Biology subjects

Wang, J.

Publications and source records attributed to Wang, J..

At least 757 records · Page 42Linked to original sources

bioRxiv2025

Cross-modal Denoising and Integration of Spatial Multi-omics data with CANDIES

Spatial multi-omics data offer a powerful framework for integrating diverse molecular profiles while maintaining the spatial organization of cells. However, inherent variations in data quality and noise levels across different modalities pose significant challenges to accurate integration and analyses. In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrates spatial multi-omics data. With our innovative model and algorithm designs, CANDIES not only enhances the quality of spatial multi-omics data, but also yields a unified and comprehensive joint representation, thereby empowering many downstream analysis. We conduct extensive evaluations on diverse synthetic and real datasets, including spatial CITE-seq data from human skin biopsy tissue, MISAR-seq data from the mouse brain, spatial ATAC-RNA-seq data from the mouse embryo and 10x visium data from human lymph nodes. CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain identification, spatiotemporal trajectories reconstruction, and spatial association mapping for complex human traits. In particular, we show that CANDIES representations can be integrated with the rich resources from genome-wide association studies (GWASs), allowing the spatial domains to be linked with complex human traits, yielding spatially resolved interpretation of complex traits in their relevant tissues.

bioinformatics↗

bioRxiv2025

Single-Cell Multimodal Profiling Highlights Persistent Aortic Smooth Muscle Cell Changes in Diabetic Mice Despite Glycemic Control

BackgroundType 2 diabetes (T2D) is associated with accelerated vascular complications like hypertension and atherosclerosis. "Phenotypic switching" of vascular smooth muscle cells (SMC), a major driver of these complications, is enhanced in diabetes. Despite adequate glycemic control, SMC dysfunction can persist due to "metabolic memory" of prior hyperglycemia. However, the mechanisms of hyperglycemic memory associated with persistent SMC dysfunction are unclear. Here, leveraging single-cell (sc) multi-omics, we examined the effect of glucose normalization on transcriptomic and epigenomic changes associated with SMC phenotypic transition in T2D mice. MethodsWe treated T2D db/db mice with the antidiabetic drug dapagliflozin (DAPA) (db/dbDAPA) or vehicle (db/db), and non-diabetic control db/+ mice with vehicle for 6 weeks. Dissected aortas were subjected to scRNA-seq, scATAC-seq, and spatial transcriptomics (Xenium) to determine single-cell changes in gene expression and chromatin accessibility. ResultsDAPA treatment conferred effective glycemic control in db/db mice, with significant reductions in blood glucose/hemoglobin A1c. scRNA and scATAC-seq analysis of aortas identified major cell populations, including SMC, fibroblasts, endothelial and immune cells. SMC were further clustered into 9 subtypes, including contractile and fibromyocyte-like cells. Cell composition analysis revealed decreases in contractile SMC and increases in vascular remodeling associated fibromyocyte-like SMC in db/db versus db/+ mice. Interestingly, DAPA did not reverse diabetes-induced decreases in contractile markers but reversed changes in several fibromyocyte markers in db/db mice. Pseudotime trajectory analysis revealed increased activities of fibromyocyte enriched transcription factors (TFs) during contractile to fibromyocyte transition. Furthermore, increased expression of TFs regulating fibromyocyte phenotype (e.g. Atf4, Bach1, Hand2, Fosl2) in db/db were partially reversed by DAPA, whereas reduced contractile TF (Mef2c) expression was unchanged. Spatial transcriptomics analysis further mapped aortic cell types within intact aortas and confirmed that DAPA reversed alterations in key fibromyocyte but not contractile genes in db/db mouse aortas. ConclusionsPersistent epigenetic changes may contribute to sustained vascular remodeling and dysfunction in T2D. T2D reduced contractile SMC gene expression and related chromatin accessibility and promoted phenotypic transition to fibromyocytes. These changes are only partially reversed by a widely used antidiabetic drug like DAPA, underscoring the need for more effective therapies that target hyperglycemic memory.

biochemistry↗

bioRxiv2025

Tnfrsf10 Signaling is Required to Maintain the Stem Cell Niche in the Zebrafish Lateral Line

Regenerative capacity varies significantly among species and across different organs. In mammals, the inner ear exhibits a limited ability to regenerate hair cells, whereas the lateral line sensory organ of zebrafish demonstrates apparently limitless capability to spontaneously regenerate hair cells following injury. However, the specific cell populations representing the stem cells in the neuromast and the mechanism of stem cell self-renewal remain unclear. In this study, we show that a mutation in tnfrsfa led to the depletion of neuromast sensory organs and impairment of hair cell regeneration in zebrafish. The decrease of cells peripheral to the neuromasts in tnfrsfa mutants indicates that these peripheral cells, particularly cells called "mantle cells", serve as the stem cells within the neuromast. Furthermore, our findings indicate that the ligands Tnfsf10 and Tnfsf10l3 (TRAIL) act through Tnfrsfa activating the NF-{kappa}B signaling pathway in the mantle cells in a non-cell-autonomous manner. NF-{kappa}B signaling is crucial for maintaining the stem cell identity of the mantle cells through activation or maintenance of sox2 expression. These findings demonstrate a critical role for TNF superfamily signaling in stem cell maintenance. HighlightsO_LIZebrafish tnfrsfa maintains the stem cell niche in neuromasts and is necessary for hair cell regeneration of the lateral line C_LIO_LITnfrsfa is essential for mantle cell proliferation during lateral line development and hair cell regeneration C_LIO_LITnfsf10 and Tnfsf10l3 signal through Tnfrsfa, activating NF-{kappa}B signaling and Sox2 expression in the mantle cells in a non-cell-autonomous manner C_LIO_LITnfrsfa regulates the development of neuromast independently of the Notch signaling pathway C_LI

developmental biology↗

bioRxiv2025

Expanding Targeted Instrumentation for Discovery Applications: Complement Reporter Ion Quantification with a Quadrupole-Ion Trap Instrument

Proteomics workflows have traditionally been divided into discovery-based and targeted approaches, with instrumentation optimized specifically for each. Discovery experiments typically utilize high-resolution analyzers while targeted workflows rely on the sensitivity and specificity of triple quadrupole systems. Recently, a quadrupole-ion trap instrument (Stellar MS) demonstrated superior performance for targeted applications compared to conventional triple quadrupoles. In this study, we expand the capabilities of this platform to multiplexed shotgun proteomics using complement reporter ion quantification in an ion trap (iTMTproC). Benchmarking experiments with defined standards show that iTMTproC achieves quantification accuracy and interference reduction comparable to MultiNotch MS3 on the Orbitrap Fusion Lumos, a dedicated quadrupole-ion trap-Orbitrap tribrid instrument optimized for this purpose. Notably, iTMTproC quantifies slightly more proteins than MultiNotch MS3. We further validate this approach through a developmental time-series analysis of frog embryos, obtaining proteomic data nearly indistinguishable from MultiNotch MS3, with slightly increased protein quantification depth. These findings significantly extend the functionality of targeted instrumentation, underscoring the versatility of quadrupole-ion trap systems and providing cost-effective access to highly accurate, multiplexed quantitative shotgun proteomics.

systems biology↗

bioRxiv2025

Cooperation Between Genotoxic Bacteria Accelerates Tumorigenesis in a Mouse Model of Colon Carcinogenesis

To identify causal links between gut microbes and tumorigenesis, we colonized groups of germ-free colon tumor-susceptible mice (ApcMin/+;Il10-/-) with 15 cultured human fecal microbiotas from healthy individuals, as well as patients with inflammatory bowel disease and colorectal cancer. The number of colonic tumors in ApcMin/+;Il10-/- mice varied by donor microbiota but not by the health status of the donor. In vitro screens of host cell proliferation, genotoxicity, and inflammation in bacteria-mammalian cell cocultures revealed that genotoxicity best predicted tumorigenic microbes in vivo with genotoxic microbes present in all tested individuals. The genotoxic subset of strains from each donor induced more tumors than the complete community - even when the complete community was not tumorigenic. Combining genotoxic microbes from multiple sources increased tumor number and decreased the time to tumor onset. Together these results suggest that most individuals harbor genotoxic bacterial strains and the balance of genotoxic to protective strains determines the timing and severity of tumorigenesis in vivo.

microbiology↗

bioRxiv2025

CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images

Image-based machine learning tools have emerged as powerful resources for analyzing medical images, with deep learning-based semantic segmentation commonly utilized to enable spatial quantification of structures in images. However, customization and training of segmentation algorithms requires advanced programming skills and intricate workflows, limiting their accessibility to many investigators. Here, we present a protocol and software for automatic segmentation of medical images guided by a graphical user interface (GUI) using the CODAvision algorithm. This workflow simplifies the process of semantic segmentation of microanatomical structures by enabling users to train highly customizable deep learning models without extensive coding expertise. The protocol outlines best practices for creating robust training datasets, configuring model parameters, and optimizing performance across diverse biomedical image modalities. CODAvision enhances the usability of the CODA algorithm (Nature Methods, 2022) by streamlining parameter configuration, model training, and performance evaluation, automatically generating quantitative results and comprehensive reports. We expand beyond the original implementation of CODA to serial histology by demonstrating robust performance across numerous medical image modalities and diverse biological questions. We provide sample results in data types including histology, magnetic resonance imaging (MRI), and computed tomography (CT). We demonstrate the diverse use of this tool in applications including quantification of metastatic burden in in vivo models and deconvolution of spot-based spatial transcriptomics datasets. This protocol is designed for researchers with interest in rapid design of highly customizable semantic segmentation algorithms and a basic understanding of programming and anatomy.

bioinformatics↗

bioRxiv2025

PepPCBench is a Comprehensive Benchmark for Protein-Peptide Complex Structure Prediction with AlphaFold3

Peptides mediate up to 40% of protein-protein interactions (PPIs), offering high specificity and the ability to target binding sites inaccessible to small molecules, making them promising candidates for drug development. Accurate modeling of protein-peptide complexes is crucial for understanding fundamental biological processes and for advancing peptide-based drug design. However, due to the high conformational flexibility of peptides, predicting their interactions with proteins remains a significant challenge. Recent advancements in artificial intelligence (AI), exemplified by all-atom protein folding neural networks (PFNNs) such as AlphaFold3 (AF3), have expanded predictive capabilities beyond proteins to encompass protein-peptide complexes. Nevertheless, existing evaluations of these methods are often limited in scope and lack systematic, multi-dimensional, and fair comparisons of PFNN performance in protein-peptide complex prediction. Here, we introduce PepPCBench, a comprehensive benchmark framework specifically developed to evaluate AF3s capabilities in predicting protein-peptide complexes. This study utilizes a carefully curated dataset named PepPCSet, which is excluded from the AF3s training or validation sets. This dataset includes 261 protein-peptide complexes with peptide lengths ranging from 5 to 30 residues. Our benchmark results indicate that AF3 outperforms other PFNNs in prediction accuracy and structural validation. However, its performance remains insufficient for practical peptide drug discovery, indicating room for improvement. It is expected that PepPCBench can provide some valuable insights into the enhancement of protein-peptide complex structure prediction and the development of peptide-based therapeutics. The dataset and pipeline protocols are available at https://github.com/zhaisilong/PepPCBench.

bioinformatics↗

bioRxiv2025

Highly sensitive chemiluminescence imaging of misfolded proteins in neurodegenerative models

Visualizing misfolded proteins would greatly facilitate early diagnosis, etiology elucidation, and therapy monitoring of neurodegeneration. Although several probes have been reported, simple and versatile detection in vivo is still challenging. We demonstrated that both generic and precise detection of misfolded proteins could be achieved with a chemiluminescence probe, ADLumin-1. For the generic aspect, ADLumin-1 was highly sensitive to various misfolded proteins, showing up to 127.73-fold higher signal-to-noise ratio than Thioflavin T. ADLumin-1 could also non-invasively visualize misfolded proteins in mouse models of Parkinsons disease, Alzheimers disease and amyotrophic lateral sclerosis. Furthermore, ADLumin-1 displayed precise detection value for -synuclein by combining with PMCA in vitro and bioorthogonal ChRET imaging technology in vivo. ADLumin-1 can selectively detect -synuclein in CSF at the femtomolar level and enables in situ monitoring of misfolded -synuclein in vivo. Combining generality and precision, our findings could be widely applied in preclinical and clinical studies of neurodegenerative diseases. TeaserA highly sensitive chemiluminescence probe enables both generic and precise detection of various misfolded proteins

biochemistry↗

bioRxiv2025

INHBA promotes the progression of gastric cancer by activating MAPK signaling pathway via targeting ITGA6

Gastric cancer (GC) is one of the most common malignancies, ranking as the fifth most common cancer and the fourth leading cause among cancer related deaths worldwide. Upregulated INHBA expression in gastric cancer tissues compared to adjacent non-cancerous tissues was confirmed by immunohistochemistry and qRT-PCR analysis. This increased expression of INHBA was found to be significantly associated with the incidence of tumor lesion, lymph node metastasis and the progression to more advanced TNM stages in patients. Functional experiments showed that INHBA could promote the proliferation of GC cells, enhance migration and invasion in vitro, while simultaneously facilitating the inhibition of apoptosis. To test whether INHBA regulated the tumor growth in vivo, the animal studies were performed. The indicated that INHBA over-expression promoted the tumor growth, including weight and volume. Moreover, this study demonstrated through a series of experiments including RNA-seq, Co-IP, Co-IF, Western blot, and Rescue studies that INHBA promotes the progression of GC by targeting ITGA6 to regulate the MAPK signaling pathway. In summary, understanding the role of INHBA/ITGA6/MAPK in tumourigenesis could provide new insights into gastric cancer therapy and targeted inhibition of INHBA might be a potential therapeutic approach for GC treatment.

cancer biology↗

bioRxiv2025

AlphaFold3 in Drug Discovery: A Comprehensive Assessment of Capabilities, Limitations, and Applications

Accurate prediction of protein-ligand interactions remains a cornerstone challenge in drug discovery. AlphaFold3 (AF3), a recent breakthrough Diffusion Transformer model, holds significant promise for structural biology, but its performance across diverse pharmaceutical applications requires systematic evaluation. In this study, we comprehensively benchmark AF3s capabilities using carefully curated datasets, examining its performance in binary protein-ligand complexes, apo/holo structural variations, GPCR-ligand conformations, ternary systems, and inhibitor affinity prediction. Our analysis reveals that AF3 excels at predicting static protein-ligand interactions with minimal conformational changes, significantly outperforming traditional docking methods in side-chain orientation accuracy. However, we identify critical limitations: AF3 struggles with protein-ligand complexes involving significant conformational changes (>5[A] RMSD), demonstrates a persistent bias toward active GPCR conformations regardless of ligand type, performs poorly on ternary complex prediction, and lacks reliable affinity ranking capability. Notably, AF3s performance declined significantly on structures released after its training cutoff date, suggesting potential memorization rather than physical understanding of molecular interactions. We explored AF3s practical utility through applications in chemoproteomics data interpretation, drug resistance mutation prediction, and kinome profiling simulation. AF3 demonstrated value as a "true-hit binary interaction modeler," capable of generating reliable structural models for experimentally validated binding pairs. However, its ranking metrics showed minimal correlation with experimental binding affinities and limited ability to differentiate across the kinome, highlighting the need for integration with physics-based scoring methods. Our findings indicate that while AF3 represents a significant advancement in protein-ligand structure prediction, it requires complementary approaches to address its limitations in conformational sampling, affinity ranking, and complex system modeling. Recent developments like YDS Ternoplex suggest that enhanced sampling techniques can overcome some of these limitations. The optimal strategy for leveraging AF3 in drug discovery likely involves its integration into hybrid computational pipelines that combine AI-based prediction with physics-based refinement and experimental validation.

biochemistry↗

bioRxiv2025

Mechanistic basis for protection against fatty liver disease by CIDEB loss-of-function mutations

Background & AimsSomatic and germline CIDEB mutations are associated with protection from chronic liver diseases. The mechanistic basis and whether CIDEB suppression would be an effective therapy against fatty liver disease remain unclear. Methods21 CIDEB somatic mutations were introduced into cells to assess functionality. In vivo screening was used to trace Cideb mutant clones in mice fed normal chow, western (WD), and choline-deficient, L-amino acid-defined, high-fat (CDA-HFD) diets. Constitutive and conditional Cideb knockout mice were generated to study Cideb in liver disease. Isotope tracing was used to evaluate fatty acid oxidation and de novo lipogenesis. Transcriptomics, lipidomics, and metabolic analyses were utilized to explore molecular mechanisms. Double knockout models (Cideb/Atgl and Cideb/Ppara) tested mechanisms underlying Cideb loss. ResultsMost CIDEB mutations showed that they impair function, and lineage-tracing showed that loss-of-function clones were positively selected with some, but not all fatty liver inducing diets. Cideb KO mice were protected from WD, CDA-HFD, and alcohol diets, but had the greatest impact on CDA-HFD induced liver disease. Hepatocyte-specific Cideb deletion could ameliorate disease after MASLD establishment, modeling the impact of therapeutic siRNAs. Cideb loss protected livers via increased {beta}-oxidation, specifically through ATGL and PPARa activation. ConclusionsCideb deletion is more protective in some types of fatty liver disease. {beta}-oxidation is an important component of the Cideb protective mechanism. CIDEB inhibition represents a promising approach, and somatic mutations in CIDEB might predict the patient populations that might benefit the most.

physiology↗

bioRxiv2025

Field Survey of Anopheles Mosquito and Their Culturable Gut Bacteria in the Border Regions of Yunnan Province, China

Despite the elimination of domestic malaria transmission within China, the border areas of Yunnan Province continue to face a heightened risk of imported cases due to the presence of diverse Anopheles mosquito species and the widespread burden of malaria in neighboring countries. While the mosquito gut microbiota offers a promising avenue for developing anti-malaria strategies, a meticulous investigation of the various Anopheles species and their associated gut microbiota in Yunnans border areas has yet to be undertaken. To address this gap, field surveys were conducted over the years 2020, 2021, and 2023. These surveys resulted in the collection of a substantial sample comprising 4,221 adult female mosquitoes spanning sixteen species with Anopheles sinensis identified as the predominant species. Furthermore, 741 cultivable strains of gut symbiotic bacteria were isolated and classified into four phyla comprising 109 genera with the composition of dominant bacteria varying annually. This study provides valuable insights into the population dynamics of malaria vectors in the border regions of Yunnan province, China, along with the intestinal bacteria they harbor including several bacterial species which exhibit promising anti-plasmodium effects. By leveraging the antagonistic effects of symbiotic bacteria against Plasmodium, our findings contribute to an ongoing global endeavor to develop innovative Anopheles management and malaria prevention strategies.

microbiology↗

bioRxiv2025

A multi-modal cell-free RNA language model for liquid biopsy applications

Cell-free RNA (cfRNA) profiling has emerged as a powerful tool for non-invasive disease detection, but its application is limited by data sparsity and complexity, especially in settings with constrained sample availability. We introduce Exai-1, a multi-modal, transformer-based generative foundation model that integrates RNA sequence embeddings with cfRNA abundance data to capture biologically meaningful representations of circulating RNAs. By leveraging both sequence and expression modalities, Exai-1 captures a biologically meaningful latent structure of cfRNA profiles. Pre-trained on over 306 billion tokens from 8,339 samples, Exai-1 enhances signal fidelity, reduces technical noise, and improves disease detection by generating synthetic cfRNA profiles. We show that self-attention and variational inference are particularly important for preservation of biological signals and contextual relationships. Additionally, Exai-1 facilitates cross-biofluid translation and assay compatibility through disentangling biological signals from confounders. By uniting sequence-informed embeddings with cfRNA expression patterns, Exai-1 establishes a transfer learning foundation for liquid biopsy, offering a scalable and adaptable framework for next-generation cfRNA-based diagnostics.

bioinformatics↗

bioRxiv2025

Surveillance of Japanese Encephalitis Virus in Piggery Effluent and Environmental Samples: A Complementary Tool for Outbreak Detection

Japanese encephalitis virus (JEV) is an emerging public health and biosecurity concern in Australia, with recent human cases and detections in mosquitoes and pigs across multiple states highlight the risk to susceptible human and animal populations. While traditional surveillance methods such as mosquito trapping, sentinel chicken programs and direct testing of pig specimens remain essential, monitoring effluent offers a valuable complementary approach for detecting infections within animal populations. This study presents the first evidence of JEV in Australian piggery effluents/environmental waters, demonstrating the feasibility of effluent and environmental water surveillance for JEV monitoring. Effluent/environmental samples from multiple piggery sites were analyzed using real-time reverse transcription polymerase chain reaction (RT-PCR), revealing the presence of JEV genetic fragments in solid and liquid fractions of effluents at three farms, with corresponding veterinary cases in some herds. Viral RNA was detected more frequently in solid fraction of effluent samples, aligning with previous findings on the partitioning behaviour of mosquito-borne viruses. The detection of JEV in the borrow pit (i.e., a man-made excavation that holds water) water sample highlights potential transmission pathways via mosquito vectors. These findings demonstrate the value of effluent monitoring as an additional tool for JEV surveillance in piggery settings, supporting potential early warning systems and mitigation strategies. Integrating effluent-based monitoring with traditional surveillance approaches could improve livestock industry related disease detection, risk assessments, and response efforts for human and animal health in endemic and emerging regions. Wastewater/effluent surveillance may have important applications for the management of a wide range of emerging animal diseases. IMPORTANCEThis study presents the first evidence of Japanese encephalitis virus (JEV) detection in Australian piggery effluents, establishing effluent surveillance as a valuable complementary tool for monitoring viral pathogens in animal populations. Our findings support the integration of effluent monitoring with traditional surveillance systems to improve early warning capabilities, enhance biosecurity, and mitigate risks to both animal and human health.

molecular biology↗

bioRxiv2025

MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis

Analyzing animal behavior is crucial in advancing neuroscience, yet quantifying and deciphering its intricate dynamics remains a significant challenge. Traditional machine vision approaches, despite their ability to detect spontaneous behaviors, fall short due to limited interpretability and reliance on manual labeling, which restricts the exploration of the full behavioral spectrum. Here, we introduce MouseGPT, a Vision-Language Model (VLM) that integrates visual cues with natural language to revolutionize mouse behavior analysis. Built upon our first-of-its-kind dataset--incorporating pose dynamics and open-vocabulary behavioral annotations across over 42 million frames of diverse psychiatric conditions--MouseGPT provides a novel, context-rich method for comprehensive behavior interpretation. Our holistic analysis framework enables detailed behavior profiling, clustering, and novel behavior discovery, offering deep insights without the need for labor-intensive manual annotation. Evaluations reveal that MouseGPT surpasses existing models in precision, adaptability, and descriptive richness, positioning it as a transformative tool for ethology and for unraveling complex behavioral dynamics in animal models.

animal behavior and cognition↗

bioRxiv2025

Rare mutations implicate CGE interneurons as a vulnerable axis of cognitive deficits across psychiatric disorders

Neuropsychiatric disorders such as autism spectrum disorder (ASD) and schizophrenia (SCZ) share genetic risk factors, including genes affected by rare high-penetrance single nucleotide variants (SNVs) and copy number variants (CNVs). ASD and SCZ exhibit both overlapping and distinct clinical phenotypes. Cognitive deficits and intellectual disability--critical predictors of long-term outcomes--are common to both conditions. To investigate shared and disorder-specific neurobiological impact of highly penetrant rare mutations in ASD and SCZ, we analyzed human single-nucleus whole-brain sequencing data to identify strongly affected brain cell types. Our analysis revealed caudal ganglionic eminence (CGE)-derived GABAergic interneurons as a key nexus for cognitive deficits across these disorders. Notably, genes within 22q11.2 deletions, known to confer a high risk for SCZ, ASD, and cognitive impairment, showed a strong expression bias toward vasoactive intestinal peptide-expressing cells (VIP+) among CGE subtypes. To explore perturbations of VIP+ GABAergic interneurons in the 22q11.2 deletion syndrome in vivo, we examined their activity in the Df(16)A+/- mouse model during a spatial navigation task and observed reduced activity along with altered responses to random rewards. At the population level, VIP+ interneurons exhibited impaired spatial encoding and diminished subtype-specific activity suggesting deficient disinhibition in CA1 microcircuits in the hippocampus, a region essential for learning and memory. Overall, these results demonstrate the crucial role of CGE-derived interneurons in mediating cognitive processes that are disrupted across a range of psychiatric and neurodevelopmental disorders.

neuroscience↗

bioRxiv2025

Plant-specific adaptations of the CDC48 unfoldase

Targeted protein degradation through the CDC48 unfoldase enables the maintenance and rapid adaptation of proteomes across eukaryotes. However, the profound differences between animals, fungi, and plants are expected to have led to a significant adaptation of the CDC48-mediated degradation. While animal and fungal CDC48 systems have shown structural and functional preservation, such analysis is lacking for plants. We determined the structural and functional characteristics of Arabidopsis thaliana CDC48A in various states and bound to the target-identifying cofactors UFD1 and NPL4. Our analysis reveals several features that distinguish AtCDC48 from its animal and yeast counterparts, despite an 80% sequence identity. Key features are that AtCDC48A displays distinct domain dynamics and interacts differently with AtNPL4. Moreover, AtNPL4 and AtUFD1 do not form an obligate heterodimer, but independently bind to AtCDC48A and mediate target degradation; however, their joint action is synergistic. An evolutionary analysis supports that these Arabidopsis features are conserved across plants and represent the ancestral state of eukaryotic CDC48 systems. Jointly, our findings support that plant CDC48 retains a greater modular and combinatorial cofactor usage, highlighting a specific adaptation of targeted protein degradation in plants.

plant biology↗

bioRxiv2025

One Tracer, Dual Platforms: Unlocking Versatility of Fluorescent Probes in TR-FRET and NanoBRET Target Engagement Assays

Target engagement assays are essential for drug discovery, utilizing Time-Resolved Fluorescence Resonance Energy Transfer (TR-FRET) and Nano Bioluminescence Resonance Energy Transfer (NanoBRET) as complementary methods for biochemical and cellular evaluation. Traditional platforms require distinct fluorescent tracers, increasing costs and complexity. This study systematically evaluates the cross-platform performance of T2-BODIPY-FL and T2-BODIPY-589, tracers developed for receptor-interacting protein kinase 1 (RIPK1) target engagement in TR-FRET and NanoBRET applications, respectively. Our results demonstrate both tracers effectively bridge biochemical and cellular assays, providing reliable measurements. T2-BODIPY-589 demonstrates superior performance in NanoBRET (Z up to 0.80) and acceptable functionality in TR-FRET (Z=0.53). Conversely, T2-BODIPY-FL performs optimally in TR-FRET (Z=0.57) and exhibits NanoBRET potential (Z up to 0.72). Competition assays with an unlabeled inhibitor yielded consistent binding constants across all combinations. These findings suggest a single tracer can integrate diverse assay platforms, enhancing consistency and comparability in drug discovery. Table of Content Graphic O_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC="FIGDIR/small/645143v2_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1cad1eforg.highwire.dtl.DTLVardef@9ccef0org.highwire.dtl.DTLVardef@d611c1org.highwire.dtl.DTLVardef@279f90_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗