Biology subjects

Cai, L.

Publications and source records attributed to Cai, L..

At least 19 recordsLinked to original sources

bioRxiv2026

NeoOmni Atlas: an integrated database for pan-cancer neoantigen derived from proteogenomics analysis

Tumor neoantigens derived from diverse molecular aberrations are important targets for personalized cancer immunotherapy, but existing resources incompletely integrate mass-spectrometry-supported canonical and non-canonical antigen evidence across cancer types. NeoOmni Atlas is an interactive pan-cancer T-cell epitope resource, built via proteogenomic reanalysis of 7,561 immunopeptidomics datasets comprising 511 million raw spectra from 2,341 samples across 26 cancer types. The analysis yielded 872,414 eluted canonical peptides and 198,961 non-canonical peptides spanning 18 molecular alteration classes, along with 759,386 unique ligands presented by 322 human leukocyte antigen (HLA) alleles. Relative to existing repositories, NeoOmni Atlas covers a broader range of cancer types, includes 60 additional HLA alleles, and catalogs 369,781 novel ligands. A computational tumor-specific filtering framework identified 316,126 tumor-associated epitopes and 61,231 tumor-specific epitopes. Tumor-specific epitopes showed stronger predicted binding affinity than tumor-associated epitopes for HLA class I A and B alleles, and 65 conserved tumor-specific epitopes recurrent across more than 10 tumor types represent potential shared antigen candidates. NeoOmni Atlas also prioritizes candidate tumor-associated antigens including PMEL (gp100:209-217) and CSPG4, and supports browsing, searching, and downloading by cancer type, human leukocyte antigen allele, peptide sequence, and source event. NeoOmni Atlas is freely accessible at http://modinfor.com/NeoOmni-Atlas/.

bioinformatics↗

bioRxiv2026

Hybrid Mechanistic-Neural Modeling of Concentration-Time Dynamics Generalizes Human Pharmacokinetics Prediction Across Unseen Chemical Space

The pharmacokinetics concentration-time profile encodes ADME dynamics. Its optimization in drug discovery curbs late-stage attrition, yet forecasting human pharmacokinetics from chemical structure remains difficult. Machine learning models ignore continuous ADME dynamics, whereas physiologically based pharmacokinetics (PBPK) models demand unavailable parameters and impose rigid, misspecified compartmental structures. We present PK-MUSE (PharmacoKinetic hybrid Mechanistic Universal System Equation), augmenting a two-compartment model with bounded, state- and time-dependent neural corrections to elimination and intercompartmental distribution. Molecular structure parameterizes compound-specific behavior, while learned corrections refine dynamic equations. Across scaffold and temporal out-of-distribution evaluations, PK-MUSE achieved the lowest mean exposure-endpoint MdFE, reducing MdFE by 7.2%, 45.2%, and 26.2% versus state-of-the-art machine learning (HML- RF), deep learning compartmental (DeepCt), and PBPK (PK-Sim) models. Interrogating the corrections localized misspecification to distribution rather than elimination. By extending mechanistic simulators with data-driven constrained neural dynamics that preserve interpretability and generalize to unseen chemotypes, PK-MUSE advances digital twins for drug discovery.

pharmacology and toxicology↗

bioRxiv2026

MCT1 activity defines an aggressive metabolic phenotype and therapeutic target in non-small cell lung cancer

Lactate uptake through monocarboxylate transporter 1 (MCT1) is associated with aggressive disease in non-small cell lung cancer (NSCLC), but how lactate transport supports tumor metabolism remains incompletely understood. Integrating stable isotope tracing, metabolomics, and transcriptomics across patient tumors, animal models, and cultured cells, we show that elevated lactate utilization and SLC16A1 expression correlate with worse clinical outcomes in NSCLC. Although MCT1 inhibition with AZD3965 does not significantly impair tumor growth as a monotherapy, it depletes purine-associated metabolite pools, alters redox balance, and induces a conserved transcriptional response involving MYC target dysregulation and suppression of nucleotide metabolism genes. Addition of exogenous hypoxanthine, or expression of the E. coli-derived NADH-producing enzyme soluble transhydrogenase, partially rescues the effects of MCT1 inhibition. Importantly, this metabolically compromised state sensitizes NSCLC to nucleotide-targeting chemotherapies, including pemetrexed. These findings reveal that MCT1 mediated lactate uptake sustains nucleotide homeostasis in NSCLC, and that its inhibition exposes a context-dependent vulnerability that can be leveraged to enhance chemotherapy efficacy.

cancer biology↗

bioRxiv2026

A positively selected microRNA controls a reversible aging program in striated muscle

Aging is the primary risk factor for most chronic diseases and is characterized in striated muscle by progressive functional decline, mitochondrial dysfunction, and chronic inflammation. The miR-128-1 locus resides within a positively selected haplotype on chromosome 2q21.3 associated with variation in grip strength, pulmonary function, and cardiometabolic traits in humans. Here, we show that antisense oligonucleotide-mediated inhibition of miR-128-3p restores muscle mass and function in aged mice, improves cardiac function while limiting adverse remodeling following myocardial infarction, and ameliorates skeletal and cardiac muscle pathology in mouse and pig models of Duchenne muscular dystrophy. Across these contexts, miR-128-3p inhibition induces a conserved transcriptional response characterized by activation of mitochondrial programs and suppression of inflammatory and fibrotic signaling, resembling the effects of established longevity interventions. These findings identify miR-128-3p as a regulator of a conserved aging-associated program and establish its inhibition as a strategy to restore tissue function across aging-related muscle pathologies.

molecular biology↗

bioRxiv2026

Synthesis cost is a hidden driver of convergent amino acid composition in plastid ribosomal proteins

Protein evolution is a walk in the evolutionary space directed by mutation and selection. While functional and structural constraints serve as the main determinant of amino acid substitution in most proteins, synthesis cost and mutational bias can also alter the direction and rate of amino acid evolution, especially in systems experiencing relaxed selection. Here, we focused on the highly expressed plastid ribosomal proteins (PRP), which comprise 58 conserved proteins encoded by both plastid and nuclear genomes. Relaxed selection has been repeatedly identified in three distantly related plant lineages, providing a valuable comparative framework to investigate the significance of synthesis cost and mutation. We first demonstrated that the hemiparasitic tribe Cymbarieae (Orobanchaceae) represented a new case where concerted cyto-nuclear rate elevation occurs in their PRP. Further investigation revealed convergent shifts in amino acid composition in all four plant lineages attributable to arginine-to-lysine and methionine-to-isoleucine/valine/leucine substitutions. The replacement residues were biophysically similar but had lower molecular weight and shorter side chains, which significantly destabilized protein folding as demonstrated by protein structure modeling. We found that the composition shifts ran counter to the expectation of mutational bias but were consistent with the expectation of synthesis cost minimization, which is potentially adaptive for highly expressed PRP. Further, cost minimization significantly influenced all conservative substitutions between biophysically similar amino acids but was absent in non-conservative substitutions. We thus propose cost minimization as a secondary selective drive for protein evolution in PRP, unmasked in lineages and sites with relaxed selection on their function.

evolutionary biology↗

bioRxiv2026

Tumor control of lysosomal acidification promotes lipoprotein assimilationand ferroptosis resistance

Lysosomes are acidic organelles that fuel cancer progression by facilitating nutrient acquisition and metabolic adaptation, yet the determinants through which cancer cells sustain specialized lysosomal functions are not fully delineated. Notably, assimilation of dietary antioxidants within lipoproteins, a lysosome-dependent process, protects tumors from ferroptosis, an oxidative form of cell death, raising the possibility that tumors evolve mechanisms to enhance this process. Here, we applied genetic screens to identify regulators of lysosome-dependent lipoprotein assimilation and ferroptosis resistance and identified ZNF217, a frequently amplified transcriptional regulator in human cancers, as a driver of tumor lysosomal function and ferroptosis resistance. ZNF217 promoted lipoprotein assimilation through transcriptional maintenance of RAB11FIP4, an endolysosomal protein. Loss of either ZNF217 or RAB11FIP4 impaired lysosomal acidification across multiple cancer types, leading to defective lipoprotein assimilation, increased lipid peroxidation, ferroptosis sensitivity, and impaired tumor growth. Mechanistically, RAB11FIP4 boosts lysosomal acidity through maintenance of RAB7A activity. Finally, disruption of ZNF217 in breast cancer cell lines and patient-derived organoids, a tumor context linked to ZNF217 expression, reduced lysosomal acidity and impaired cancer growth through increased ferroptosis sensitivity. Together, we identify transcriptional regulation of lysosomal acidification as a key metabolic adaptation that enables extracellular antioxidant acquisition and tumor progression.

cancer biology↗

bioRxiv2026

Spatial second-order features predict glioma malignant transformation

Isocitrate dehydrogenase (IDH) mutant gliomas often transform from low to high grade aggressive tumors. The genetic and molecular drivers of this Malignant Transformation (MT) are poorly understood, and predicting whether a patient will undergo MT is an unmet challenge of high clinical relevance. To stratify patients by MT risk, we applied integrated spatial DNA and RNA profiling to biopsies from 18 retrospective glioma patients which will either remain stable, undergo MT or have already transformed. The resulting dataset consisted of >600,000 single cells, measuring 962 DNA loci, 1150 RNAs, and their spatial locations. Using this dataset, we found that genetic copy number alterations (CNAs), cell types compositions, and cellular neighborhoods did not predict MT. Instead, second order effects i.e. pairwise interactions, are highly predictive of future transformation. First, we identified abnormal chromosomal contact patterns that clearly separate future stable versus future MT samples. Second, we identified 24 ligand-receptors (LR) pairs cross-expressed in neighboring cells as the main molecular factors predictive of transformation and recurrences. We then validated a cross-expressing pair of ENPP2-LPAR1 interactions with a separate cohort of patient samples. In addition, using the LR+ cell pairs as an anchor, we identified cell signaling-specific gene expression programs that can predict from bulk or single cell RNAseq data the time to recurrence. We used a cell-interaction-based foundation model (CIFM) optimized on the spatial RNA data in forward simulations and identified potential myeloid signaling factors involved in MT. Lastly, we analyzed the effect of detection sensitivity on the ability to capture pertinent LR+ neighboring cells by down-sampling transcript and showed that the ability to detect cross-expressing signaling LR transcripts (typically <10 copies per cell) decays rapidly with lower sensitivity, but is more robust to down-sampling of the areas of the tissue imaged. The importance of second-order features suggests that increased depth and dimensionality of data on a smaller quantity of samples can provide valuable insight, and that high-sensitivity and multiple-modalities spatial approaches can help identify markers to risk-stratify patients, aid in therapeutic decision making, and uncover potential therapeutic targets.

cancer biology↗

bioRxiv2026

The liver compensates for zonal loss of glucose 6-phosphatase to prevent glycogen storage disease

The liver is organized into spatial zones with distinct metabolic roles, but how this architecture contributes to disease remains unclear. Glucose production is concentrated in periportal hepatocytes and depends on G6PC1; loss of this enzyme causes glycogen storage disease type Ia (GSD1a). We tested whether G6PC1 loss in specific zones, including its primary periportal location, is sufficient to cause disease. Unexpectedly, loss of G6pc1 in any single zone caused local glycogen accumulation but did not produce the systemic metabolic abnormalities or liver tumors seen after whole-liver deletion. Instead, disease developed only after near-complete loss of G6pc1 across the entire liver. These findings show that organ-wide compensation preserves metabolic homeostasis and suppresses tumorigenesis despite localized disruption. TeaserSpatial G6pc1 loss causes local glycogen storage without systemic metabolic disease.

physiology↗

bioRxiv2026

A Label-Free Multi-Metric Pipeline for Benchmarking Single-Cell RNA-Sequencing Clustering and Testing the Reproducibility of Cell-Type Heterogeneity

A discovered sub-population from single-cell transcriptomic data is only meaningful if it is reproducible, yet clustering is usually done with one method on one embedding and rarely tested. We present a label- free, multi-metric pipeline that reframes clustering as an auditable, methods-blind decision and separates two notions of stability that are commonly conflated: reproducibility under cell resampling (bootstrap) and reproducibility under re-embedding (retraining the representation). The pipeline evaluates seven clustering configurations across cluster counts using five non-redundant quality metrics. As a whole- dataset control on a mouse retinal atlas, it recovers an eight-cell-type annotation at 96.3% accuracy (adjusted Rand index, ARI = 0.91) without labels. We then validate the discovery mode on two cell types with opposite ground truth. On bipolar cells, which have well-established subtypes, the pipeline accepts the sub-structure: across-embedding reproducibility rises with cluster number to a high plateau (mean pairwise ARI [~]0.93 near the [~]15 known bipolar subtypes), with quality metrics improving in parallel. On rod photoreceptors, treated as homogeneous, it rejects over-clustering: the metric-selected partition passes a bootstrap-stability check but is not reproducible when the embedding is retrained (mean pairwise ARI = 0.69), and the metrics do not improve with cluster number. On synthetic data, the test recovers real structure down to a 5% subpopulation while rejecting null data (high sensitivity and specificity). Bootstrap stability alone is therefore insufficient evidence for sub-population; the across-embedding test discriminates real sub-structure from over-clustering and applies to any cell type as a reproducible alternative to single-method, single-embedding clustering.

bioinformatics↗

bioRxiv2026

Spatial Topology Reveals Biologically Distinct Recurrent Motifs in Colorectal Cancer

Most spatial transcriptomic analyses of solid tumors focus on individual cell states or single-sample spatial domains rather than on recurrent multicellular tissue architectures shared across patients, and typically depend on predefined cell-type annotations or compartment definitions. We developed STORM (Spatial Topology analysis of Recurrent Motifs), an unsupervised graph-attention variational autoencoder that learns recurrent spatial motifs directly from cell-level graph structure and molecular profiles without cell-type labels, manual annotation, or predefined compartments. We applied to 32 Xenium sections from 16 patients with paired early-onset (EOCRC) and average-onset (AOCRC) colorectal cancer, STORM identified 10 recurrent motifs that self-organized into tumor-parenchymal, stromal, and immune macro-compartments. Among these, the Desmoplastic Fibrotic Barrier (DFB) motif, a CAF- and ECM-rich boundary architecture, was associated with restricted CD8+ T-cell geodesic access to tumor cores independently of CD8+ abundance, as demonstrated by abundance-normalized neighborhood enrichment statistics and within-sample mixed-effects models. EOCRC selectively amplified this barrier-exclusion architecture, exhibiting tighter tumor parenchyma, denser DFB shells, and a DFB-specific ECM activation program that yielded an age-specific prognostic signature in TCGA-COAD. Translation of motif macro-classes to H&E images via a Vision Transformer classifier produced an image-derived DFB-barrier composite that predicted overall survival in advanced-stage TCGA colorectal cancer. STORM provides an annotation-free framework for discovering recurrent spatial motifs and identifies a fibroblast barrier architecture whose topological association with immune exclusion is independent of effector abundance, amplified in early-onset disease, and translatable to a deployable pathology-based prognostic biomarker.

bioinformatics↗

bioRxiv2026

The SEA-AD DREAM Challenge: Community benchmarking human and AI agent solutions for Alzheimer's disease neuropathology prediction from single-nucleus transcriptomics

Single-nucleus transcriptomic atlases offer an unprecedented opportunity to connect cellular molecular states with Alzheimer's disease (AD) neuropathology, but whether these profiles encode reproducible, predictive information about pathological burden remains unclear. We present the SEA-AD DREAM Challenge, an open, international, model-to-data competition built on the Seattle Alzheimer's Disease Brain Cell Atlas to predict Alzheimer's disease neuropathological severity from single-nucleus RNA-sequencing data. Participants developed containerized models to predict categorical neuropathological staging, including overall Alzheimer's disease neuropathologic change, Braak stage, Thal phase, and CERAD score, as well as quantitative amyloid-{beta} and phospho-tau burden measured by 6E10 and AT8 immunohistochemistry. Across 17 eligible teams from 15 countries, the crowdsourcing framework enabled systematic comparison of diverse computational approaches and surfaced a broad landscape of modeling strategies and candidate predictive features. Top-performing methods achieved near-perfect prediction of categorical staging, with the best submission reaching a quadratic weighted kappa of 1.0 for the Overall AD Neuropathological Change score (ADNC), and competitive prediction of quantitative pathological burden in held-out data, with a best concordance correlation coefficient of 0.48. Post hoc perturbation analyses revealed that top categorical-stage predictions relied heavily on donor-level metadata-driven signals rather than transcriptomic features, whereas quantitative pathology prediction was more robust and supported by transcriptomic and cell-type-associated features with potential biological relevance to AD progression. The challenge also introduced the first AI Agent Track in a DREAM Challenge, providing an early benchmark for autonomous and human-guided agentic model development in single-cell neuroscience. This work demonstrates that single-nucleus transcriptomes encode substantial information about Alzheimer's disease pathology, establishes a reproducible benchmark for molecular neuropathology prediction, and highlights critical principles for designing privacy-preserving, leakage-aware community challenges using deeply phenotyped human brain data.

neuroscience↗

bioRxiv2026

UcTCRp: a TCRβ-based framework for quantitative MAIT- and iNKT-associated repertoire-state profiling

MAIT and iNKT cells are conventionally identified using invariant or semi-invariant TCR chains, antigen-loaded tetramers, or transcriptomic phenotypes. These requirements limit their detection in public and clinical immune-repertoire datasets that contain only TCR{beta} sequences. Here we present UcTCRp, a TCR{beta}-only framework for profiling MAIT- and iNKT-associated repertoire states in bulk immune repertoires. UcTCRp integrates V-gene context and CDR3{beta} sequence features using a transformer-based representation pretrained on more than one million TCR{beta} sequences and supervised with curated cross-species MAIT, iNKT and conventional T cell references. The framework defines conserved model-informative TCR{beta} features, uses V-matched negative sampling to reduce germline-segment shortcuts, and generalizes across independent human and mouse datasets. In paired scRNA-seq/scTCR-seq datasets, UcTCRp recovered transcriptome-defined MAIT and iNKT cells and identified additional MAIT-like candidates supported by receptor evidence but missed by expression-only annotation. Bulk calibration against paired single-cell references and synthetic spike-in experiments established operating characteristics for repertoire-level abundance estimation. These results establish unpaired TCR{beta} repertoires as an actionable substrate for reconstructing unconventional T cell-associated immune states, enabling archived repertoire resources to be repurposed for systems-level studies of tissue immunity, disease and therapeutic response.

bioinformatics↗

bioRxiv2026

Distinct and shared synergistic effects of high-fat and high-iron diets on liver tumorigenesis and transcriptomic remodeling in mice

Background and AimsThe incidence of hepatocellular carcinoma (HCC) is increasingly driven by metabolic risk factors, including obesity and iron overload. Although high-fat and high-iron diets independently promote hepatocarcinogenesis, their shared and distinct molecular effects remain unclear. We sought to define how dietary fat and iron differentially shape HCC development. ApproachMale C3HeB/FeJ mice were exposed to long-term high-fat (HFD), high-iron (HID), or combined (HFD+HID) diets for 16.5 months. Tumor burden, hepatic iron distribution, mTOR signaling, oxidized phospholipid (OxPL) accumulation, and transcriptomic alterations across normal, adjacent non-tumor, and tumor liver tissues were analyzed using biochemical, histological, and RNA sequencing approaches. ResultsAll diets induced HCC with comparable tumor burden. HID increased iron levels in non-tumor liver tissue but resulted in relative iron depletion within tumors, indicating tumor-specific iron utilization. Tumors from all diet groups showed robust mTOR activation and increased OxPL accumulation, with stronger oxidative stress signatures in HFD and HFD+HID tumors. Transcriptomic analyses revealed conserved oncogenic programs alongside diet-specific signatures, with HFD exerting a dominant effect on metabolic reprogramming and gene dysregulation, whereas HID preferentially enhanced immune and inflammatory signaling. Progressive, monotonic changes in gene expression were observed across disease stages. Cross-species analyses linked diet-induced mouse tumors to immunologically "hot" human HCC subtypes. ConclusionsDietary fat and iron promote HCC through overlapping yet distinct molecular pathways, highlighting metabolic and immune mechanisms as key targets in diet-associated liver cancer.

cancer biology↗

bioRxiv2026

Whole-organism spatial transcriptomics at single-cell resolution in C. elegans

Spatial transcriptomics has advanced our understanding of tissue organization by mapping gene expression in its native context yet applying these techniques to whole organisms remains a significant challenge. Caenorhabditis elegans is well-suited to whole organism-level analysis because its compact size, transparency, reproducible anatomy, and genetic tractability make it possible to link molecular and cellular changes to circuit function and behavior within the same animal. However, current transcriptomic approaches in C. elegans are often limited by spatial resolution or multiplexing capacity, making it challenging to profile multiple gene expression patterns across intact worms while preserving spatial context. Here, we present a single-molecule fluorescence in situ hybridization workflow that enables multiplex imaging with single-cell resolution across the entire worm. This approach allows sequential imaging of one gene per fluorescent channel using two channels across 20 hybridization rounds, enabling the profiling of up to 40 genes while preserving spatial context. We further provide a curated marker-gene panel for reproducible neuron identification, which, together with probabilistic assignment of transcripts to segmented nuclei, enables quantitative measurements of gene expression levels. We used this method to identify up to 86 neuronal classes and reveal sex- and neuron-specific expression patterns at single-cell resolution. Together, these results establish a scalable framework for the spatial analysis of gene expression and cell identity in intact C. elegans.

neuroscience↗

bioRxiv2026

Global Convergence of Plant Functional Trait Composition in the Anthropocene

Since the onset of European colonial expansion, humans have accelerated species migration across continents, reshaping plant functional composition and associated ecosystem processes. Plant functional traits-such as leaf area, plant height, or rooting depth-are structured along major axes of variation, including size and leaf economics, that reflect ecological strategies. While human-mediated changes in this trait space have been documented regionally or for specific taxa, there exists no global, grid cell-level quantification of past shifts across major axes of trait variation. Here, we link global citizen science plant occurrence data with data on 37 above- and below-ground traits, and information on native and introduced status for each occurrence. Using dimension-reduction on grid cell-level trait means and introduced species status as a proxy for anthropogenic change, we identify three major axes of functional variation: the size, leaf economics, and life-span axes. By comparing past (native-only) and present-day trait distributions in 3D trait space and geographically, we find prominent region-specific shifts along all three axes. Overall, functional composition converges toward (mostly) smaller, more acquisitive, and shorter-lived assemblages, with region-specific differences in which axis shifts are most pronounced. These results provide the first global estimate of how human-mediated plant introductions have altered ecosystem functional composition in the past centuries, highlighting the spatial patterns and trait dimensions most affected by anthropogenic pressures.

ecology↗

bioRxiv2026

UFMylation of Pyruvate Dehydrogenase Regulates Mitochondrial Metabolism

The ubiquitin-fold modifier 1 (UFM1) post-translational modification (PTM), or UFMylation, regulates protein homeostasis and is essential for human development. Yet the roles of the de-UFMylase, UFM1-specific peptidase 2 (UFSP2), which removes UFM1 from UFMylated proteins, remain poorly characterized. Here, we demonstrate that UFMylation and UFSP2 regulate mitochondrial metabolism. Quantitative proteomics in UFSP2-deficient cells revealed the accumulation of many proteins previously unknown to be impacted by UFMylation. These included components of the mitochondrial ribosome, electron transport chain (ETC), and pyruvate dehydrogenase (PDH) complex. Functional analyses demonstrated that excessive UFMylation in UFSP2-deficient cells increases mitochondrial respiration, glucose oxidation in the tricarboxylic acid (TCA) cycle, and PDH enzymatic activity. We identified dihydrolipoamide S-acetyltransferase (DLAT), the E2 component of PDH, as a direct UFMylation substrate, with lysine 118 (K118) as the primary conjugation site. Mutating K118 to arginine (K118R) abolished DLAT UFMylation and reduced pyruvate oxidation, identifying this modification as an activator of PDH. These findings reveal a UFMylation-based regulatory mechanism that controls mitochondrial function by inducing utilization of pyruvate as a TCA cycle fuel.

biochemistry↗

bioRxiv2026

Rewiring of 3D Enhancer-Promoter Interactome Underlies Diabetic Endothelial Dysfunction

BackgroundDiabetic vascular complications are driven by endothelial dysfunction, yet the role of 3D genome organization in this process is unknown. We sought to define the alterations in chromatin architecture in diabetic endothelium and identify the key regulators involved. MethodsWe generated a high-resolution 3D epigenomic atlas of diabetic endothelial cells from mouse models and human subjects using H3K27ac HiChIP, complemented by ChIP-seq, ATAC-seq, and RNA-seq. A human cohort was used to assess protein expression in diabetic versus non-diabetic endothelial cells. To identify JUNB-interacting proteins, we performed rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME), with protein-protein interaction validated by co-immunoprecipitation. Functional validation was performed using in vitro, ex vivo, and in vivo approaches, including endothelial-specific knockdown in a diabetic hindlimb ischemia model. ResultsMulti-omics profiling revealed extensive enhancer reprogramming in diabetic endothelium, with AP-1 binding motifs being consistently and selectively enriched in downregulated enhancers across three distinct diabetic models. Analysis of a human cohort confirmed significantly reduced JUNB protein levels in diabetic endothelial cells. We identified widespread disruption of JUNB-anchored enhancer-promoter interactions, which underlies transcriptional repression of key endothelial genes. RIME and co-immunoprecipitation established the E3 ubiquitin ligase RBBP6 as a direct JUNB interactor that promotes its polyubiquitination and proteasomal degradation in response to hyperglycemia. Human cohort analysis further showed reciprocal elevation of RBBP6 in diabetic endothelial cells. Either JUNB overexpression or RBBP6 knockdown restored enhancer-promoter connectivity, reactivated vasoprotective transcriptional programs, and rescued endothelial function. Critically, endothelial-specific knockdown of Rbbp6 in diabetic mice restored endothelium-dependent vasorelaxation and improved perfusion recovery after hindlimb ischemia, independent of systemic glucose levels. ConclusionsOur study unveils a novel mechanism whereby hyperglycemia induces enhancer reprogramming and disrupts endothelial 3D genome architecture through RBBP6-mediated degradation of JUNB. The RBBP6-JUNB axis is established as a crucial link between metabolic stress and epigenomic reprogramming in vascular disease, presenting a promising therapeutic target for diabetic vasculopathy.

genomics↗

bioRxiv2026

Transforming Histology into Virtual Multiplex Immunofluorescence to Decode Prognostic Spatial Immunity in Hepatocellular Carcinoma

The spatial organization of the tumor immune microenvironment (TIME) drives hepatocellular carcinoma (HCC) prognosis but remains unquantifiable on routine H&E slides. Here, we present HCCExplorer, a deep learning framework that translates H&E into virtual multiplex immunofluorescence (mIF) and uses multi-modal graph learning to decode spatial survival signals. Trained on 30 H&E-mIF slide pairs, HCCExplorer evaluated a 1,813-slide multi-center cohort. It achieved superior overall survival stratification over clinical indices and state-of-the-art pathology and protein foundation models, yielding a concordance index of 0.71 and a Hazard Ratio (HR) of 15.46 (P < 0.001), maintaining stability across three external cohorts. Beyond risk stratification, interpretation of model features identified M1 macrophage infiltration as a protective determinant (HR=0.40, P < 0.05). Furthermore, it uncovered a protective "Containment Niche" at the invasion frontier (HR=0.02, P < 0.01), featuring macrophages co-localizing with Foxp3+ Tregs and CD4+ T cells. Ultimately, HCCExplorer provides actionable, spatially-resolved biomarkers from conventional histology for precision HCC management.

pathology↗