Biology subjects

Wen, H.

Publications and source records attributed to Wen, H..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

A Spatially Resolved Atlas of Alternative Polyadenylation Across 18 Human Tissues and 76 Disease States

Alternative polyadenylation (APA) is a key regulator of gene expression and cellular dynamics, yet systematic investigations of spatially resolved APA across diverse human tissues remain limited. Here, we developed SpatialAPA (https://github.com/Omicslab-Zhang/spatialAPA), a framework that benchmarks multiple APA identification methods and integrates spatial APA data with gene expression and cellular dynamics at spatial resolution. Applying SpatialAPA to 363 spatial transcriptomic data from 56 projects across 18 human tissues and 76 diseases, we constructed a spatially resolved APA atlas comprising 346,932 APA events across 52,175 genes. This atlas reveals organ-specific APA patterns and provides new insights into how APA regulates tissue homeostasis and disease progression beyond transcriptional control. To ensure cross-sample comparability, we applied batch correction, while spatial cell deconvolution uncovered cell-type-specific dynamics and interactions. In triple-negative breast cancer, integrated spatial and single-cell analyses identified TSPAN8-positive epithelial subpopulations whose distinct APA regulation and transcriptional programs drive differentiation and malignant progression. To facilitate community access, we developed an online platform (http://www.biomedical-web.com/spatialAPAdb/home) for exploring APA, gene expression, and cellular dynamics in health and disease. Together, this study establishes the first comprehensive spatial APA atlas, providing a valuable resource and analytical framework for investigating molecular mechanisms and therapeutic targets.

bioinformatics↗

bioRxiv2025

The Disruption and Normalization of Cortical Language Network Dynamics After Post-Stroke White Matter Disconnection

The human language network undergoes reorganization across different spatiotemporal scales, limited by its fixed structural neurobiological foundations. Although stroke often damages white matter, its effect on temporary language network reorganization is not fully understood. This study examined longitudinal behavioral and resting-state fMRI data from post-stroke patients with exclusive subcortical lesions at three time points: two weeks (n = 38), three months (n = 29), and one year (n = 27). Patients showed mild to moderate language impairments during the acute phase, which improved within three months. The extent of disconnection in several left-hemispheric white matter tracts was negatively associated with language deficits. Healthy controls (HC, n = 25) exhibited domain-segregation dynamics in the cortical language network, limited by the underlying white matter pathways. In patients, severe, state- and track-specific network disruptions were observed in the acute phase, characterized by hypo- and hyper-connectivity and abnormal topological features. As language recovery progressed, connectivity patterns began to return to normal, resembling those of HC, and the domain-segregation dynamics reappeared. These results deepen our understanding of structure-function coupling and indicate significant post-lesional plasticity within the cortical language network.

neuroscience↗

bioRxiv2025

NTM-DB: A Comprehensive Non-Tuberculosis Mycobacteria Genomic Database

Non-tuberculous mycobacteria (NTM) are a major group of environmental bacteria, with approximately one-third of them causing serious human infections, particularly respiratory diseases. The global rise in the prevalence and severity of NTM infections has posed a major public health challenge. While high-throughput sequencing has generated vast genomic data on NTM, there remains a lack of comprehensive resources for cross-species genomic analysis. To address these limitations, we have developed a specialized database called NTM-DB (https://ngdc.cncb.ac.cn/ntmdb) tailored for NTM researchers and clinicians. NTM-DB offers the most comprehensive collection of NTM genomic and bioinformatic resources, including 16,469 genome assemblies (13,134 newly assembled genomes), 189 type/standard strain genomes representing 177 species and 12 subspecies, 705 MLST types, 33,240 resistance genes, and 74,315 virulence genes. A user-friendly interactive website has been constructed to enable efficient browsing, MLST profiling, searching, online analysis, and downloading of the aforementioned data. Notably, with online analysis tools, users can perform customized genotyping, cross-species phylogeny, pan-genome, and virulence and drug resistance gene annotation analyses using our data and/or their uploaded data. Overall, with its comprehensive data, intuitive interface, and powerful analysis tools, NTM-DB serves as an important resource and reference for NTM researchers and clinicians, improving diagnosis and treatment for various NTM-related diseases, and supporting both scientific discovery and clinical practice.

microbiology↗

bioRxiv2025

TMC1 and TMC2 are cholesterol-dependent scramblases that regulate membrane homeostasis in auditory hair cells

TMC1 and TMC2, the pore-forming subunits of the mechanoelectrical transduction (MET) complex in inner ear sensory hair cells, are essential for auditory and vestibular function. Pathogenic mutations in TMC1 are a leading cause of genetic hearing loss, but their underlying cellular mechanisms remain poorly understood. Here, we reveal that TMC1 and TMC2 are cholesterol-regulated lipid scramblases whose activity modulates plasma membrane asymmetry. Using reconstituted proteoliposomes and molecular dynamics simulations, we demonstrate that both proteins facilitate phospholipid translocation across membrane bilayers, a process tuned by cholesterol and enhanced by deafness-causing TMC1 mutations. We show that this scramblase activity correlates with TMC1-dependent externalization of phosphatidylserine and membrane blebbing in murine auditory hair cells, linking TMC1-dependent membrane homeostasis dysregulation to auditory sensory cell pathology. These findings identify TMCs as a novel family of lipid scramblases, advancing our understanding of MET complex biology and offering mechanistic insight into membrane-driven forms of hereditary deafness.

neuroscience↗

bioRxiv2025

Evolution of hunt, kill, devour in a Vibrio ecospecies

Understanding the molecular and evolutionary basis of adaptation is a central challenge in biology. We have recently identified bacterial "ecospecies": strains that share a well-mixed gene pool with the rest of the species in most of the genome, but carry nearly fixed differences in [~]100 coadapted genes. We hypothesized this structure reflects ecological selection, but ecospecies have so far been characterized only genomically. Here, using "Molassodon" of the marine bacterium Vibrio parahaemolyticus, we provide first experimental evidence linking a multilocus ecospecies genotype to a laboratory phenotype. We find that Molassodon swimming in viscous environments is enhanced by differentiated lateral flagella that no longer drive surface swarming. Using Tn-seq, RNA-seq, and molecular genetics, we link differentiated Type VI secretion and nutrient uptake genes to the swimming phenotype, potentially invoking a natural adaptation related to "hunting". We also identify a convergent genotype also linking lateral flagella and nutrient uptake, suggesting ecological differentiation in V. parahaemolyticus occurs along recurring phenotypic axes and involves borrowing traits from a broader Vibrio gene pool. Together, our results provide first experimental evidence that ecospecies are functionally coherent units and illustrate how they can provide a window into the complex adaptations that shape bacterial ecology.

microbiology↗

bioRxiv2025

UniCure: A Foundation Model for Predicting Personalized Cancer Therapy Response

Predicting drug efficacy across diverse patient contexts remains a major challenge in oncology, as models trained on cancer cell lines often fail to capture patient-specific biology. Emerging biological foundation models and patient-derived technologies offer a promising solution. Here, we present UniCure, the first pre-trained foundation model integrating both omics and chemical foundation models (UCE and Uni-mol) to predict transcriptomic responses to drugs across diverse cellular and tissue contexts, enabling personalized cancer therapy and drug prioritization at the individual level. Rather than encoder/decoder used in traditional models, UniCure utilizes parameter-efficient fine-tuning (PEFT) techniques for optimizing the training process, a novel FlexPert module for modeling flexible drug-cell interactions, and a Maximum Mean Discrepancy (MMD) loss for learning unpaired data. Trained on over 1.8 million perturbation RNA-seq profiles over 22,000 compounds, 166 cell types, and 24 tissues, UniCure achieves high accuracy in predicting both dose-dependent responses and drug combination effects, demonstrating strong generalization across bulk and single-cell transcriptomic data. In particular, fine-tuning on our patient-derived tumor-like clusters and real-world data of 800 profiles enables UniCure to generate individualized therapeutic predictions on patients tissue samples. In addition, UniCure enables patient stratification based on the predicted drug responses, providing a new way for subtyping patients. UniCures drug prioritization was validated across over 1000 patients from pan-cancer cohorts and supported by experiments of candidate therapeutics. By enabling the potential to screen millions of compounds per patient at scale, UniCure represents a biologically grounded tool that could advance personalized precision oncology and accelerate drug discovery.

bioinformatics↗

bioRxiv2025

The effects of E. granulosus PSCs proteins on host immune cells were elucidated through Transcriptomic and Metabolomic analyses

BackgroundEchinococcus granulosus (E. granulosus), a causative agent of zoonotic diseases, is widely prevalent across most regions and primarily affects vital organs such as the liver and lungs. The parasite secretes immunomodulatory substances, and its protoscolex proteins (PSCs) have been identified as potential diagnostic antigens and vaccine candidates. Although transcriptomics and metabolomics approaches have been employed to investigate echinococcosis, the impact of PSCs on the functionality of host immune cells and the expression of immunomodulatory-related genes remains largely unexplored. MethodsIn this study, immunofluorescence analysis was employed to determine the binding of PSCs to lymphocytes. The CCK-8 assay was utilized to evaluate the impact of varying concentrations of PSCs on cell viability, while ELISA was performed to quantify the levels of cytokines such as IL-9 and IL-4. Furthermore, by integrating LC-MS/MS-based metabolomics and RNA-Seq-based transcriptomics technologies, a systematic analysis was conducted to investigate the metabolic reprogramming and differential gene expression in lymphocytes following PSCs treatment, thereby elucidating the molecular mechanisms underlying its immune regulatory effects. ResultsPSCs proteins inhibited lymphocyte proliferation in a concentration-dependent manner, promoted IL-9 secretion at low concentrations, enhanced IL-4 expression at medium concentrations, and continuously up-regulated TGF-{beta} with increasing concentrations. Transcriptomic analysis identified 1,840 differentially expressed genes (991 up-regulated and 849 down-regulated), and KEGG enrichment analysis highlighted metabolic pathways, including phenylalanine metabolism. Gene Ontology (GO) analysis revealed that the functions of these genes were primarily associated with immunometabolic reprogramming and regulation of the intestinal barrier. Metabonomic profiling detected 12 significantly altered metabolites (8 up-regulated and 4 down-regulated). These findings suggest that PSCs reshape the host immune microenvironment via a metabolic-gene network. This study systematically elucidates how PSCs regulate immune cell function and metabolic homeostasis through multiple targets, providing novel insights into the immune escape mechanisms of parasites. ConclusionPSCs can induce host immune metabolic reprogramming, characterized by the activation of pyrimidine metabolism and the inhibition of bilirubin-mediated oxidative stress, through concentration-dependent regulation of Th9 cytokines (IL-9, IL-4/TGF-{beta}) and suppression of the PI3K-Akt signaling pathway. This process remodels the immune tolerance microenvironment. The aim is to elucidate the molecular mechanisms underlying parasite escape via multi-dimensional immunometabolic regulation, thereby providing a novel direction for the targeted treatment of echinococcosis. Author summaryE. granulosus is a zoonotic disease that can cause space-occupying lesions in the liver, lung and other organs, seriously threaten human health and cause economic losses in animal husbandry. It evades immune attack through a variety of mechanisms.This study investigates the regulatory effects of E. granulosus PSCs on host immune cell function, with a focus on their concentration-dependent influence on Th9 cell differentiation and cytokine expression, including IL-9. Through integrative analyses of transcriptomics and metabolomics, we elucidate the molecular mechanisms underlying immunometabolic reprogramming induced by PSCs. Our findings demonstrate that at low concentrations, PSCs promote the secretion of IL-9 associated with Th9 cells, upregulate IL-4/TGF-{beta} signaling, activate pyrimidine metabolism (leading to thymidine accumulation), enhance bile secretion pathways, and increase bilirubin levels to suppress oxidative stress and T-cell function, thereby establishing an immune-tolerant microenvironment. This research provides the first comprehensive illustration of immune microenvironment remodeling mediated by PSCs via multi-omics network regulation, offering novel insights into the treatment of cystic echinococcosis and host-parasite interactions.

immunology↗

bioRxiv2025

Molecular insights into early malignant transition of hepatocellular carcinoma

The molecular continuum of premalignant-to-malignant transition remains elusive in carcinogenesis, primarily due to the difficulty in sampling transitional lesions. Here, we present comprehensive genomic and immunological profiling of 21 very early hepatocellular carcinomas (veHCCs) arising within 17 cancer-prone DNs. Surprisingly, 82% of cancer-prone DNs harbored TERT alterations, suggesting a predisposing rather than causative role in malignancy transition. A substantial increase in CNA burden, rather than SNV, was noted, indicating a role of chromosome instability in malignancy transition. Additionally, veHCC-originating DNs showed immune inactivity, not falling into the prevailing paradigm that HCCs develop in a context of chronic inflammation. 43% of veHCCs showed an inflamed phenotype with relatively mild CNA burden but already exhibited immune evasion features. Two major evolutionary scenarios were thus proposed: 1) CNA-dominant progression and 2) early immune evasion of those with mild CNA burden. Collectively, our findings illustrate previously unexplored molecular paradigms in HCC initiation, highlighting the therapeutic potential of immunotherapy for early intervention.

cancer biology↗

bioRxiv2025

A perturbation proteomics-based foundation model for virtual cell construction

Building a virtual cell requires comprehensive understanding of protein network dynamics of a cell which necessitates large-scale perturbation proteome data and intelligent computational models learned from the proteome data corpus. Here, we generate a large-scale dataset of over 38 million perturbed protein measurements in breast cancer cell lines and develop a neural ordinary differential equation-based foundation model, namely ProteinTalks. During pretraining, ProteinTalks gains a fundamental understanding of cellular protein network dynamics. Our model encodes protein networks and exhibits consistently improved predictive accuracy across various downstream tasks, highlighting its generalization capabilities and adaptability. In cancer cells, ProteinTalks robustly predicts drug efficacy and synergy, identifies novel drug combinations, and, through its interpretability, uncovers resistance-associated proteins. When applied to more complex system, patient-derived tumor xenografts, ProteinTalks predicts potential responses to drugs. Its integration with clinical patient data enhances the prognosis prediction of breast cancer patients. Collectively, we present a foundational model based on proteome dynamics, offering potential for various downstream applications, including drug discovery, and providing a basis for developing virtual cells.

systems biology↗

bioRxiv2024

Apolipoprotein-L1 G1 variant contributes to hydrocephalus but not to atherosclerosis in apolipoprotein-E knock-out mice

IntroductionIn USA, six million individuals with Sub-Saharan ancestry carry two APOL1 high-risk variants, which increase the risk for kidney diseases. Whether APOL1 high-risk variants are independent risk factors for cardiovascular diseases is unclear and requires further investigation. MethodsWe characterized a mouse model to investigate the role of APOL1 in dyslipidemia and cardiovascular diseases. Transgenic mice carrying APOL1 (G0 and G1 variants) on bacterial artificial chromosomes (BAC/APOL1 mice) were crossed with the ApoE knock-out (ApoE-KO) atherosclerosis mouse model. The compound transgenic mice were evaluated for the impact of APOL1 on systemic phenotypes. ResultsApoE-KO mice carrying APOL1-G0 and APOL1-G1 did not show differences in the extent of atherosclerotic lesions or aortic calcification, as evaluated by Sudan IV staining and radiographic examination, respectively. However, [~]20% of ApoE-KO; BAC/APOL1-G1 mice developed hydrocephalus and required euthanasia. The hydrocephalus was communicating and likely was due to excess cerebrospinal fluid produced by the choroid plexus, where epithelial cells expressed APOL1. Single-nuclear RNA-seq of choroid plexus identified solute transporter upregulation and mTORC2 pathway activation in APOL1-G1-expressing epithelial cells. Further, in the All of Us cohort, we found higher hydrocephalus prevalence among individuals with the APOL1-G1 variant in both recessive and dominant models, supporting the mouse findings. ConclusionWhile APOL1-G1 expression in ApoE-KO mice did not worsen cardiovascular disease phenotypes, we uncovered hydrocephalus as a novel APOL1 risk allele-mediated phenotype. These findings extend the spectrum of APOL1-associated pathologies.

molecular biology↗

bioRxiv2024

MethylQUEEN: A Methylation Encoded DNA Foundation Model

DNA 5-methylcytosine (5mC) modification plays a pivotal role in many biological processes, yet 5mC information and pattern hidden behind remains to be explored. Here, we develop Methylation Language Model based on Quintuple Bidirectional Transformer (MethylQUEEN), a novel pre-trained DNA methylation foundation model capable of sensing methylation states and covering the genome-wide methylation landscape. Through tailored methylation-prone pre-training, MethylQUEEN effectively captured epigenetics information hidden within the DNA sequences: it accurately traces DNAs tissue-of-origin, and successfully recovers the expression profile through methylation states. Integrative analysis on MethylQUEENs attention scores also enables us to reveal the unique methylation status of a tissue for precise disease detection, and identifying key regulatory 5mC sites for disease intervention. As a result, MethylQUEEN signifies a new paradigm in methylation analysis for various biological problems. Besides, our study demonstrates the effectiveness of directly integrating methylation information into pre-training, offering new perspectives and methodologies for a range of methylation-related biological processes. It serves as an initial exploration for the development of more comprehensive epigenomic models.

genomics↗

bioRxiv2024

AMPK acts to remove immune barriers to CD8+ T cell-mediated immunity against hepatocellular carcinoma

Dysregulated metabolism in tumor tissues, and para-tumor tissues alike, can lead to immunosuppression, which may underlie cancer development. However, metabolic intervention as a therapeutic strategy has been of no avail. In this study, we explored the anti-cancer therapeutic effect of aldometanib that specifically targets lysosome-associated aldolase to mimic glucose starvation and thereby activates the lysosomal AMP-activated protein kinase (AMPK), a master regulator of metabolic homeostasis. We show that aldometanib inhibits the growth of HCC in an AMPK-dependent manner, allowing hepatoma-bearing mice to survive to mature ages, although aldometanib does not possess cytotoxicity towards HCC or normal cells. Intriguingly, aldometanib exerts anti-cancer effects only in immune-competent host mice, but not in immune-defective mice. We have further found that the HCC tissues in aldometanib-treated mice are massively infiltrated with CD8+ T cells, which is not seen in mice with liver-specific knockout of AMPK. Our findings thus suggest that the metabolic regulator AMPK rebalances the tumor microenvironment to allow the cytotoxic immune cells inside the body to eliminate cancer cells and effectively contain the tumor tissues. The finding that metabolic intervention can make cancer a life-long manageable disease, may potentially usher in a new era of cancer therapy.

immunology↗

bioRxiv2024

Differential metabolic biomarkers between coronary heart disease and diabetes mellitus

BackgroundCoronary heart disease (CHD), the leading cause of death globally, is a complex disease often association with metabolic disorders, particularly when co-occurring with diabetes mellitus (DM). However, the mechanisms underlying the interaction between these two chronic conditions remain poorly understood. MethodsIn this study, we enrolled 320 participants, including 103 healthy controls (HC), 62 individuals with CHD, 44 with DM, and 47 with both CHD and DM (CHDDM)) in the training set, along with 64 participants (20 HC, 18 CHD, 12 DM, and 14 CHDDM) in the test set. Plasma metabolomic profiling was performed using gas chromatograph-mass spectrometry (GC-MS) and ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS)-based multi-platform untargeted metabolomics. ResultsUsing multi-platform metabolomics, we identified 453 distinct metabolites. Through multivariate statistical analysis, we assigned 63, 27, and 56 disease-associated metabolic biomarkers for CHD, DM, and CHDDM, respectively, followed by pathway enrichment analysis. Using an area under the receiver operating characteristic (ROC) curve (AUC) cutoff value > 0.77, we identified 10, 4, and 8 disease-specific biomarkers for CHD, DM, and CHDDM, respectively. Notably, N-Formyl-L-methionine, N-Acetyl-L-methionine, PE 36:1p (18:0p/18:1), PE 36:2p (18:0p/18:2), and PE 34:2p (16:0p/18:2) were predominant in CHD, glycerophosphocholine and turanose were distinctive to DM, and xanthine and PE 38:5p (18:1p/20:4) were unique to CHDDM. These disease-specific metabolites showed superior diagnostic performance in their respective conditions. Additionally, 23 metabolites were identified as potential biomarkers for assessing the risk of CHD in DM patients. ConclusionThis comparative metabolomics approach provided new insights into disease-specific markers and pathogenic pathways, offering potential for improved diagnosis and management of CHD and DM.

molecular biology↗

bioRxiv2024

SOX2-induced IL-1alpha-mediated immune suppression drives epithelial dysplasia malignant transformation

Squamous cell carcinomas (SCC) are often preceded by potentially malignant precursor lesions, most of which remain benign. The terminal exhaustion phenotypes of effector T-cells and the accumulation of myeloid-derived suppressor cells (MDSC) have been thoroughly characterized in established SCC. However, it is unclear what precancerous lesions harbor a bona fide high risk for malignant transformation and how precancerous epithelial dysplasia drives the immune system to the point of no return. Here we show that expression of SRY-box transcription factor 2 (SOX2) in precancerous lesions imparts an irreversible risk that recruits suppressive myeloid cells by promoting the release of CCL2. We developed a unique genetically engineered mouse model (GEMM) to recapitulate the malignant transformation of epithelial dysplasia to SCC in the oral mucosa with high histologic and phenotypic fidelity. Using a combination of longitudinal human specimens and the Sox2-GEMM, we found that the myeloid cells in precancerous epithelial dysplasia exhibit a distinctive dichotomous profile featuring high levels of IL-1-SLC2A1 and low levels of type-I interferon (IFN-I) signatures, which occurs before SCC emerges histologically. Brief priming of myeloid cells with IL-1 desensitizes them to IFN-I agonists and makes myeloid-derived suppressor cells (MDSC) even more suppressive of T-cell activation. Mechanistically, IL-1 activation represses the expression of DHHC3/7 enzymes, which are responsible for the palmitoylation of stimulator of interferon genes (STING). Early blockade of IL1 signaling using pharmacologic and genetic approaches similarly reduces MDSC and SLC2A1high myeloid cells, suppresses epithelial dysplasia transformation, and extends survival. This work establishes a previously unrecognized SOX2-CCL2-IL1 pathway that leads to irreversible immune escape when precancerous epithelial lesions transform.

cancer biology↗

bioRxiv2024

NaDyNet: A Toolbox for Dynamic Network Analysis of Naturalistic Stimuli

Experiments with naturalistic stimuli (e.g., listening to stories or watching movies) are emerging paradigms in brain function research. The content of naturalistic stimuli is rich and continuous. The fMRI signals of naturalistic stimuli are complex and include different components. A major challenge is isolate the stimuli-induced signals while simultaneously tracking the brains responses to these stimuli in real-time. To this end, we have developed a user-friendly graphical interface toolbox called NaDyNet (Naturalistic Dynamic Network Toolbox), which integrates existing dynamic brain network analysis methods and their enhanced versions. The main features of NaDyNet are: 1) extracting signals of interest from naturalistic fMRI signals; 2) incorporating six commonly used dynamic analysis methods and three static analysis methods; 3) enhanced versions of these dynamic methods by adopting inter-subject analysis to eliminate the effects of non-interest signals; 4) performing K-means clustering analysis to identify temporally reoccurring states along with their temporal and spatial attributes. We then introduced the rationale for incorporating inter-subject analysis to improve existing dynamic brain network analysis methods, and presented numerous examples. We also summarized research progress in comparing methodological efficacy, offered our recommendations for method selection in dynamic network analysis, and discussed the limitations of current approaches and directions for future research. We hope that this open source toolbox will promote the development of naturalistic neuroscience. The toolbox is available at https://github.com/yuanbinke/Naturalistic-Dynamic-Network-Toolbox.

neuroscience↗

bioRxiv2024

MARVEL: Microenvironment Annotation by Supervised Graph Contrastive Learning

Recent advancements in in situ molecular profiling technologies, including spatial proteomics and transcriptomics, have enabled detailed characterization of the microenvironment at cellular and subcellular levels. While these techniques provide rich information about individual cells spatial coordinates and expression profiles, extracting biologically meaningful spatial structures from the data remains a significant challenge. Current methodologies often rely on unsupervised clustering followed by cell type annotation based on differentially expressed genes within each cluster and most of the time will require other information as the reference (e.g., HE-stained images). This is labor-intensive and demands extensive domain knowledge. To address these challenges, we propose a supervised graph contrastive learning framework, MARVEL. MARVEL is a supervised graph contrastive learning method that can effectively embed local microenvironments represented by cell neighbor graphs into a continuous representation space, facilitating various downstream microenvironment annotation scenarios. By leveraging partially annotated examples as strong positives, our approach mitigates the common issues of false positives encountered in conventional graph contrastive learning. Using real-world annotated data, we demonstrate that MARVEL outperforms existing methods in three key microenvironment-related tasks: transductive microenvironment annotation, inductive microenvironment querying, and the identification of novel microenvironments across different slices.

bioinformatics↗

bioRxiv2024

Proteolysis-targeting vaccines (PROTAVs) for robust tumor combination immunotherapy

Protein/peptide subunit vaccines are promising to promote the tumor therapeutic efficacy of immune checkpoint blockade (ICB). However, current protein/peptide vaccines elicit limited antitumor T cell responses, leading to suboptimal therapeutic efficacy. Here, we present proteolysis-targeting vaccines (PROTAVs) that facilitate antigen proteolytic processing and cross-presentation to potentiate T cell responses for robust ICB combination immunotherapy of melanoma. PROTAVs are modular conjugates of protein/peptide antigens, E3 ligase-binding ligands, and linkers. In antigen-presenting cells (APCs), PROTAVs bind to E3 ligases to rapidly ubiquitinate PROTAV antigens, facilitating antigen proteolytic processing by proteasome, and thereby promoting antigen cross-presentation to T cells and potentiating CD8+ T cell responses. We developed a melanoma PROTAV using a tandem peptide of trivalent melanoma-associated antigens. Co-delivered by lipid nanoparticles (LNPs) with bivalent immunostimulant adjuvants, this PROTAV promotes the quantity and quality of melanoma-specific CD8+ T cells in mice. Further, combining PROTAV and ICB ameliorates the immunosuppressive melanoma microenvironment. As a result, PROTAV and ICB combination enhances melanoma complete regression rates and eradicated 100% large BrafV600E melanoma without recurrence in syngeneic mice. PROTAVs hold the potential for robust tumor combination immunotherapy.

bioengineering↗

bioRxiv2024

The connectional diaschisis and normalization of cortical language network dynamics after basal ganglia and thalamus stroke

Stroke affecting the basal ganglia and thalamus can lead to language deficits. In addition to the lesions direct impact on language processing, connectional diaschisis involving cortical-subcortical interactions also plays a critical role. This study investigated connectional diaschisis using the "dynamic meta-networking framework of language" in patients with basal ganglia and thalamus stroke, analyzing longitudinal resting-state fMRI data collected at 2 weeks (n = 32), 3 months (n = 19), and one year post-stroke (n = 23). As expected, we observed dynamic cortico-subcortical interactions between cortical language regions and subcortical regions in healthy controls (HC, n = 25). The cortical language network exhibited dynamic domain-segregation patterns in HCs, severely disrupted in the acute phase following stroke. The connectional diaschisis manifested as dual effects characterized by both hypo- and hyper-connectivity, which positively and negatively correlated with language deficits, respectively. State-specific changes in nodal and topological properties were also identified. Throughout language recovery, cortical language network dynamics gradually normalized toward sub-optimal domain-segregation patterns, accompanied by the normalization of nodal and topological properties. These findings underscore the crucial role of cortico-subcortical interactions in language processing.

neuroscience↗