learning and contrastive representation learning.

Availability and implementation
The Apache-licensed source code is available at (https://github.com/batmen-lab/phylomix).
Funmap: integrating high-dimensional functional annotations to improve fine-mapping
Authors: Li Yuekai , Xiao Jiashun , Ming Jingsi , Zeng Yicheng , Cai Mingxuan 
Published: January 2025

Abstract

Motivation
Fine-mapping aims to prioritize causal variants underlying complex traits by accounting for the linkage disequilibrium of genome-wide association study risk locus. The expanding resources of functional annotations serve as auxiliary evidence to improve the power of fine-mapping. However, existing fine-mapping methods tend to generate many false positive results when integrating a large number of annotations.

Results
In this study, we propose a unified method to integrate high-dimensional functional annotations with fine-mapping (Funmap). Funmap can effectively improve the power of fine-mapping by borrowing information from hundreds of functional annotations. Meanwhile, it relates the annotation to the causal probability with a random effects model that avoids the over-fitting issue, thereby producing a well-controlled false positive rate. Paired with a fast algorithm, Funmap enables scalable integration of a large number of annotations to facilitate prioritizing multiple causal single nucleotide polymorphisms. Our comprehensive simulations across a wide range of annotation relevance settings demonstrate that Funmap is the only method that produces well-calibrated false discovery rate under the setting of high-dimensional annotations while achieving better or comparable power gains as compared to existing methods. By integrating genome-wide association studies of 4 lipid traits with 187 functional annotations, Funmap consistently identified more variants that can be replicated in an independent cohort, achieving 15.5%–26.2% improvement over the runner-up in terms of replication rate.

Availability and implementation
The Funmap software and all analysis code are available at https://github.com/LeeHITsz/Funmap.
PUPMCR: an R package for image-based identification of color based on Rayner’s (1970) terminology and known fungal pigments
Authors: Zapanta Niña Rose , Santos Rhenz Hannah , Pineda Jericho Ivan , Pedrosa Jireh Sealtiel , Rabelas Kristine Joyce , Samontan Charina , Alvarez Lourdes , Deocaris Chester 
Published: January 2025

Abstract
Fungi are eukaryotic organisms grouped based on different traits of their morphology. In 1970, R. W. Rayner published A Mycological Colour Chart to provide a standardized system for identifying color in fungi. While its terminologies have contributed a standard way of color matching for taxonomic diagnoses, this method using the personal color perception of the observer does not guarantee accuracy. Considering the diversity of fungi, visual color matching is expected to be challenging without a standard assisting instrument. In this study, the R package PUPMCR is developed to approximate the color name and associated pigments of fungal species based on the pixel coordinates of its uploaded image. This software utilizes CIELAB and RGB color spaces as well as Euclidean and Chi-square distance metric systems. The package is tested and validated using 300 fungal images as a dataset for conducting interrater reliability tests. Results showed the highest agreement for parameters utilizing the RGB color space (Cohen’s kappa values: 0.655 ± 0.013 for RGB and Euclidean; 0.658 ± 0.004 for RGB and Chi-square), attributed to its computational efficiency, which facilitates more uniform binning and universally scaled distance metrics. The produced color-identifying tool is also available as a Shiny web application (https://pupmcr.shinyapps.io/PUPMCR/) to allow better accessibility for users on the World Wide Web. The development of PUPMCR not only benefits a variety of users from its free accessibility but also provides a more reliable color identification system in the field of mycology.
Robust Bayesian graphical regression models for assessing tumor heterogeneity in proteomic networks
Authors: Yao Tsung-Hung , Ni Yang , Bhadra Anindya , Kang Jian , Baladandayuthapani Veerabhadran 
Published: January 2025

ABSTRACT
Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of two canonical assumptions: (i) a homogeneous graph with a common network for all subjects or (ii) an assumption of normality, especially in the context of Gaussian graphical models. Both assumptions are restrictive and can fail to hold in certain applications such as proteomic networks in cancer. To this end, we propose an approach termed robust Bayesian graphical regression (rBGR) to estimate heterogeneous graphs for non-normally distributed data. rBGR is a flexible framework that accommodates non-normality through random marginal transformations and constructs covariate-dependent graphs to accommodate heterogeneity through graphical regression techniques. We formulate a new characterization of edge dependencies in such models called conditional sign independence with covariates, along with an efficient posterior sampling algorithm. In simulation studies, we demonstrate that rBGR outperforms existing graphical regression models for data generated under various levels of non-normality in both edge and covariate selection. We use rBGR to assess proteomic networks in lung and ovarian cancers to systematically investigate the effects of immunogenic heterogeneity within tumors. Our analyses reveal several important protein–protein interactions that are differentially associated with the immune cell abundance; some corroborate existing biological knowledge, whereas others are novel findings.
The pathogenesis and management of heatstroke and heatstroke-induced lung injury
Authors: Liu Jian , Li Qin , Zou Zhimin , Li Li , Gu Zhengtao 
Published: January 2025

Abstract
In the past two decades, record-breaking heat waves have caused an increasing number of heat-related deaths, including heatstroke, globally. Heatstroke is a life-threatening systemic condition characterized by a core body temperature &gt;40°C and the subsequent development of multiple organ dysfunction syndrome. Lung injury is a well-documented complication of heatstroke and is usually the secondary cause of patient death. In recent years, extensive research has been conducted to investigate the underlying causes of heatstroke and heatstroke-induced lung injury. This review aims to consolidate and present the current understanding of the key pathogenic mechanisms involved in heatstroke and heatstroke-induced lung injury. In addition, systemic factors such as heat cytotoxicity, systemic inflammation, oxidative stress, endothelial cell dysfunction, and other factors are involved in the pathogenesis of lung injury in heatstroke. Furthermore, we also established current management strategies for heatstroke and heatstroke-induced lung injury. However, further investigation is required to fully understand the detailed pathogenesis of heatstroke so that potentially effective means of treating and preventing heatstroke and heatstroke-induced lung injury can be developed and studied.
Childhood-onset inflammatory bowel disease and chronic non-bacterial osteomyelitis: a Swedish nationwide cohort study 2002-2022
Authors: Malmquist Marianne , Voghera Siri ,   The SWIBREG Study Group, Berg Stefan , Saalman Robert , Olén Ola 
Published: January 2025

Abstract

Background and Aims
Nationwide, population-based studies of chronic non-bacterial osteomyelitis (CNO) in patients with childhood-onset inflammatory bowel disease (IBD) are lacking.

Methods
We used nationwide registers to identify all children in Sweden diagnosed with IBD during 2002-2022 and the occurrence of CNO in this IBD cohort and general population non-IBD comparators. To estimate the temporal associations between IBD and CNO we used Cox regression. We compared clinical data for IBD patients with CNO (IBD+CNO) and the IBD patients without CNO.

Results
We identified 8244 children with IBD and 82 400 non-IBD comparators. At IBD diagnosis, CNO had been diagnosed in 0.13% (11/8244) of the IBD cohort and 0.03% (26/82 400) of the non-IBD comparators. During follow-up, 13 additional CNO cases occurred in the IBD cohort and 22 in the non-IBD comparators (adjusted hazard ratio = 5.87 [95% CI 2.95-11.66]). The prevalence of CNO among all prevalent children with IBD and prevalent matched non-IBD comparators December 31, 2022 was 0.48% (9/1885) and 0.02% (4/18 567), respectively. Median age at IBD diagnosis was lower in IBD + CNO compared to IBD without CNO (11 vs 14 years [−3 years, 95% CI −5 to −1]). Extraintestinal manifestations (except CNO) were more frequent in IBD + CNO (62% vs 21%, P &lt; .0001). Treatment with biologics was more common in the IBD + CNO group (78% vs 44%, P = .004), prescribed for IBD and/or CNO.

Conclusions
We found a 6-fold increased risk of CNO in childhood-onset IBD compared to non-IBD comparators. Patients with IBD + CNO are characterized by younger age at IBD onset, more frequent extraintestinal manifestations, and higher usage of biologics.
Conserved ancillary residues situated proximally to the VIM-2 active-site affect its metallo β-lactamase activity
Authors: Jain Diamond , Ajith Tejavath , Verma Jyoti , Chatterjee Debasmita , Ghosh Anindya S 
Published: January 2025

Abstract
Verona-integron-metallo-β-lactamase (VIM-2) is one of the most widespread class B β-lactamase responsible for β-lactam resistance. Although active-site residues help in metal binding, the residues nearing the active-site possess functional importance. Here, to decipher the role of such residues in the activity and stability of VIM-2, the residues E146, D182, N210, S207, and D213 were selected through in-silico analyses and substituted with alanine using site-directed mutagenesis. The effects of substitution mutations were assessed by comparing the changes in β-lactam susceptibility pattern of Escherichia coli host cell expressing VIM-2 and its mutated proteins. VIM-2_N210A enhanced the susceptibility of the host by ∼4–8 folds against penicillins and cephalosporins, while the expression of VIM-2_D182A radically increased the susceptibility of host. However, expression of VIM-2_E146A reduced the susceptibility of host by 2-fold. Further, proteins were purified to homogeneity, and VIM_N210A and VIM_D182A displayed reduced thermal stability than VIM-2. Moreover, in vitro catalytic efficiencies of VIM-2_D182A were drastically reduced against all the β-lactams tested whereas the same were moderately reduced for VIM-2_N210A. Conversely, the catalytic efficiency was marginally altered for VIM_E146A. Overall, we infer that both N210A and D182A substitutions negatively affect the performance of VIM-2 by influencing substrate specificity and stability, respectively.
Similar, but not the same: multiomics comparison of human valve interstitial cells and osteoblast osteogenic differentiation expanded with an estimation of data-dependent and data-independent PASEF proteomics
Authors: Lobov Arseniy , Kuchur Polina , Boyarskaya Nadezhda , Perepletchikova Daria , Taraskin Ivan , Ivashkin Andrei , Kostina Daria , Khvorova Irina , Uspensky Vladimir , Repkin Egor , Denisov Evgeny , Gerashchenko Tatiana , Tikhilov Rashid , Bozhkova Svetlana , Karelkin Vitaly , Wang Chunli , Xu Kang , Malashicheva Anna 
Published: January 2025

Abstract
Osteogenic differentiation is crucial in normal bone formation and pathological calcification, such as calcific aortic valve disease (CAVD). Understanding the proteomic and transcriptomic landscapes underlying this differentiation can unveil potential therapeutic targets for CAVD. In this study, we employed RNA sequencing transcriptomics and proteomics on a timsTOF Pro platform to explore the multiomics profiles of valve interstitial cells (VICs) and osteoblasts during osteogenic differentiation. For proteomics, we utilized 3 data acquisition/analysis techniques: data-dependent acquisition (DDA)–parallel accumulation serial fragmentation (PASEF) and data-independent acquisition (DIA)–PASEF with a classic library-based (DIA) and machine learning–based library-free search (DIA-ML). Using RNA sequencing data as a biological reference, we compared these 3 analytical techniques in the context of actual biological experiments. We use this comprehensive dataset to reveal distinct proteomic and transcriptomic profiles between VICs and osteoblasts, highlighting specific biological processes in their osteogenic differentiation pathways. The study identified potential therapeutic targets specific for VICs osteogenic differentiation in CAVD, including the MAOA and ERK1/2 pathway. From a technical perspective, we found that DIA-based methods demonstrate even higher superiority against DDA for more sophisticated human primary cell cultures than it was shown before on HeLa samples. While the classic library-based DIA approach has proved to be a gold standard for shotgun proteomics research, the DIA-ML offers significant advantages with a relatively minor compromise in data reliability, making it the method of choice for routine proteomics.
Enhancing inbreeding estimation and global conservation insights through chromosome-level assemblies of the Chinese and Malayan pangolin
Authors: Lan Tianming , Tian Yinping , Shi Minhui , Liu Boyang , Lin Yu , Xia Yanling , Ma Yue , Sahu Sunil Kumar , Wang Qing , Li Jun , Chen Jin , Hou Fanghui , Yin Chuanling , Wang Kai , Fu Yuan , Que Tengcheng , Liu Wenjian , Liu Huan , Li Haimeng , Hua Yan 
Published: January 2025

Abstract
A high-quality reference genome coupled with resequencing data is a promising strategy to address issues in conservation genomics. This has greatly enhanced the development of conservation plans for endangered species. Pangolins are fascinating animals with a variety of unique features. Unfortunately, they are the most trafficked wild animal in the world. In this study, we assembled a chromosome-scale genome with HiFi long reads and Hi-C short reads for the Chinese and Malayan pangolin and provided two new representative reference genomes for the pangolin species. We found a great improvement in the evaluation of genetic diversity and inbreeding based on these high-quality genomes and obtained different results for the detection of genome-wide extinction risks compared with genomes assembled using short reads. Moderate inbreeding and genetic diversity were reverified in these two pangolin species, except for one Malayan pangolin population with high inbreeding and low genetic diversity. Moreover, we identified a much higher inbreeding level (FROH = 0.54) in the Chinese pangolin individual from Taiwan Province compared with that from Mainland China, but more than 99.6% runs of homozygosity (ROH) fragments were restricted to less than 1 Mb, indicating that the high FROH in Taiwan Chinese pangolins may have accumulated from historical inbreeding events. Furthermore, our study is the first to detect relatively mild genetic purging in pangolin populations. These two high-quality reference genomes will provide valuable genetic resources for future studies and contribute to the protection and conservation of pangolins.
Structural and Functional Glycosylation of the Abdala COVID-19 Vaccine
Authors: Burnap Sean A , Calvaresi Valeria , Cabrera Gleysin , Pousa Satomy , Limonta Miladys , Ramos Yassel , González Luis Javier , Harvey David J , Struwe Weston B 
Published: January 2025

Abstract
Abdala is a COVID-19 vaccine produced in Pichia pastoris and is based on the receptor-binding domain (RBD) of the SARS-CoV-2 spike. Abdala is currently approved for use in multiple countries with clinical trials confirming its safety and efficacy in preventing severe illness and death. Although P. pastoris is used as an expression system for protein-based vaccines, yeast glycosylation remains largely uncharacterised across immunogens. Here, we characterise N-glycan structures and their site of attachment on Abdala and show how yeast-specific glycosylation decreases binding to the ACE2 receptor and a receptor-binding motif (RBM) targeting antibody compared to the equivalent mammalian-derived RBD. Reduced receptor and antibody binding is attributed to changes in conformational dynamics resulting from N-glycosylation. These data highlight the critical importance of glycosylation in vaccine design and demonstrate how individual glycans can influence host interactions and immune recognition via protein structural dynamics.