Biology subjects

Xie, L.

Publications and source records attributed to Xie, L..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

APPLE-MS: A affinity purification-mass spectrometry method assisted by PafA-mediated proximity labeling

While affinity purification-mass spectrometry (AP-MS) has significantly advanced protein-protein interaction (PPI) studies, its limitations in detecting weak, transient, and membrane-associated interactions remain. To address these challenges, we introduced an innovative proteomic method termed Affinity Purification coupled Proximity LabEling-Mass Spectrometry (APPLE-MS), which combines the high specificity of Twin-Strep-tag enrichment with PafA-mediated proximity labeling. This method achieves unprecedented sensitivity while maintaining high specificity (4.07-fold over AP-MS). APPLE-MS also revealed the dynamic mitochondrial interactome of SARS-CoV-2 ORF9B during antiviral responses, while endogenous PIN1 profiling uncovered novel roles in DNA replication. Notably, APPLE-MS enabled in situ mapping of GLP-1 receptor complexes, demonstrating its unique capabilities for membrane PPI studies. This versatile method advances interactome research by providing comprehensive, physiologically relevant PPI networks, opening new opportunities for mechanistic discovery and therapeutic targeting. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=184 HEIGHT=200 SRC="FIGDIR/small/652320v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@aaf55borg.highwire.dtl.DTLVardef@1d25af1org.highwire.dtl.DTLVardef@150c1acorg.highwire.dtl.DTLVardef@db0d1c_HPS_FORMAT_FIGEXP M_FIG C_FIG MOTIVATION - Cell Reports Methods onlyAffinity purification-mass spectrometry (AP-MS) has become a widely used method for capturing high-affinity protein-protein interactions (PPIs). However, it still faces several limitations, including high levels of non-specific binding, challenges in detecting weak interactions, and limited ability to identify PPIs at the cell surface in situ. To overcome these challenges, we introduce a modular AP-MS method enhanced by PafA-mediated proximity labeling, which improves both the specificity and sensitivity of PPI detection in a single, streamlined workflow. SIGNIFICANCE - Cell Chemical Biology onlyProximity labeling is a powerful tool in chemical biology, providing critical support for studying protein function, cell signaling, and disease mechanisms by chemically labeling and capturing biomolecular interactions. In this study, we introduce a modular affinity purification-mass spectrometry (AP-MS) method that integrates proximity labeling. This method leverages the proximity-dependent enzymatic activity of PafA to covalently attach PupE to nearby proteins, in combination with the high-affinity interaction between Twin-Strep-tag and streptavidin to achieve efficient binding. This innovative approach enables precise identification of protein-protein interactions (PPIs), offering a powerful tool for capturing weak and transient interactions that are often missed by traditional AP-MS techniques. By enhancing both the specificity and efficiency of protein labeling, our work provides a robust chemical biology strategy to elucidate complex protein interactions, which are essential for understanding cellular processes and developing targeted therapeutic strategies, offering new insights into protein function and interaction dynamics in both health and disease.

biochemistry↗

bioRxiv2025

Structural insight into the transcription activation mechanism of the phage Mor/C family activators

Bacteriophage Mu, a temperate phage that infects E. coli K-12 and other enteric bacteria, precisely controls its replication cycle through hijacking host RNA polymerase (RNAP) by the middle operon regulator Mor and the late gene transcription activator C. Though a dimeric arrangement and significant conformational changes are proposed for the distinct Mor/C family activators, the underlying transcription activation mechanism remains unclear. In this study, we present two cryo-EM structures of the transcription activation complex (Mor-TAC and C-TAC) with phage Mu middle and late gene promoters, respectively. Remarkably, the Mor/C activators bind to promoter DNA as a centrosymmetric tetramer rather than as the proposed dimer, concurrently stabilizing by the N-terminal dimerization domains and C-termini. The C-terminal DNA binding domains and two anti-{beta}-strands simultaneously interact with two adjacent DNA major grooves. The activators also engage a variety of interactions with the conserved domains (CTD, {sigma}70R4, and {beta} FTH) of RNAP, providing evidences for a recruitment mechanism. In addition, single-molecule FRET assays show that C significantly enhances RPitc formation, suggesting a different multi-step activation mechanism for C. Collectively, these findings reveal the unique transcription activation mechanism of tetrameric Mor/C family activators, unraveling a novel mode of phage hijacking and bacterial transcription regulation.

biochemistry↗

bioRxiv2025

Scalable Culture of Mammalian Cells in RGD Peptide-Modified Alginate Hydrogel Microtubes

Traditional livestock farming is resource-intensive and environmentally unsustainable, necessitating alternative methods for meat production. Cell cultured meat, produced by expanding and differentiating animal cells, offers great potential for substituting for conventional animal meat. Nevertheless, it is still limited by the scalability and efficiency of current cell culture technologies. In this study, we developed an RGD peptide-modified alginate hydrogel microtube microbioreactor (AlgTubes) to support the scalable culture of anchor-dependent cells, such as myoblasts and adipocytes, for cell-cultured meat production. AlgTubes provide a cell-friendly 3D microenvironment that enhances cell viability, growth, and yield while overcoming limitations of conventional bioreactors, such as shear stress, aggregation, and diffusion constraints. We successfully expanded mouse (C2C12) and quail (QM7) myoblasts in AlgTubes, achieving cell densities exceeding 1.0 x 10 cells/mL, far surpassing traditional stirred-tank bioreactors. Differentiation resulted in the formation of mature myotubes. Co-culturing myoblasts with mesenchymal stem cells or fibroblasts further improved yield and viability, particularly under differentiation conditions. By significantly increasing cell culture density, AlgTubes can substantially reduce culture volume, lowering labor requirements, reagent costs, equipment needs, facility space, and manufacturing expenses.

bioengineering↗

bioRxiv2025

EasyMultiProfiler: An Efficient Multi-Omics Data Integration and Analysis Workflow for Microbiome Research

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/649266v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@14dd0b1org.highwire.dtl.DTLVardef@1c04a79org.highwire.dtl.DTLVardef@159894dorg.highwire.dtl.DTLVardef@1454a01_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG HighlightsO_LIEasyMultiProfiler provides unified integration of microbial multi-omics data C_LIO_LIEasyMultiProfiler enables natural language-style analytical workflows C_LIO_LIEasyMultiProfiler solves key challenges in standardization and reproducibility C_LIO_LIEasyMultiProfiler extracts meaningful biological insights efficiently C_LI Host-microbiome interactions are crucial in maintaining physiological homeostasis and influencing disease progression. While traditional microbiome research, focused on microbial diversity and abundance, provided essential frameworks, the integration of multi-omics approaches established a far more comprehensive, systems-level understanding of microbiome functionality. However, significant methodological challenges in multi-omics data integration remain, including inconsistent sample coverage, heterogeneous data formats, and complex downstream analytical workflows. These challenges impact the reproducibility and reliability of results and underscore the critical requirement for developing standardized, systematic workflows. Thus, we developed the EasyMultiProfiler (EMP) workflow, a streamlined and efficient analytical framework for multi-omics data analysis. EMP provides a comprehensive infrastructure based on the SummarizedExperiment and MultiAssayExperiment classes, creating a unified framework for storing and analyzing omics data. The frameworks architecture comprises five interconnected functional modules: data extraction, preparation, support, analysis, and visualization. These modules are smoothly integrated into natural language-style analytical workflows, offering users an efficient and standardized solution for multi-omics analysis. EMP addresses critical challenges in multi-omics data analysis, including data integration, workflow standardization, and result reproducibility. The platforms modular architecture and intuitive interface provide researchers and clinicians with a robust, flexible workflow to systematically extract biologically relevant insights from complex multi-omics datasets.

bioinformatics↗

bioRxiv2025

Polymorphic α-Glucans as Structural Scaffolds in Cryptococcus Cell Walls for Chitin, Capsule, and Melanin: Insights from 13C and 1H Solid-State NMR

Cryptococcus species are major fungal pathogens responsible for life-threatening infections in approximately a million individuals globally each year, with alarmingly high mortality rates. These fungi are distinguished by a distinctive cell wall architecture further reinforced by two virulence-associated layers, melanin and capsule, rendering them insensitive to antifungal agents targeting the cell wall, such as echinocandins. The molecular interplay between these three biomolecular layers remains poorly understood. Here we employ solid-state NMR spectroscopy to examine intact cells of both wild-type and capsule-deficient strains of C. neoformans, along with its melanized cells. High-resolution 13C and 1H data revealed five distinct structural forms of -1,3-glucans that play versatile roles in forming the rigid cell wall scaffold by interacting with chitin microfibrils and chitosan, and in stabilizing the mobile matrix by associating with {beta}-1,6-glucan and a small fraction of {beta}-1,3-glucan. Two primary forms of -1,3-glucans were distributed throughout the cell wall, hosting melanin deposition in the inner domain and capsule attachment on the cell surface. These findings offer a paradigm shift in understanding the cryptococcal cell wall and its interaction with two key virulence factors on opposite sides, raising critical biochemical questions that could inform the development of more effective antifungal treatments for cryptococcosis.

biochemistry↗

bioRxiv2025

Context-dependent Interactors Regulate TDP-43 Dysfunction in ALS/FTLD

TDP-43 mislocalization, aggregation, and loss of splicing function are neuropathological hallmarks in over 97% of Amyotrophic Lateral Sclerosis (ALS), 45% of Frontotemporal Lobar Degeneration (FTLD), and 60% of Alzheimers Disease, which has been reclassified as LATE-NC. However, the mechanisms underlying TDP-43 dysfunction remain elusive. Here, we utilize APEX2-driven proximity labeling and mass spectrometry to characterize the context-dependent TDP-43 interactome in conditions of cytoplasmic mislocalization, impaired RNA-binding contributing to aggregation, and oxidative stress. We describe context-dependent interactors, including disrupted interactions with splicing-related proteins and altered biomolecular condensate (BMC) associations. By integrating ALS and FTLD snRNA-seq data, we uncover disease-relevant molecular alterations and validate our dataset through a functional screen that identifies key TDP- 43 regulators. We demonstrate that disrupting nuclear speckle integrity, particularly through the downregulation of the splicing factor SRRM2, promotes TDP-43 mislocalization and loss of function. Additionally, we identify NUFIP2 as an interactor associated with mislocalization that sequesters TDP-43 into cytoplasmic aggregates and co-localizes with TDP-43 pathology in patient tissue. We also highlight HNRNPC as a potent TDP-43 splicing regulator, where precise modulation of TDP-43 or HNRNPC can rescue cryptic exon splicing. These findings provide mechanistic insights and potential therapeutic targets for TDP-43 dysfunction.

molecular biology↗

bioRxiv2025

Rapid High-Resolution Analysis of Polysaccharide-Lignin Interactions via Proton-Detected Solid-State NMR with Application to Eucalyptus and Spruce

The plant secondary cell wall, a complex matrix composed of cellulose, hemicellulose, and lignin, is crucial for the mechanical strength and water-proofing properties of plant tissues, and serves as a primary source of biomass for biorenewable energy and biomaterials. Structural analysis of these polymers and their interactions within the secondary cell wall has been heavily relying on 13C-based solid-state NMR techniques. In this study, we explore the application of 1H-detected solid-state NMR techniques for rapid, high-resolution structural characterization of polysaccharides and lignin, demonstrated on the stems of hardwood eucalyptus and softwood spruce. We employed several strategies, including the use of synthesized 2D spectra to resolve central 1H resonances and the combined application of 3D hCCH and hCHH experiments for complete resonance assignment and unambiguous identification of lignin-carbohydrate interactions. Our findings emphasize the central role of acetylated three-fold xylan conformers, rather than two-fold, in stabilizing the carbohydrate-lignin interface, with glucuronic acid sidechains in eucalyptus glucuronoxylan colocalizing with lignin, revised cellulose-lignin interactions involving uncoated microfibril surfaces, and pectin-lignin interactions indicative of early-stage lignification. We also carefully evaluated the interference from tannins in softwood species. These results present a novel approach for rapid structural analysis of lignocellulosic biomaterials without the need for solubilization or extraction.

biophysics↗

bioRxiv2025

Integrating Interpretable Machine Learning and Multi-omics Systems Biology for Personalized Biomarker Discovery and Drug Repurposing in Alzheimer's Disease

Alzheimers disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health record (EHR) data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; nine tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations (SHAP) identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct "subtissues" (clusters of samples); and gene-gene co-expression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six FDA-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large U.S. de-identified insurance-claims database (n = 364733), exposure to promethazine, one of the candidate drugs, was associated with a 57-62 % lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both p < 0.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multi-omics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

systems biology↗

bioRxiv2025

Accelerated genome shuffling associated with rapid evolution of sexual conflict in seed beetles

Structural variation (SV) has an extensive impact on the evolution of the genome. The diverse SV provide a material basis for genome evolution, which can affect gene expression, 3D genome structure, and even changes in complex chromosomal features through regional recombination, copy number variation, and chromosome rearrangement. Here, we assemble and compare the chromosome-level assemblies of three species (Acanthoscelides obtectus, Callosobruchus chinensis, and C. maculatus) in Bruchinae, a clade of Coleoptera that is often used as models to study sexual conflict, to investigate the influence of SV on the changes of various genomic characteristics during species evolution. Through comparative genomic analysis of Coleoptera species, we reveal extensive structural variants (SVs) within the Bruchinae family, which is linked to the ongoing activity of LINE transposons. We show that SV drive the rapid evolution of sex chromosomes and correlated with dynamic evolution of sex-biased gene expression in seed beetle species. Our analysis highlights the acquisition of novel genes on the Y chromosome through frequent gene translocations, gradually shaping them into genes exhibiting male-biased expression patterns. This underscores the Y chromosomes role in promoting sexual dimorphism evolution. Our findings reveal the significant contributions of SV to genome evolution, specifically through their influence on sex chromosome evolution and the evolutionary dynamics of sex-biased genes.

evolutionary biology↗

bioRxiv2025

MolGene-E: Inverse Molecular Design to Modulate Single Cell Transcriptomics

Designing drugs that can restore a diseased cell to its healthy state is an emerging approach in systems pharmacology to address medical needs that conventional target-based drug discovery paradigms have failed to meet. Single-cell transcriptomics can comprehensively map the differences between diseased and healthy cellular states, making it a valuable technique for systems pharmacology. However, single-cell omics data is noisy, heterogeneous, scarce, and high-dimensional. As a result, no machine learning methods currently exist to use single-cell omics data to design new drug molecules. We have developed a new deep generative framework named MolGene-E to tackle this challenge. MolGene-E combines two novel models: 1) a cross-modal model that can harmonize and denoise chemical-perturbed bulk and single-cell transcriptomics data, and 2) a contrastive learning-based generative model that can generate new molecules based on the transcriptomics data. MolGene-E consistently outper-forms baseline methods in generating high-quality, hit-like molecules on gene expression profiles from two evaluation settings: CRISPR knock-out perturbation profiles from L1000toRNAseq dataset, and single-cell gene expression profiles from Sciplex-3 dataset, both in zero-shot molecule generation setting. This superior performance is demonstrated across diverse de novo molecule generation metrics. Extensive evaluations demonstrate that MolGene-E achieves state-of-the-art performance for zero-shot molecular generations. This makes MolGene-E a potentially powerful new tool for drug discovery.

bioinformatics↗

bioRxiv2025

Effects of a Sequential Application of Plant Protection Products on Soil Microbes and Free-Living Nematodes in a Field Experiment

During crop growth cycle, often several different plant protection products (PPPs) are applied, in combination or sequentially. Such sequential applications result in unintentional mixtures of residues that may affect ecosystem services supported by non-target organisms such as soil microbes and nematodes. This scenario of sequential PPP application is frequent in agricultural practice but rarely addressed experimentally at field scale with regard to environmental impacts. The objective of this study was to evaluate the effect of individual and sequential application of three PPPs (the herbicide clopyralid, the insecticide zeta-cypermethrin, and the fungicide pyraclostrobin) on soil microbial communities, and on the abundance of free-living nematode. Single applications (at 1x or 10x the agronomical dose) were made to triplicated field plots with each one of the PPPs or all three PPPs in sequence, with untreated plots serving as controls. Plots were sampled prior to each application, and 7 and 28 days thereafter. The fungal communitys composition and abundance were found to be more susceptible than the bacterial community to PPP applications, while the bacterial was mainly driven by the in-field heterogeneity of soil properties. Transient effects of PPP applications were detected on nematode abundance. Higher tier ecotoxicological studies offer greater ecological relevance compared to the standard laboratory tests but are challenged by environmental variations that should be accounted for when evaluating the ecotoxicity of pesticides on soil organisms.

ecology↗

bioRxiv2025

Developing an anatomically valid segmentation protocol for anterior regions of the medial temporal lobe for neurodegenerative diseases

BackgroundThe anterior portion of the medial temporal lobe (MTL) is one of the first regions targeted by pathology in sporadic Alzheimers disease (AD) and Limbic-predominant Age-related TDP-43 Encephalopathy (LATE) indicating a potential for metrics from this region to serve as imaging biomarkers. Leveraging a unique post-mortem dataset of histology and magnetic resonance imaging (MRI) scans we aimed to 1) develop an anatomically valid segmentation protocol for anterior entorhinal cortex (ERC), Brodmann Area (BA) 35, and BA36 for in vivo 3 tesla (T) MRI and 2) incorporate this protocol in an automated approach. MethodsWe included 20 cases (61-97 years old, 50% females) with and without neurodegenerative diseases (11 vs. 9 cases) to ensure generalizability of the developed protocol. Digitized MTL Nissl-stained coronal histology sections from these cases were annotated and registered to same-subject post-mortem MRI. The protocol was developed by determining the location of histological borders of the MTL cortices in relation to anatomical landmarks. Subsequently the protocol was applied to 15 cases twice, with a 2-week interval, to assess intra-rater reliability with the Dice Similarity Index (DSI). Thereafter it was implemented in our in- house Automatic Segmentation of Hippocampal Subfields (ASHS)-T1 approach and evaluated with DSIs. ResultsThe anterior histological border distances of ERC, BA35 and BA36 were evaluated with respect to various anatomical landmarks and the distance relative to the beginning of the hippocampus was chosen. To formulate segmentation rules, we examined the histological sections for the location of borders in relationship to anatomical landmarks in the coronal sections. The DSI for the anterior MTL cortices for the intra-rater reliability was 0.85-0.88 and for the ASHS-T1 against the manual segmentation was 0.62-0.65. DiscussionWe developed a reliable segmentation protocol and incorporated it in an automated approach. Given the vulnerability of the anterior MTL cortices to tau deposition in AD and LATE, the updated approach is expected to improve imaging biomarkers for these diseases.

neuroscience↗

bioRxiv2025

Predicting Mutation-Induced Relative Protein-Ligand Binding Affinity Changes via Conformational Sampling and Diversity Integration with Subsampled Alphafold2 in Few-Shot Learning

Predicting the impact of mutations on protein-ligand binding affinity is crucial in drug discovery, particularly in addressing drug resistance and repurposing existing drugs. Current structure-based methods are constrained by their dependence on known protein-ligand complex structures. The absence of structural data for mutated proteins, along with the potential of mutations to modify protein conformational states, brings challenges for reliable predictions. The downstream application of conformational sampling of Alphafold, such as the prediction of binding free energy changes induced by mutation, has not been fully explored. To tackle the challenge, we propose a few-shot learning approach that integrates AlphaFold2 subsampling, ensemble docking, and Siamese learning to predict the relative binding affinity changes induced by mutations. We validate our approach for predicting binding affinity changes in 31 variants of the mutated Abelson tyrosine kinase (ABL). The conformation ensembles for each ABL variant are generated using AlphaFold2 subsampling. The most likely conformational states are selected by Gaussian fitting to construct the pairwise structural inputs and compute relative binding energies. Then the relative binding affinities are derived by averaging the values predicted by the Siamese learning model across the constructed conformational pairs. By benchmarking on the Tyrosine kinase inhibitors (TKI) dataset and the direct binding affinity using the refined set of PDBbind database, our proposed approach achieves an increased Spearman correlation coefficient for five out of six TKI molecules across 31 mutants when estimating relative binding affinities. Although the proposed method is validated on the ABL, it can be transferred and applied to other drug-target interaction predictions when the conformational flexibility of proteins needs to be considered. We anticipate the approach will be valuable for predicting binding energy changes induced by mutations in proteins or ligands, facilitating drug discovery efforts.

bioinformatics↗

bioRxiv2025

Survival Reinforced Transfer Learning for Multicentric Proteomic Subtyping and Biomarker Discovery

Omics-based molecular subtyping in large-scale and multicentric cohort studies is a prerequisite for proteomics-driven precision medicine (PDPM). However, keeping the subtypes with robust molecular features and significant associations with prognosis among different cohorts is challenging due to the biological heterogeneity and technical inconsistency. Herein, we propose a subtyping algorithm, named Survival Reinforced Patient Stratification (SRPS), to adapt the known subtypes from the discovery cohort to another by simultaneously preserving the distinct prognosis and molecular characteristics of each subtype. SRPS has been benchmarked on simulated and real-world datasets, where it shows a 12% increase in classification accuracy and possesses the best prognostic discriminations. Moreover, based on the calculated subtype significance score, an unpopular protein, Peptidylprolyl Isomerase C (PPIC), was identified as the top-1 remarkable protein for subtyping the hepatocellular carcinoma (HCC) patients with the worst prognosis. Eventually, PPIC was experimentally proved to be a pro-cancer protein in HCC, confirming our work as a practice of interpretable machine learning guided biological discovery in PDPM research.

bioinformatics↗

bioRxiv2025

Sequence-based Drug-Target Complex Pre-training Enhances Protein-Ligand Binding Process Predictions Tackling Crypticity

Predicting protein-ligand binding characteristics, such as affinity and kinetics, is critical for accelerating drug discovery. However, many existing computational methods face key limitations, including insufficient integration of comprehensive databases, inadequate representation of protein structural dynamics, and incomplete modeling of microscale protein-ligand interactions. To address these challenges, we introduce ProMoNet, a sequence-based pre-training and fine-tuning framework to enhance the prediction of protein-ligand binding characteristics. ProMoNet leverages protein and molecular foundation models to expand data coverage and enhance diversity. It also introduces a pre-training strategy based on protein-ligand binding site prediction, which bridges protein- and ligand-level representations to support downstream prediction tasks involving protein-ligand complexes. Our pre-training module effectively models microscale protein-ligand interactions and captures the dynamic nature of proteins, including binding site crypticity, without relying on 3-dimensional structural inputs. Notably, this module surpasses or matches state-of-the-art structure-based methods in identifying exposed and cryptic binding sites while maintaining high efficiency. Our fine-tuning module then efficiently transfers the pre-trained knowledge to downstream tasks such as binding affinity and binding kinetics prediction, achieving superior performance. The combination of ProMoNets strong performance and demonstrated efficiency across multiple tasks highlights its potential for broad applications in drug discovery. Scientific ContributionWe propose ProMoNet, a sequence-based pre-training and fine-tuning framework for protein-ligand binding characteristic prediction, where protein-ligand binding site prediction is introduced as a pre-training strategy to bridge independent protein- and ligand-level representations for downstream complex-level tasks. We design two dedicated modules, including a pre-training module that models microscale protein-ligand interactions and captures protein dynamics, as well as a fine-tuning module that efficiently integrates the pre-trained representations for downstream tasks. Even compared to structure-based methods, ProMoNet matches state-of-the-art performance in exposed and cryptic binding site identification and delivers superior results in binding affinity and kinetics prediction, making it a promising tool for drug discovery.

bioinformatics↗

bioRxiv2024

Phosphotyrosine Signal Profiling of Clinical CAR-T Reveals Tonic Signaling Associated with Therapeutic Efficacy

Chimeric Antigen Receptor T (CAR-T) therapies have revolutionized the treatment of cancers such as relapsed and refractory B cell malignancies. However, the precise therapeutic mechanism of CAR-T action remain to be elucidated. In this study, we systematically analyzed CAR signaling via phosphotyrosine (pTyr) proteomics in CAR-T cells from both clinical patients and healthy donors. We found that CAR-T products from clinical patients displayed heightened tyrosine phosphorylation, particularly in the JAK-STAT, MAPK and TCR signaling cascades. We also identified the significantly regulated pTyr sites in primary CAR-T cells under tonic signaling or upon stimulation by antigen-presenting CD19-K562 cells. Although both CD28{zeta} and 4-1BB{zeta} CAR-T cells exhibited comparable pTyr changes, CD28{zeta} CAR-T cells displayed a more pronounced activation in the TCR signaling pathway. Additionally, comparative analysis between clinical and primary CAR-T cells suggested that CAR-T products were subject to heightened tonic signaling, which may be related to therapeutic relapse. Our findings reveal the state of clinical CAR-T products and refine the CAR-T signal transduction network, providing comprehensive insights and informed guidance for CAR-T therapies.

immunology↗

bioRxiv2024

Short RNA chaperones promote aggregation-resistant TDP-43 conformers to mitigate neurodegeneration

Aberrant aggregation of the prion-like, RNA-binding protein TDP-43 underlies several debilitating neurodegenerative proteinopathies, including amyotrophic lateral sclerosis (ALS). Here, we define how short, specific RNAs antagonize TDP-43 aggregation. Short, specific RNAs engage and stabilize the TDP-43 RNA-recognition motifs, which allosterically destabilizes a conserved helical region in the prion-like domain, thereby promoting aggregation-resistant conformers. By mining sequence space, we uncover short RNAs with enhanced activity against TDP-43 and diverse disease-linked variants. The solubilizing activity of enhanced short RNA chaperones corrects aberrant TDP-43 phenotypes in optogenetic models and ALS patient-derived neurons. Remarkably, an enhanced short RNA chaperone mitigates TDP-43 proteinopathy and neurodegeneration in mice. Our studies reveal mechanisms of short RNA chaperones and pave the way for the development of short RNA therapeutics for fatal TDP-43 proteinopathies.

biochemistry↗

bioRxiv2024

Single-cell resolution spatial analysis of antigen-presenting cancer-associated fibroblast niches

Recent studies have identified a unique subtype of cancer-associated fibroblasts (CAFs) termed antigen-presenting CAFs (apCAFs), which remain the least understood CAF subtype. To gain a comprehensive understanding of the origin and function apCAFs, we construct a fibroblast molecular atlas across 14 types of solid tumors. Our integration study unexpectedly reveals two distinct apCAF lineages present in most cancer types: one associated with mesothelial-like cells and the other with fibrocytes. Using a high-resolution single-cell spatial imaging platform, we characterize the spatial niches of these apCAF lineages. We find that mesothelial-like apCAFs are located near cancer cells, while fibrocyte-like apCAFs are associated with tertiary lymphoid structures. Additionally, we discover that both apCAF lineages can up-regulate the secreted protein SPP1, which facilitates primary tumor formation and peritoneal metastasis. Taken together, this study offers an unprecedented resolution in analyzing apCAF lineages and their spatial niches.

cancer biology↗