Biology subjects

Zhao, H.

Publications and source records attributed to Zhao, H..

At least 73 records · Page 4Linked to original sources

bioRxiv2026

immunoPETE: A DNA-based integrated B-cell and T-cell receptor profiling platform

The vast diversity of B and T cell receptors generated through the recombination of Variable (V), Diversity (D), and Joining (J) gene segments plays a critical role in adaptive immunity. Profiling immune repertoires at the DNA level provides a robust and stable approach to capture the clonal composition of these receptors. immunoPETE is an assay designed to target recombined human T-cell Receptor Beta (TRB), T-cell Receptor Delta (TRD), and Immunoglobulin Heavy (IGH) chain genes directly from genomic DNA. Simultaneous profiling of B and T cell receptor chains in a single reaction provides internally normalized clone counts and facilitates the study of B-T cell interactions. Full-length amplicon consensus sequences representative of original template DNA molecules are accurately reconstructed using Unique Molecular Identifiers (UMIs). An in-house pipeline compiles VDJ rearrangements from the Complementarity-Determining Region 3 (CDR3) of TRB, TRD and IGH chains into comprehensive readouts at cell-level resolution. In this study, we describe the immunoPETE end-to-end workflow, followed by a comprehensive benchmarking of its performance in adaptive immune profiling. Where applicable, we used both natural and contrived samples and characterized the assays accuracy, linearity, and reproducibility across several metrics: retrieving CDR3 sequences, determining B and T cell ratios, total cell count, yield, fraction of functional rearrangements, clonal diversity, composition of dominant clones, pairwise similarity, and V/J gene usage frequencies. Furthermore, we assessed its quantitative limits concerning the total number of lymphocytes and the detection of rare clones. As an example of its applications, we show that adding immune biomarkers extracted from immunoPETE data to clinical factors improves prediction of progression-free survival in a cohort of non-muscle invasive bladder cancer (NMIBC) patients. Finally, we discuss the broad applications of immunoPETE in the study of aging, cancers, infections, and autoimmune disorders with reference to select published studies.

immunology↗

bioRxiv2026

Weather Characterization for Optimizing Genomic Prediction in Miscanthus sacchariflorus

Environmental factors affect crop growth and development thus their consideration across sites and years become essential for genotypic evaluation. Genomic selection (GS) has been broadly implemented to accelerate breeding cycles by skipping field evaluations thus allowing early identification of outperforming genotypes. In this study, 7,740 phenotypic records corresponding to 516 Miscanthus sacchariflorus genotypes evaluated in five locations across three years were considered for analysis. Additionally, environmental data on six weather covariates was implemented to characterize similarities between locations. Different sets of locations of variable sizes were used for model calibration based on two cross-validations (CV00 and CV0) schemes leaving out one location at a time. Predictive ability across locations of the best model varied between 0.45 and 0.90 for both schemes. These results were compared to associate predictive ability in function of weather patterns between training and testing sets to allow models calibration optimization. We found it is feasible to optimize resource allocation by considering environmentally correlated sets. In most cases, the information from only one and, at most, two locations were enough to deliver better results than using all four locations, reducing training sets by up to 75%. The results obtained shed light on helping breeders make informed decisions considering weather data when designing evaluations.

genomics↗

bioRxiv2026

ENHANCING GENOMIC PREDICTION MODELS IN MISCANTHUS POPULATIONS BY INCORPORATING THE GENOTYPE-BY-ENVIRONMENT INTERACTION

Giant Miscanthus giganteus (Mxg) is one of the most promising perennial crops to generate biomass feedstock for bioenergy and biobased products. It is derived from the natural inter-species hybridization of Miscanthus sacchariflorus (Msa) and Miscanthus sinensis (Msi) species, thus population improvement within these species is crucial. Genomic selection (GS) is an attractive option to accelerate breeding of perennial grasses, such as Miscanthus, which requires up to three years of evaluation to produce reliable phenotypic data. Hence, genotypes are observed in multiple years and locations causing inconsistent response patterns from one year to the next, between location, and/or location-by-year combinations. These inconsistencies are known as the genotype-by-environment interaction effect (GxE). Although GS has been successfully implemented in multiple annual crops where straightforward cross-validation schemes exist to assess the levels of predictive ability that can be reached, for perennial crops new cross-validation schemes will help avoid data contamination. Here, we propose a series of cross-validation schemes to evaluate model performance for perennial crops. We perform a case study by analyzing one panel of each species (516 genotypes of Msa, 280 genotypes of Msi) scored for biomass yield at different locations around the world over several years. The results of the different cross-validation schemes provide insights about the usefulness of GS to accelerate the breeding process of Miscanthus species. In addition, leveraging the GxE effects of different types significantly increases predictive ability (up to 10% in Msa and 30% for Msi) compared to the conventional approaches based on main effects only.

genomics↗

bioRxiv2026

VarDCL: A Multimodal PLM-Enhanced Framework for Missense Variant Effect Prediction via Self-distilled Contrastive Learning

Missense variants are a common type of genetic mutation that can alter the structure and function of proteins, thereby affecting the normal physiological processes of organisms. Accurately distinguishing damaging missense variants from benign ones is of great significance for clinical genetic diagnosis, treatment strategy development, and protein engineering. Here, we propose the VarDCL method, which ingeniously integrates multimodal protein language model embeddings and self-distilled contrastive learning to identify subtle sequence and structural differences before and after protein mutations, thereby accurately predicting pathogenic missense variants. First, leveraging sequence and structural information before and after mutations, VarDCL generates sequence-structural multimodal features via different language models. It incorporates both global and local perspectives of feature embeddings to provide the model with dynamic, multimodal, and multi-view input data. Additionally, a Self-distilled Contrastive Learning (SDCL) module was proposed to enable more effective information integration and feature learning, enhancing the models ability to detect sequence and structural changes induced by mutations. Within this module, the multi-level contrastive learning framework excels at capturing information differences before and after mutations within the same modality; meanwhile, the feature self-distillation mechanism effectively utilizes high-level fused features to guide the learning of low-level differential features, facilitating information interaction across different modalities. The VarDCL framework not only ensures the models capacity to learn dynamic changes pre- and post-mutation but also significantly improves cross-modal information interaction between sequence and structure, thereby remarkably boosting the models performance in distinguishing pathogenic mutations from benign ones. To validate the effectiveness of VarDCL, extensive experiments were conducted. The ablation study demonstrates that all key components of VarDCL contribute significantly. On an independent test set containing 18,731 clinical variants, VarDCL achieved an AUC of 0.917, an AUPR of 0.876, an MCC of 0.690, and an F1-score of 0.789, outperforming 21 state-of-the-art existing methods. Benchmark analysis shows that VarDCL can be utilized as an accurate and potent tool for predicting missense variant effects.

bioinformatics↗

bioRxiv2026

pertTF: context-aware AI modeling for genome-scale and cross-system perturbation prediction

Predicting genetic perturbation responses at a single-cell level is central to building models for cell state and disease. However, existing approaches are limited on predicting phenotypic outcomes beyond expression changes and generalizing predictions across genome-scale perturbations in biologically relevant contexts. Here we introduce pertTF, a transformer-based single-cell genetic perturbation model. pertTF was trained from a unique dataset capturing single cell expressions profiles of 30 full gene knockouts across 14 relevant cell types during human pancreatic development and beta-cell differentiation. pertTF outperforms current methods in predicting expression changes of perturbing unseen genes in unseen cellular contexts. In addition, pertTF infers perturbation-induced shifts in cell identity and population composition, an important phenotypic outcome of perturbation in many physiology and disease settings. Through transfer learning, pertTF operates in physiologically relevant systems, including primary human islets, where large-scale perturbation experiments are challenging. The generalizability of pertTF is further demonstrated by in silico pooled and single-cell CRISPR screens, capturing critical regulators of stem cells and early pancreatic cell development. These results establish pertTF as a framework for integrating large-scale single-cell perturbation data with AI models to predict genetic perturbation effects across cellular systems and disease contexts.

bioinformatics↗

bioRxiv2026

Automated high-throughput fabrication of patient-specific vessel-on-chips enables a generative AI digital twin--Cascade Learner of Thrombosis (CLoT) for personalized thrombosis prediction

We developed an integrated platform combining high-throughput automated biofabrication, systematic patient-derived tissue experiments, and specialized artificial intelligence to enable patient-specific computational "digital twins" for thrombosis prediction. Our automated manufacturing platform fabricates 80 fully assembled, patient-specific vessel-on-chips within 10 hours from clinical imaging--a [~]100-fold improvement over manual methods--achieving sub-micron precision through novel two-stage pneumatic motion control and integrated optical feedback. Using these chips, we systematically captured thrombosis across 491 high-fidelity videos spanning 6 patient-derived vascular geometries, 5 distinct anatomical injury sites, and 14 anticoagulant/antiplatelet interventions, establishing a "physical twin" experimental corpus. We trained CLoT (Cascade Learner of Thrombosis), a conditional video diffusion model efficiently adapted via lightweight Low-Rank Adaptation (LoRA) to generate realistic thrombosis videos conditioned on patient-specific geometry, injury location, and drug treatment. Rigorous benchmarking against state-of-the-art commercial models (Sora, Wan, Kling, Seedance, Hailuo, Hunyuan) reveals CLoT achieves 7.38-fold superior temporal biological consistency and 5.3-fold higher spatial morphological fidelity. Prospective validation on unseen patients demonstrates >90% temporal accuracy. This integrated paradigm--combining automated fabrication with domain-specialized generative AI--establishes proof-of-concept for personalized medicine enabled by digital twins trained on human-derived vascular anatomy, enabling pre-treatment antithrombotic evaluation while providing a replicable template for translating tissue engineering into clinical practice.

bioengineering↗

bioRxiv2026

Identifying genetic regulations on immune cell type proportions and their impacts on autoimmune diseases

Genetic regulation of immune cell composition plays a crucial role in the etiology of complex diseases, yet remains poorly understood. We propose a unified analytical framework that integrates genome-wide association studies (GWAS) of cell type proportions with cell-type-wide association studies (cWAS) to systematically characterize both the genetic regulation of immune cell composition and its downstream effects on disease risk. Using single-cell RNA sequencing data from the OneK1K cohort, we conducted a GWAS of immune cell-type proportions with a depth-weighted quasi-binomial model designed for bounded, overdispersed traits. We identified 47 genome-wide significant loci influencing eight fine-labeled immune cell subtypes. Leveraging these identified genetic effects, we further imputed genetically regulated proportions (GRPs) using polygenic risk score (PRS)-based imputation and assessed their associations with complex diseases through cWAS. We identified five significant cell type-disease associations, including two with type 1 diabetes, two with Crohns disease, and one with ulcerative colitis. Together, our results demonstrate that cell type proportions observed in scRNA-seq can reveal regulatory loci and offer insights into how genetic variations regulate immune cell type proportions to affect disease risk. Although we focused on immune single-cell data, our framework is applicable to other tissues or cellular compositions as scRNA-seq datasets expand. Author SummaryGenome-wide association studies (GWASs) have uncovered many disease-associated signals, yet most lie in noncoding regions and are difficult to interpret. Mapping GWAS signals to the relevant cell types is therefore important for better understanding the biological mechanisms that drive disease. A major challenge is that observed gene expression and measured cell-type proportions can be influenced by environmental factors and disease status. In contrast, genotypes are less affected by these factors, making them more reliable for interpreting factors of diseases. Moreover, the cell-type proportions are bounded and often skewed, so standard GWAS models that rely on Gaussian assumptions may lose power. To address this, we developed a quasi-binomial approach that better matches the data and improves discovery while controlling false positives. In real data, our method identified more genetic loci associated with cell-type proportions than a traditional linear model. To further investigate how genetic variation regulates immune cell composition to influence disease risk, we integrated our results with disease GWAS summary statistics to identify immune cell types that may contribute to disease susceptibility. Together, our results link disease-associated GWAS signals to specific immune cell types and provide insights into the cellular mechanisms that may underlie these diseases.

genetics↗

bioRxiv2026

Disulphide and sequence-encoded conformational priors guide nanobody structure prediction

Nanobody binding is largely governed by the HCDR3 loop, which adopts distinct placement regimes relative to the framework: compact, framework-contacting (kinked blueprint) and solvent-exposed (extended blueprint). Many nanobodies also contain additional cysteines that form non-canonical disulphide bonds, imposing covalent constraints on binding-loop conformations. Current structure predictors are typically trained and benchmarked with smooth coordinate-based objectives, so models may appear reasonable under root-mean-square deviation (RMSD), while adopting an incorrect HCDR3 blueprint or failing to recover the native disulphide connectivity, impacting paratope geometry and functional interpretation. Here, we show that the HCDR3 blueprint is predictable from sequence alone, allowing for explicit constraints during modelling. We implement these principles into NbForge, a lightweight nanobody folding model that incorporates blueprint- and disulphide-aware inductive biases and is trained with filtered self-distillation. NbForge improves recovery of HCDR3 blueprint and non-canonical disulphide formation over previous lightweight models and achieves coordinate accuracy at par to state-of-the-art, large, resource-intensive predictors, while running at sub-second inference speed. We show that using NbForge monomer models as templates further improves the success rate of predicting nanobody-antigen complexes. Together, these results motivate blueprint- and disulphide-aware benchmarks for nanobody modelling beyond RMSD, and show that appropriate inductive biases can close the performance gap to heavyweight predictors. We make the sequence classifier (NbFrame) and NbForge available for download and via a user-friendly web server.

bioinformatics↗

bioRxiv2026

Unveiling the taxonomic diversity and unprecedented biosynthetic treasure of the phylum Myxococcota

Natural products remain vital sources of therapeutics, particularly anti-infectives, and members of the phylum Myxococcota constitute an especially rich reservoir for their discovery. Based on decades of microbiological efforts, we present 154 new Myxococcota genomes and propose a revised taxonomy expanding the number of described families from 11 to 28 and genera from 32 to 90. Comparison with an equivalent set from the prime source Actinomycetota shows that Myxococcota possess a comparable biosynthetic diversity, underscoring their promise for large-scale isolation and sequencing efforts. The vast untapped potential reflected in 2,387 uncharacterized gene cluster families is highlighted by genome mining efforts, yielding four validated compounds exhibiting novel chemistry, including myxolutamids and myxopentacins. We show that many Myxococcota-derived natural products, such as myxolutamid A and two new sorangicin derivatives, are conserved within taxonomic lineages. New described families thus bear high biosynthetic potential underpinning the importance of precise taxonomic classification guiding targeted drug discovery.

microbiology↗

bioRxiv2026

Characterization of the Gut Microbiota and Serum Metabolomics in Patients with Type 2 Diabetes Mellitus and Newly Diagnosed Acute Coronary Syndrome

Biomarkers for the early identification of acute coronary syndrome (ACS) risk remain inadequately investigated, particularly in patients with type 2 diabetes mellitus (T2DM), for whom timely clinical intervention may substantially enhance prognostic outcomes. The gut microbiota and serum metabolites may serve as pivotal mediators in the occurrence and progression of ACS among patients with T2DM, and whether these factors can be used for the precise discrimination of patients with T2DM complicated by ACS remains to be explored. Overall, 76 participants were enrolled (38 patients diagnosed with T2DM complicated by ACS and another 38 with T2DM without ACS). 16S rRNA sequencing combined with untargeted LC-MS metabolomics revealed a dysregulated gut-serum axis in patients with T2DM complicated by ACS: enrichment of proinflammatory microorganisms (Enterococcus spp.), reduction of butyrate producers (Butyricimonas spp.) and concomitant dysregulation of circulating lipid metabolites-upregulation of PC(16:0/9:0 (CHO)) and arachidonic acid alongside downregulation of cholesterol sulfate. By integrating multiomics data and applying various feature selection methods, we subsequently identified six key biomarkers. The final constructed combined model robustly distinguished patients with T2DM complicated by ACS from those with T2DM alone (AUC = 0.983), outperforming the other single omics models. Our study revealed that the gut microbiota and related serum metabolites serve as key mediators in the onset and progression of ACS among patients with T2DM, and demonstrated their potential value as noninvasive biomarkers for the early diagnosis of T2DM complicated by ACS. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSWe combined 16S rRNA sequencing with untargeted LC-MS metabolomics to dissect the gut-serum axis in patients with T2DM complicated by newly diagnosed ACS. We identified distinct gut microbial and serum metabolic signatures that distinguish ACS progression within the T2DM population. A multiomics classifier integrating clinical, microbial, and metabolic variables achieved robust diagnostic performance (AUC = 0.983) and outperformed single omics models. What Are the Clinical Implications?Integrated multiomics biomarkers facilitate the early identification of ACS progression in patients with T2DM, offering novel avenues for precision prevention, dynamic clinical surveillance, and individualized therapeutic strategies. Dysregulations of the gut microbiota and serum metabolome may play an important role in the development and progression of ACS in patients with T2DM.

microbiology↗

bioRxiv2026

Structural basis for the broad recognition specificity of an Arabidopsis immune receptor

Plant nucleotide-binding leucine-rich repeat (NLR) immune receptors typically confer resistance through recognition of specific pathogen effectors. The Arabidopsis NLR WRR4A defies this paradigm by recognizing multiple sequence-divergent effectors from Albugo candida, conferring resistance to multiple pathogen races. Despite minimal sequence similarity, these effectors share a conserved N-terminal ferredoxin-like fold. Through cryo-EM structure determination of two WRR4A resistosomes bound to sequence-distinct effectors, combined with AlphaFold modelling, we reveal a shape-based recognition mechanism: WRR4A engages structurally conserved backbone features of the effectors in a mostly side chain-independent manner, enabling recognition of diverse effectors with similar three-dimensional architectures. These insights guided successful engineering of WRR4A to acquire novel recognition specificity. In addition, analysis of the monomeric WRR4A resting state reveals a distinct domain architecture characteristic of C-JID-containing TIR-NLRs and informs their activation mechanism. This work provides insights into NLR-mediated broad-spectrum recognition and the potential for structure-informed engineering of improved crop resistance.

plant biology↗

bioRxiv2026

Functional characterization and molecular engineering of a O-methyltransferase involved in bis-benzylisoquinoline alkaloids biosynthesis from Nelumbo nucifera

Nelumbo nucifera (lotus), a traditional aquatic plant in Asia, is valued for its nutritional and therapeutic properties. Benzylisoquinoline alkaloids (BIAs) are its major bioactive components, with significant bioactivities and pharmacological values. O-methyltransferases (OMTs) play a crucial role in shaping the structural and functional diversity of BIAs. Herein, we characterized a specific OMT, designated Nn7OMT, which exhibits a novel catalytic function: methylating the C7 position of the bisbenzylisoquinoline (bisBIA) skeleton. Nn7OMT also displayed substrate promiscuity, catalyzing O-methylation at the C7 position of bisBIAs, C6/C7 positions of 1-benzylisoquinolines, C7 position of aporphines, and C9 position of protoberberines, with isoliensinine as its preferred substrate. The expression profile of Nn7OMT correlates with bisBIA accumulation in planta, supporting its involvement in bisBIA biosynthesis in N. nucifera. Protein engineering guided by molecular docking and molecular dynamics simulations identified key residues critical for Nn7OMT activity. Mutants M158A and V306A retained catalytic activity toward isoliensinine while showing nearly undetectable activity against 1-benzylisoquinoline alkaloids, thereby significantly improving Nn7OMTs substrate specificity. These findings advance our understanding of BIAs biosynthesis in lotus and provide valuable biocatalysts with enhanced specificity. SIGNIFICANCEIn this study, we report the innovative discovery and characterization of Nn7OMT, the first O-methyltransferase isolated from Nelumbo nucifera that harbors a novel catalytic activity toward the bisbenzylisoquinoline alkaloid (bisBIA) backbone. Nn7OMT displayed substrate promiscuity, catalyzing methylation at the C7 position of the bisbenzylisoquinoline skeleton (isoliensinine), C6/C7 positions of the 1-benzylisoquinoline skeleton (norcoclaurine, coclaurine, N-methylcoclaurine), C7 position of the aporphine skeleton (lirinidine), and C9 position of the protoberberine skeleton (scoulerine)--with isoliensinine as its most preferred substrate. Innovatively, the catalysis of isoliensinine by Nn7OMT was a novel function and the first O-methyltransferase identified to catalyze the bisbenzylisoquinoline backbone, which may be involved in the biosynthesis of neferine. Nn7OMTs expression profile correlated with bisBIA accumulation in planta, further supporting its role in bisBIA biosynthesis in N. nucifera. We further engineered Nn7OMT using molecular docking and MD simulation-guided strategies. Mutants M158A and V306A retained catalytic activity for isoliensinine while showing nearly undetectable activity against 1-benzylisoquinoline alkaloid substrates. This targeted engineering significantly improved the specificity of Nn7OMT, rendering it highly suitable for methylated isoliensinine biosynthesis by reducing by-product accumulation and simplifying purification processes. Our work deepens understanding of the unique BIA metabolic pathway in N. nucifera through the identification of the first bisBIA-catalyzing O-methyltransferase, and provides valuable biocatalysts with enhanced substrate specificity.

molecular biology↗

bioRxiv2026

Polyploid cardiomyocytes define disease-specific transcriptional states in the mammalian heart

The adult mammalian heart has a limited regenerative capacity. Following injury, cardiomyocytes undergo a hypertrophic response accompanied by polyploidization, which has been described as a barrier to proliferation and regeneration of the heart1,2. However, the unique molecular programs of polyploidy, or genome multiplied cardiomyocytes, and their influence on the disease-related myocardial remodelling process remains unclear. Here, we integrate single-nuclei and high-resolution spatial multi-omics across human, rat, and mouse hearts to define novel cardiac cell states and their tissue niches in ischemic and non-ischemic heart disease. Computational analysis across scales allowed us to generate detailed networks of the cardiac tissue remodelling process as well as tissue and sub-cellular environments uniquely enriched in polyploid cardiomyocytes or their diploid origins. We identify a conserved, dichotomous transcriptional program distinguishing diploid from polyploid cardiomyocytes. Polyploid cardiomyocytes demonstrated rewired metabolic and chromatin-remodeling transcriptional programs and recapitulate the gene signature of immature human fetal cardiomyocytes. Notably, we observe that polyploid cardiomyocytes--rather than the general myocyte population--are the primary sites of enrichment for major heart-failure drug targets, including the mineralocorticoid, {beta}1-adrenergic, and glucagon-like peptide-1 receptors. Based on our cross-species dataset we further identified TNIK, a Wnt-pathway regulator expressed in polyploid cardiomyocytes across species, as a potential therapeutic target and demonstrate that pharmacological TNIK inhibition improves cardiac function after myocardial infarction in rats. Together, this species-spanning, disease-resolved study redefines cardiomyocyte heterogeneity in heart disease and suggests a therapeutic path to heart failure treatment by targeting polyploid cardiomyocytes.

genomics↗

bioRxiv2026

Geometric Fragility in Alzheimer's Disease: Probing the Loss of Hippocampal Hierarchical Abstraction via Contrastive Point Cloud Modeling

While human spatial cognition relies on hierarchical abstraction to integrate local features, Alzheimers disease (AD) pathology is often reduced to local volumetric atrophy. We propose geometric fragility as a distinct failure of global topological integrity within the hippocampus rather than a mere accumulation of local errors. Using a texture-invariant point cloud framework, we contrasted local feature aggregation with global hierarchical attention as computational probes for structural perception. The global paradigm demonstrated enhanced diagnostic sensitivity and exhibited cognitive resilience under simulated neuronal sparsity by maintaining structural recognition where local models suffered pattern collapse. Global modeling effectively unfolded the pathological manifold, revealing a linear trajectory of geometric degradation consistently correlated with clinical cognitive decline. These findings indicate that AD progression may be characterized as a systemic topological dissolution and suggest that effective biomarkers should prioritize hierarchical shape abstraction to accurately map the continuous transition from healthy cognition to dementia.

neuroscience↗

bioRxiv2026

Convergent post-drought recovery of biomass and functional traits under constant and periodic warming in slow- and fast-growing plants

Extreme climate events such as droughts and heatwaves are intensifying under climate change, yet their combined effects on plant recovery remain unclear. In a two-year outdoor mesocosm experiment, we tested how grassland species with contrasting growth strategies recover from summer drought under four warming regimes: ambient, moderate warming (+2 {degrees}C), periodic heatwaves (+7 {degrees}C), and their combination. Experimental communities of native fast- and slow-growing species plus the invasive Solidago canadensis were assessed for above-ground biomass and leaf traits (SLA, LDMC, chlorophyll content, stomatal conductance) at one- and four-months post-drought. Biomass fully recovered within one month in both growth strategies, but leaf traits showed transient shifts, over-recovery in SLA and under-recovery in LDMC, likely reflecting production of new leaf tissues. These deviations generally returned to control levels by four months, regardless of warming treatments. Solidago canadensis exhibited high tolerance to heat and drought, with early biomass and trait recovery, indicating potential for dominance under climate extremes. Biomass recovery was similar across growth strategies, suggesting that growth-related differences play a minimal role in short-term recovery; however, early regrowth was characterised by contrasting trait shifts. Such lagged trait recovery, combined with rapid invasive recovery, suggests potential for longer-term shifts in grassland composition and function. We recommend that incorporating trait-based recovery dynamics is essential for predicting ecosystem stability under compound climate extremes.

ecology↗

bioRxiv2026

Systemic and Local Delivery of siRNA to the CNS and Periphery via Anti-IGF1R Antibody Conjugation

siRNA delivery platforms capable of accessing both central and peripheral tissues are critically needed to expand the therapeutic potential of oligonucleotides. To address this, we developed a novel siRNA-antibody conjugate by attaching an Hprt-targeting siRNA to an engineered antibody shuttle. This shuttle targets the insulin-like growth factor 1 receptor (IGF1R) using a fused antibody fragment (Clone F) and utilizes an antibody backbone with no tissue-relevant binding in this study. The resulting conjugate, designated Clone F-Hprt, demonstrated robust in vivo knockdown across multiple tissues. Clone F-Hprt demonstrated enhanced penetration into central nervous system (CNS) tissues compared to unconjugated siRNA following intracerebroventricular (ICV) and intravenous (IV) administration. In peripheral tissues, Clone F-Hprt achieved widespread knockdown in muscle, heart, and lung, consistent with IGF1R expression. The conjugate was well tolerated across all routes, including with repeated dosing. Although several receptor-mediated approaches for CNS delivery are progressing to the clinic (e.g., targeting the transferrin receptor), clinical validation remains to be demonstrated. Our findings highlight IGF1R as an alternative receptor capable of supporting delivery across both central and peripheral tissues, offering a complementary strategy for expanding the therapeutic landscape of oligonucleotide delivery.

neuroscience↗

bioRxiv2026

Hyperhomocysteinemia does not increase the risk of intracerebral hemorrhage in hypertensive mice

ObjectiveHyperhomocysteinemia (HHcy) affects approximately 75% of the population in China, and there is currently controversy regarding whether HHcy increases the risk of hemorrhagic stroke. This study aims to investigate the effects of high homocysteine (Hcy) levels on cerebral hemorrhage in hypertensive mice by administering homocysteine to them. MethodsMale C57BL/6 mice at 8 months of age were used in the experiment. The study was divided into two groups: the Hcy + AngII + L - NAME group and the AngII + L - NAME group. Magnetic resonance imaging (MRI) was performed when the mice exhibited signs of cerebral hemorrhage.After the hemorrhage, anesthesia was induced to euthanize the animals, and then the brain tissue was fixed. The total rearing period was 18 weeks. The relationship between homocysteine and stroke was described by plotting survival curves. The location and quantity of cerebral hemorrhage were determined through histopathological staining. ResultsThe serum Hcy concentration of mice fed with Hcy for 6 weeks increased to 23.07 mol/L, and the blood pressure ranged from 170 to 180 mmHg. The number of deaths due to cerebral hemorrhage was 10 in both the AngII + L - NAME + Hcy group and the AngII + L - NAME group. The p - value of the survival curves between the two groups was 0.162, indicating no statistically significant difference. ConclusionThe results demonstrated that elevated homocysteine levels did not influence the incidence of intracerebral hemorrhage in hypertensive mice. Hyperhomocysteinemia does not increase the risk of intracerebral hemorrhage in hypertensive mice

neuroscience↗

bioRxiv2026

Riboflavin Deficiency-Induced Angular Stomatitis and Conjunctivitis in Associated with Gut Microbiota Dysbiosis

PurposeRiboflavin deficiency causes ariboflavinosis, and the purpose of this study is to investigate the potential biological factors underlying its occurrence. MethodsMale F344 rats were randomly assigned to R6 and R0 groups. The healthy controls (R6 group) was fed a riboflavin-normal diet (6 mg/kg riboflavin), while the R0 group was fed a riboflavin-deficient diet (0 mg/kg riboflavin) for 16 weeks. Following this period, the R0 group was switched to a riboflavin replenishment diet (6 mg/kg riboflavin) for an additional 4 weeks (weeks 17-20). The bacterial communities were analyzed using high-throughput 16S rRNA gene sequencing. ResultsRiboflavin deficiency induces ariboflavinosis in rats (66.7%), characterized by angular stomatitis and conjunctivitis. With riboflavin replenishment, ariboflavinosis was completely resolved. Riboflavin deficiency altered the gut microbiota co-occurrence network and composition. The potential mechanism is predicted to involve an increase in glycan biosynthesis and metabolism within the gut microbiota, particularly in lipopolysaccharide biosynthesis pathways. ConclusionsThese findings suggest that riboflavin deficiency induces ariboflavinosis by altering the gut microbiota, providing new insights into the mechanisms of riboflavin deficiency and its association with chronic inflammation. Clinical perspectivesRiboflavin is an essential nutrient that must be obtained through the diet, as the human body cannot synthesize it. Since riboflavin is not stored in the body, regular dietary intake is necessary. Riboflavin deficiency is prevalent worldwide and can lead to ariboflavinosis. However, the specific mechanisms underlying riboflavin deficiency-induced ariboflavinosis remain unclear. This study demonstrates that riboflavin deficiency causes ariboflavinosis (characterized by angular stomatitis and conjunctivitis) in rats and reveals a close relationship between riboflavin deficiency and alterations in gut microbiota composition. Gut dysbiosis appears to be a hallmark of ariboflavinosis and may serve as a promising diagnostic biomarker and therapeutic target for the condition.

microbiology↗