Biology subjects

Li, J.

Publications and source records attributed to Li, J..

At least 235 records · Page 13Linked to original sources

bioRxiv2026

Application of a High-Biomimetic Tumor Organoid-CAF Co-Culture Model for the Efficacy Evaluation of CAR-T Drugs

The tumor microenvironment (TME) is a complex ecosystem composed of tumor cells, cancer-associated fibroblasts (CAFs), immune suppressive cells, and the extracellular matrix (ECM), playing a crucial role in tumor development and CAR-T cell therapy efficacy. CAR-T therapy has shown promise in hematological malignancies but faces challenges in solid tumors due to the TMEs ability to suppress CAR-T cell infiltration, proliferation, and cytotoxicity. Traditional drug evaluation models, such as 2D cell cultures and animal models, have significant limitations due to oversimplification of the in vivo environment or physiological differences between species. Organoid models offer a more biomimetic approach but often fail to fully recapitulate the TMEs complexity and heterogeneity. Our research developed a tumor organoid and CAF co-culture model using the IBAC co-culture chip, demonstrating that CAFs significantly impact CAR-T cell therapy efficacy by forming physical (e.g., fibronectin) and chemical (e.g., IL-10) barriers that prevent CAR-T cell infiltration and cytotoxicity. This model provides a high-biomimetic platform for investigating the TMEs effects on CAR-T therapy and highlights the importance of incorporating a comprehensive stromal component into in vitro models to enhance their predictive power for cancer treatment.

cell biology↗

bioRxiv2026

Bacterial internalins exploit E-cadherin to promote head-neck tumor metastasis and drug resistance

Head and neck squamous cell carcinoma (HNSCC) is an aggressive malignancy characterized by local invasion, lymph node metastasis, and therapeutic resistance. Chronic periodontal disease has been linked to HNSCC progression, yet the responsible pathogens and underlying molecular mechanisms remain unclear. Here, we show that the keystone periodontal pathogen Porphyromonas gingivalis promotes HNSCC metastasis and chemoresistance through two internalin proteins that are secreted via the type IX secretion system (T9SS). These internalin proteins specifically bind the EC1 domain of E-cadherin through their curved solenoid-like leucine-rich repeats (LRRs), facilitating bacterial invasion and inducing epithelial-to-mesenchymal transition (EMT). Mechanistically, internalin-E-cadherin engagement drives {beta}-catenin nuclear translocation and activates p38 and JNK1/2 MAP kinase signaling pathways, enhancing tumor cell migration, metastatic dissemination, and resistance to cisplatin-induced apoptosis. Tissue microarrays detect internalin antigens in HNSCC specimens, supporting their in vivo relevance. Together, these findings establish a direct mechanistic link between an oral pathogen and HNSCC progression and extend the paradigm of internalin-E-cadherin interactions from microbial pathogenesis to cancer biology.

microbiology↗

bioRxiv2026

Bacteria-mimetic bioadhesives with multivalent mucoadhesion and drug-compatible delivery

The ability to adhere to mucus-lined tissues underpins a range of biomedical devices and therapies. However, many existing strategies rely on covalent bonding chemistries and can be unstable, cytotoxic, or incompatible with therapeutics. Here, we present a bacteria-mimetic bioadhesion strategy inspired by Vibrio cholerae. A short Bap1-derived adhesion peptide is grafted onto chitosan to strengthen mucus interactions through multivalent, cooperative secondary bonding, while preserving pH-triggered interfacial bridging behavior. Bacterial peptide grafting significantly increases adhesion energy on porcine intestine, and when paired with a tough hydrogel matrix achieves adhesion energies >400 J/m2 without forming covalent bonds to tissue. Confocal imaging reveals deep tissue penetration ([~]80 m) with markedly enhanced mucin binding and no loss of cytocompatibility. Ex vivo intestinal delivery and in vitro drug release tests demonstrate improved drug transport and tissue exposure compared to carbodiimide-mediated covalent bonding strategy. These findings establish a bacteria-mimetic bioadhesion strategy for tissue repair and drug delivery. Novelty StatementBioadhesive designs have drawn inspiration from nature such as mussel-inspired catechol chemistry and gecko-inspired dry adhesion. In contrast, bacterial adhesion mechanisms, despite enabling robust colonization of mucus-lined tissues under demanding conditions, remain largely overlooked. This work introduces a bacteria-mimetic bioadhesion strategy that selectively repurposes a short, non-pathogenic peptide derived from a Vibrio cholerae adhesin to enhance bioadhesion through multivalent, cooperative physical interactions rather than covalent bonding. This strategy significantly toughens adhesion even on chitosan, a polymer already rich in adhesive functional groups. By decoupling bacterial adhesion function from pathogenic risk, this study establishes bacterial adhesion peptides as a safe, modular, and mechanistically distinct foundation for next generation bioadhesives with improved drug compatibility.

bioengineering↗

bioRxiv2026

Dynamic dissociation of the IFT complex drives ciliary dysfunction during C. elegans ageing

Intraflagellar transport (IFT) is essential for cilia, and its dysfunction drives ciliopathies and systemic ageing. However, the in vivo dynamics of individual components in the IFT complexes remain obscured, leaving the mechanisms of age-dependent IFT failure largely unknown. Here, we report a dual-color super-resolution imaging strategy to dissect the structural integrity and kinetics of IFT trains in the sensory cilia of young and aged Caenorhabditis elegans. We show that IFT complexes are not static entities. Instead, distinct components undergo dynamic dissociation within IFT trains. This intra-complex dissociation causes a remarkable reduction in IFT velocity and is significantly exacerbated in the cilia of aged worms. Mechanistically, we identify the conserved TRiC/CCT chaperonin complex and daf-19/RFX, the master transcription factor driving IFT genes, as critical regulators of IFT stability. We demonstrate that their age-dependent downregulation drives the progressive IFT component dissociation. Our findings re-frame the IFT complex as a highly dynamic assembly, uncover a new dimension of IFT regulation, and identify the progressive uncoupling of IFT components as a key driver of ciliary dysfunction during ageing.

cell biology↗

bioRxiv2026

A cinnamyl alcohol dehydrogenase scaffold organizes monoterpenoid indole alkaloid biosynthesis

Biosynthesis of ~3,000 monoterpenoid indole alkaloids (MIAs), including the anticancer drug vinblastine, involves the highly unstable intermediate strictosidine aglycone. Its formation by strictosidine {beta}-glucosidase (SGD) and subsequent conversion by geissoschizine synthase (GS) occur in spatially separated compartments, representing a major biosynthesis bottleneck. Here we discover VinBLAST, a cinnamyl alcohol dehydrogenase-like protein repurposed as a scaffold for efficient processing of this labile intermediate. VinBLAST physically mediates SGD and GS interaction in the nucleus and allosterically enhances GS catalytic efficiency. VinBLAST homologues from diverse plant families enhance biosynthesis of several representative MIAs, with the production of catharanthine increased to ~160 mg L-1 in yeast, nearly 1,000-fold higher than previous studies. Our discovery provides a missing link in organizing MIA biosynthesis and enables scalable bioproduction of geissoschizine-derived therapeutics.

synthetic biology↗

bioRxiv2026

The Euler Characteristic Transform Enables Classification of Complex Plant Shapes and Prediction of Leaf Venation from Blade Geometry

(1) RationaleQuantifying and predicting plant morphology is central to understanding development and evolution, yet many plant forms lack homologous features required for traditional morphometrics. We apply the Euler Characteristic Transform (ECT), an injective descriptor from topological data analysis, to encode 2D plant shapes. The ECT converts contours into image-like representations that preserve shape information while enabling deep learning. (2) MethodsWe computed ECTs for large datasets of leaf and pavement cell shapes and used convolutional neural networks (CNNs) for classification. We also trained CNNs to approximate the inverse mapping, predicting leaf shape masks from radial ECTs. (3) Key resultsECT-based models achieved high classification accuracy, surpassing previous approaches on millions of herbarium-derived leaves. Notably, grapevine leaf venation was predicted from blade geometry alone, demonstrating that vascular structure is encoded in the outline. (4) Main conclusionThe ECT provides a compact, information-preserving representation of biological shape that integrates naturally with deep learning. It enables both accurate classification and predictive reconstruction, revealing latent morphological information and offering new opportunities to study plant form across scales.

plant biology↗

bioRxiv2026

Heterogeneous transmission estimation and strategy optimization for Chikungunya: a vector-borne modeling study differentiating age and sex

Against the backdrop of global climate change and accelerating population mobility in 2025, chikungunya fever (CHIKF) exhibited a trend of worldwide spread, significantly increasing the difficulty of controlling tropical mosquito-borne diseases. To enhance the precision of intervention strategies, this study developed an age- and sex-structured human-mosquito interaction dynamic model based on data from the largest CHIKF outbreak ever recorded in China, and conducted a targeted analysis of prevention and control strategies. By decomposing the basic reproduction number and examining population heterogeneity, asymptomatic males aged 15-59 years were identified as the core transmission group. Optimal control analysis revealed that the synergistic implementation of three measures-- reducing the effective human-to-mosquito transmission rate, reducing the effective mosquito-to-human transmission rate, and suppressing mosquito population density--could reduce the overall infection rate by 95.7586%. Among these, mosquito population suppression should be prioritized as a universal core strategy; however, its protective effect on females aged 60 years and above was relatively weak, warranting particular attention. The study further demonstrated that asymmetric intensity combinations targeting these three intervention pathways--such as intensity profiles of "10%, 90%, 90%" or "60%, 80%, 90%"--could achieve effective outbreak control. This research elucidates population-specific transmission patterns and key pathways for intervention intensity, providing a theoretical and strategic foundation for the precise control of mosquito-borne diseases. It also provides actionable operational insights to support rapid response and strategy optimization for future emerging outbreaks. Author summaryCHIKF is a mosquito-borne viral disease that is gradually spreading from tropical regions to other areas. To achieve more precise control of this disease, we developed an age- and sex-structured analytical model based on the largest CHIKF outbreak in China, aiming to provide a scientific basis for responding to potential future outbreaks with inherent uncertainties. The study found that asymptomatic males aged 15-59 years were the primary drivers of transmission and should be prioritized as a key population for reducing viral spread in prevention efforts. When evaluating the effectiveness of different intervention strategies, females aged 60 years and above were the least affected by the implemented measures, indicating that this group should strengthen personal protection to lower their infection risk. Among all control measures, mosquito suppression was the most effective, suggesting that vector control strategies should be prioritized in future outbreak responses.

pathology↗

bioRxiv2026

BioClaw: Human-Bot Research Collaboration Ecosystems in Group Chats

Day-to-day research discussions in group chats often generate hypotheses, analysis requests, and interpretation decisions, yet executing those analyses still requires researchers to leave the conversation and rely on fragmented local tools, databases, visualization software, and literature search engines. In this work, we present BioClaw, a human-bot research collaboration ecosystem that converts natural-language requests in group conversations into tool-grounded analyses executed within isolated Docker containers. Deployed across 8 messaging platforms, BioClaw turns each group chat into a persistent execution workspace. Its design combines multi-channel orchestration, per-group state and workspace management, and isolated containerized execution for reliable shared use over long-lived conversations. To support practical research workflows, BioClaw combines containerized execution with preinstalled 31 biomedical tools and 95+ skills. The application of BioClaw spans various biomedical domains (e.g., genomics, clinics, structural biology) and data modalities (e.g., sequencing data, EHR data, protein structure data). These results establish the viability of embedding executable, tool-rich agent workflows within shared digital workspaces, positioning group chats as a transformative paradigm for collaborative scientific discovery and innovation.

bioinformatics↗

bioRxiv2026

VeloTrace Reconciles Divergent Velocity and Trajectory in Single-cell Transcriptomics with Deep Neural ODE

Cellular identity and fate transitions are governed by continuous molecular processes that form dynamic trajectories within a high-dimensional transcriptomic landscape. Existing methods attempt to model these dynamics from two complementary perspectives: trajectory inference and velocity modeling. Ideally, velocity and trajectory are dual aspects of transcriptomic dynamics where velocity is tangent to trajectory everywhere. This inherent connection between velocity and trajectory is currently absent in transcriptomic analysis. Splicing velocity are precision-limited to inadequately-sequenced genes, while trajectory inference prioritizes the modeling of global trends while omitting local dynamics. This divergence breaks the geometric continuity between local velocities and global trajectories, hindering the reliable interpretation of developmental dynamics. To reconcile trajectory inference and RNA velocity, we introduce VeloTrace, a framework that unifies them through Neural Ordinary Differential Equations (NeuralODEs). VeloTrace learns a continuous-time velocity field whose integral curves constitute the trajectory itself, while ensuring that velocities are tangent to integral paths everywhere. Leveraging a splicing quality score, VeloTrace incorporates high-quality splicing velocity as partial supervision for velocity orientation and grounding. During optimization, VeloTrace incorporates a Monte Carlo multi-time-frame supervision strategy to ensure coherence between local and global trajectorys and suppress sequencing-induced stochastic diffusion. Through refining the velocity field and cell-specific parameters for pseudo-time, expression, and velocity, VeloTrace reconstructs a smooth, local-and-global-coherent velocity-vector-guided flow in the transcriptomic latent space. This strategy ensures a complementary integration of velocity and trajectory, imputing the transcriptional kinetics for genes of insufficient strength, whose kinetics cannot be accurately portrayed by splicing velocity. In simulation benchmarks, VeloTrace captured the transcriptional dynamics of all expressed genes, even those with inadequate sequencing coverage, producing velocity directions that were most consistent with the true direction and every-where tangential across the entire process, outperforming state-of-the-art methods, including scVelo, UniTVelo, VeloVI and scTour. VeloTrace uniquely reconciles RNA velocity and trajectory inference, creating a velocity field where each cell can infer past and future transitions from its current state. Moreover, VeloTrace extends reliable velocity estimation to a broader set of genes. When applied to mouse neural stem cell differentiation data, it successfully recovers dynamics of driver genes for two developmental lineages, including those with low expression, shedding light on their regulatory roles during differentiation. This unified framework lays the foundation for more accurate modeling of gene regulation and cell fate decisions in complex biological systems.

bioinformatics↗

bioRxiv2026

Modification-aware AI enables terminal chemical modifications for peptide design and discovers potent antimicrobials

Antimicrobial resistance (AMR) is an escalating global health threat, intensifying the urgent need for novel antibiotics. Here, we introduce Termini, a generative machine learning (ML) framework that integrates peptide generation, classification, and regression modules to systematically identify potent antimicrobial peptides (AMPs). A key feature of this pipeline is its explicit design of both N- and C-terminal modifications, combined with predictive modeling across 15 bacterial species. We experimentally validated the framework against 11 pathogenic species, representing the broadest spectrum of testing reported to date in such a study. We synthesized and tested 120 peptides (including 60 unique amino acid sequences) with and without terminal modifications. Remarkably, 111 of these peptides (92.5%) demonstrated antimicrobial activity in vitro. Comparative analyses demonstrated that terminal modifications substantially enhanced antimicrobial potency. Furthermore, the lead candidates identified through in vitro screening exhibited robust anti-infective efficacy in subsequent in vivo experiments, confirming their therapeutic potential. Collectively, these results highlight the high hit rate, biological relevance, and translational promise of this integrative, modification-aware AI framework for next-generation antimicrobial peptide development.

microbiology↗

bioRxiv2026

Cell-type-resolved transcriptional reprogramming in resistant soybean roots reveals cambial activation and early syncytium initiation upon nematode infection

Soybean cyst nematode (SCN) is the most destructive pathogen of soybean, yet the cellular basis of host resistance remains poorly understood. Here, we present a high-quality, cell-type-resolved atlas of root responses during early SCN infection in the highly resistant genotype PI437654, capturing transcriptional states across all major tissues, including rare syncytial cells. Our analyses reveal that resistance is mediated not by a localized defense but by coordinated, multicell reprogramming spanning invasion layers, vascular tissues, and feeding site-associated cells. We identify the vascular cambium as the primary cellular origin of SCN-induced syncytia, resolving a long-standing question in nematology. Mechanistically, resistance arises from disruption of key processes required for feeding site establishment, secretory stress via imbalanced vesicle trafficking, suppression of endoreduplication to prevent hypertrophic syncytial growth, and activation of autophagy to maintain cellular homeostasis. Spatially organized hormone signaling networks, including jasmonic acid, salicylic acid, and ethylene pathways, further reinforce defense, with GmJAZ1 functioning as a central regulator of JA-SA crosstalk. Collectively, PI437654 enforces resistance by targeting host cell identity, nutrient sink formation, and sustained parasitism, deploying a multilayered, tissue-specific defense strategy. This study provides a mechanistic, systems-level framework for SCN resistance and establishes a single-cell resource capturing rare root cell states, offering actionable targets for engineering durable nematode resistance. Key pointsO_LISoybean cyst nematode (SCN) is the most destructive pathogen of soybean worldwide, yet the cellular basis of early host responses and feeding site initiation remains poorly understood. C_LIO_LIUsing single-nucleus RNA sequencing (snRNA-seq), we generated a cell-type-resolved atlas of early SCN infection in roots of a unique and highly resistant soybean genotype PI437654. Trajectory analysis integrated with syncytium marker genes revealed that cambium cells are selectively targeted as the cellular origin of syncytium formation. C_LIO_LISCN infection triggers extensive cell-type-specific transcriptional reprogramming, particularly in vascular tissues (xylem, phloem, and cambium), involving pathways related to cell cycle and endoreduplication, vesicle trafficking, autophagy, and phytohormone signaling. C_LIO_LIFunctional validation demonstrated enhanced autophagy activation in infected roots via increased GFP-GmATG8a-labeled autophagic puncta, while overexpression of the jasmonic acid regulator GmJAZ1 significantly enhanced SCN resistance in susceptible soybean. C_LIO_LITogether, these findings define the cellular origin of SCN-induced syncytia and reveal coordinated cell-type-specific defense programs, providing a mechanistic framework for engineering durable resistance to SCN. C_LI

plant biology↗

bioRxiv2026

Concordant transcriptional and morphological remodeling revealed by in vivo Perturb-CLEAR

The principle that form follows function has long guided thinking in biology and architecture alike. In the nervous system, however, form does more than reflect function: neuronal morphology actively constrains input patterns, synaptic integration, and circuit wiring. During postnatal neurodevelopment, dendritic architectures are assembled and remodeled through genetically encoded programs and activities, transforming molecular programs into circuit architecture. However, dendritic morphogenesis has been difficult to quantify at scale while systematically testing how genetic variants, including neurodevelopmental disorder (NDD) risk genes, alter these structures in vivo. We developed Perturb-CLEAR, which integrates pooled CRISPR screening and whole-mount imaging to quantify brain-wide cytoarchitecture, and paired it with Perturb-seq to link structural phenotypes to transcriptomic changes. Applying Perturb-CLEAR to the developing mouse cortex revealed morphogenesis trajectories accompanied by transcriptomic dynamics. Moreover, systematic perturbation of NDD risk genes uncovered gene-specific multimodal phenotypes. Adnp perturbation remodels basal dendrites in L4/5 IT (intratelencephalic) neurons but not other dendritic compartments or cell types, alongside consistent transcriptional shifts. Combined morphology and transcriptome analyses link NDD risk genes to concordant multimodal cellular phenotypes in the developing brain, highlighting diverse paths of perturbation effect propagation across modalities.

neuroscience↗

bioRxiv2026

Engineered Lactate Catabolizing Probiotics Reveal Timescale Dependent Microbiome-Host Metabolic Coupling

The exchange of lactate, a metabolic substrate and regulator, between the gut lumen and systemic circulation for use in host and microbial processes is well documented, but tools capable of uncovering whether this process influences host metabolic status across acute and chronic contexts are lacking. In our prior work, we engineered probiotic Bacillus subtilis PY79 to produce lactate oxidase (LOX) intracellularly, allowing it to rapidly convert intestinal lactate to pyruvate. Following oral administration, LOX reduced systemic lactate concentrations at rest and under challenge conditions, providing a platform for investigating lactates influence on host metabolism and microbiota. In the present work, we demonstrate that acute LOX administration effectively rewired microbiota function and host energy balance, as revealed by 16S sequencing and indirect calorimetry. In silico microbial community modeling via MICOM and metagenomic inference via PICRUSt2 suggested that acute shunting of lactate to pyruvate induced microbiota remodeling towards anabolic processes, reflected by increased flux of pyruvate, acetate, and formate, alongside moderate to large increases (Cohens d = 0.60-1.00) in pathways for fructan degradation, B-vitamin biosynthesis, and lipid synthesis. These anabolic shifts temporally aligned with transient increases in host energy expenditure ({beta} = 1.08, p<0.05) via glucose oxidation ({beta} = 0.01, p<0.05), hinting at functional coupling between microbial biosynthesis and host energy balance via lactate exchange. Of note, acute LOX administration also improved thermoregulation and survival following LPS-induced sepsis, demonstrating functional relevance of these metabolic effects during acute inflammatory challenge. To assess chronic effects, we administered LOX for 6 weeks during diet-induced obesity. LOX treatment persistently reduced blood lactate. However, this chronic lactate reduction did not curtail the progression of diet-induced obesity or induce sustained modulation of host energy expenditure. This disconnect between acute and chronic findings suggests that gut-centric lactate conversion affects energy balance through microbiome and/or host-dependent mechanisms, but cannot override homeostatic forces in the long term to produce clinical benefit during chronic disease. Our results validate LOX probiotics as a tool for acute metabolic augmentation, and highlight a clear homeostatic limit to gut-centric therapies. This platform may enable targeted design of probiotic interventions matched to therapeutic timescale and inform synbiotic formulations that overcome homeostatic compensation.

microbiology↗

bioRxiv2026

Integration of iPSC-derived microglia into human midbrain organoids enhances microglial maturation and inflammatory signaling

Microglia are the resident immune cells of the central nervous system and play key roles in the healthy brain during development and adulthood, as well as during neurodegenerative diseases - including Parkinsons disease (PD). Yet the role of microglia in PD pathogenesis has not been fully elucidated. Limitations of 2D cell culture and animal models in simulating human microglia in the brain parenchyma have contributed to this knowledge gap. Human midbrain organoids (hMOs) provide a promising model that can recapitulate elements of PD pathology but lack microglial cells. Here we adapt protocols for the differentiation of hMOs and human iPSC-derived microglia (iMG) to generate iMG-hMO assembloids. Within assembloids, integrated iMG (intMG) express canonical microglia markers and induce the release of cytokines and chemokines. Transcriptomic profiling by single cell RNA sequencing reveals that intMG adopt a more mature and inflammation-responsive state compared to 2D iMG. The integration of microglia results in increased signaling through inflammatory and trophic pathways that drive altered transcriptional signatures of dopaminergic neurons and astrocytes within assembloids. Overall, iMG-hMO assembloids have the potential to more faithfully model the role of microglia and neuroinflammation in PD pathogenesis.

neuroscience↗

bioRxiv2026

Integrative Multi-cohort Transcriptomics and Network Pharmacology Analysis Reveals Key Network Nodes and Potential Drug Clues in PCOS Granulosa Cells

BackgroundPolycystic ovary syndrome (PCOS) is a prevalent endocrine disorder with complex pathophysiology and limited therapeutic options. Identifying key molecular drivers and potential drug candidates is critical for improving clinical outcomes. MethodsWe integrated multi-cohort transcriptomics (GSE155489, GSE138518, GSE226146) with weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, and drug repurposing. Differential expression analysis identified 1,039 DEGs, and WGCNA identified 10 PCOS-associated modules. Intersection of DEGs with module genes yielded 498 core candidate genes, which were subjected to functional enrichment, PPI network analysis, and connectivity map-based drug repurposing (CLUE/LINCS). Candidate drugs were further evaluated by molecular docking and ADMET prediction using a triple intersection strategy (hub genes, high differential expression, drug-target evidence). ResultsFunctional enrichment revealed significant enrichment in cell adhesion and TGF-beta signaling. PPI network analysis identified CD44 as the top hub gene (degree=42). Drug repurposing identified 106 candidate drugs, including troglitazone and enzalutamide. Using the triple intersection strategy, five genes (ID2, NR4A1, GJA5, ID1, MYH11) were prioritized for molecular docking. GJA5 showed strong predicted binding affinity with flufenamic acid (-7.88 kcal/mol), and cytosporone B exhibited favorable druglikeness (0 Lipinski violations). ConclusionThis study systematically characterizes PCOS-associated gene networks and provides a prioritized set of candidate targets and drugs through a purely computational framework. CD44 emerges as a key network node with potential relevance in PCOS pathophysiology. These findings offer testable hypotheses for future mechanistic studies and drug discovery efforts in PCOS.

systems biology↗

bioRxiv2026

A Haplotype-resolved Telomere-to-Telomere Pig Genome

Haplotype-resolved telomere-to-telomere (T2T) pig genomes have not been reported, but may be essential for xenotransplantation and interspecies chimerism. Here we describe fully phased paternal and maternal T2T assemblies of an Erhualian pig (Ssc_EHL), enabling chromosome-scale reconstruction at base resolution. We uncover 2,498 (paternal haplotype) and 2,577 (maternal haplotype) protein-coding loci previously unresolved in Sscrofa11.1, while maintaining high concordance for anchored gene models between haplotypes. Applying this haplotype-resolved T2T annotation to publicly available pig-to-human kidney xenograft transcriptomes further allows the resolution of xenograft cell states and reveals newly resolved, cell-type-enriched genes. Cross-species synteny and orthology analyses against human identified a subset of high-confidence pig-specific genes with limited human counterparts, thus, refining the genomic basis of interspecies divergence. By integrating these orthology features with organ transcriptomes, we further defined an Interspecies Chimerism Compatibility score to prioritize organ-enriched, highly conserved loci with minimal redundancy imbalance, providing haplotype consistent targets for engineering organ-deficient pigs. Together, this haplotype-resolved T2T pig genome provides a foundational resource for mechanistic studies and rational genome engineering in xenotransplantation and interspecies chimerism.

genomics↗

bioRxiv2026

The structure-interaction model of polymyxin lipopeptides with human oligopeptide transporter 2

Multidrug-resistant (MDR) Gram-negative bacteria pose a critical global health threat, while polymyxins remain a last-line therapy. However, their clinical use is limited by nephrotoxicity. Human oligopeptide transporter 2 (hPepT2) is a membrane transporter mediating the reabsorption of polymyxins in renal cells and contributes to their nephrotoxicity, but the molecular basis of their interaction remains unclear. Here, we investigated the structure-interaction relationship (SIR) of polymyxins with hPepT2 by integrating computational, chemical, and cell biology approaches. Bioinformatic modelling predicted an outward-facing hPepT2 structure and a potential transport pathway, with polymyxins interacting at the lateral opening, particularly E214, D215, D317, D342, and E622. Transporter mutagenesis and molecular analyses confirmed that D215 is critical for polymyxin binding, while other residues influence transporter turnover and/or expression. We subsequently synthesised polymyxin analogues with modifications at Dab1, Dab3, Dab5, and Dab9 of polymyxins, which reduced interactions with hPepT2. Notably, alanine substitution at Dab3 reduced nephrotoxicity in mice while retaining antibacterial activity. Overall, this proof-of-concept study demonstrates that the hPepT2-polymyxin SIR model provides a viable strategy for developing novel, safer lipopeptide antibiotics.

biochemistry↗

bioRxiv2026

Effect of population structure and stabilizing selection on quantitative genetic variation

We study one of the simplest scenarios of polygenic selection that can be imagined: a subdivided population of diploid individuals expressing an additive trait under spatially homogeneous stabilizing selection. We are interested in the amounts of variation that can be maintained at mutation-selection-migration-drift equilibrium, at individual loci and at the level of the trait, within and among subpopulations. We derive analytical approximations for variance components and summary statistics such as FST and QST under the assumptions of the infinite-island model and compare these with individual-based simulations. We find that: (i) There is a critical migration threshold (which depends on effect sizes of trait loci) below which population structure strongly inflates genic variance in the subdivided population to levels well above those in a panmictic population. Variation within each subpopulation is maximized close to the critical migration rate. (ii) The genetic basis of trait variation across subpopulations is most similar close to this migration threshold and (counter-intuitively) becomes less similar for higher migration rates. This has consequences for the portability of Genome-Wide Association Studies (GWAS) between subpopulations, i.e, the extent to which loci with large contributions to variance in one subpopulation explain variance in other subpopulations. (iii) An analytical mean-field approach based on the single-locus diffusion approximation, together with effective migration and selection parameters (to account for associations between loci), very accurately predicts various quantities.

evolutionary biology↗