Biology subjects

Ji, X.

Publications and source records attributed to Ji, X..

At least 19 recordsLinked to original sources

bioRxiv2026

DNA methylation reveals molecular heterogeneity and urate excretion-associated subtypes of gout

Background: The molecular heterogeneity among gout patients remains largely unexplored, hindered the precise treatment for gout. Common indices for gout subtyping are cumbersome to measure, resulting in poor patient compliance. Methods: We elucidated the molecular heterogeneity of gout and developed a simple, reliable classification method for gout subtypes based on genome-wide DNA methylation, to facilitate individualized treatment. Genome-wide DNA methylation profiling was conducted in two independent cohorts, comprising a discovery cohort (n = 108) and a validation cohort (n = 100) representing four clinical gout subtypes. Results: We identified two gout subgroups associated with the fractional excretion of uric acid (FEUA) but not with 24-hour urinary uric acid levels (UUE) based on unsupervised clustering of whole-genome DNA methylation profiles, highlighting the contribution of epigenetic regulation to molecular heterogeneity in urate metabolism. Enrichment analysis links FEUA-associated subtypes to renal cell types, calcium related pathways and extended to the circulating immune compartment, with neutrophil- versus memory B cell-skewed leukocyte composition. A methylation-based classification model using 18 LASSO-selected CpGs with an XGBoost classifier effectively distinguished patients with high and low uric acid excretion, achieving excellent predictive performance (training cohort: AUC = 0.96, AP = 0.91; validation cohort: AUC = 0.86, AP = 0.85), outperforming the published model with clinical characteristics and SNPs. The signature site-associated genes NEK1 and TRPS1 display significant expression differences among urate-stratified patients and immune cells groups. TRPS1 may affect the AP-1/NF-{kappa}B inflammatory programme. Conclusions: These findings reveal distinct DNA methylation signatures underlying gout heterogeneity and demonstrate the feasibility of DNA methylation markers for gout subtype classification. The proposed methylation-based model provides a promising approach for personalized management of gout.

bioinformatics↗

bioRxiv2026

Impaired glycolysis in bone underlies age-related bone loss in mice

Bone loss and metabolic dysregulation often coexist in older adults, yet the relationship between age-related changes in bone cell metabolism and bone-forming activity remains unclear. By modeling both conditions in aged mice and using in vivo stable-isotope tracing, we demonstrated that age-related decline in bone formation was coupled with an intrinsic impairment of glycolysis within bone tissue. Impaired glycolysis was associated with reduced protein levels of key glycolysis enzymes, and transcriptomics analyses revealed compensatory upregulation of glycolysis genes in aged bone. Metabolic intervention through long-term caloric restriction prevented age-related trabecular bone loss. Furthermore, osteoblast-specific genetic enhancement of glycolysis, via overexpression of HIF1 or the rate-limiting enzyme PFKFB3, significantly improved osteoblast function and increased bone mass in aged mice. Together, the study identifies glycolytic decline as a cell-autonomous driver of osteoblast deficiency and provides proof of principle that reactivating glycolysis can promote bone accrual in aging.

physiology↗

bioRxiv2026

Phylodynamic analysis reveals asymmetric and accelerating global spread of dengue virus

Dengue virus (DENV) continues to expand globally, with increasing incidence in both endemic and non-endemic countries. Leveraging a large global dataset of DENV genomes starting from the 1940s, we reconstructed global and regional patterns of DENV spread using phylogeographic and phylodynamic methods. Our analyses reveal that the current global diversity of urban DENV originated in Asia in the first half of the 20th century. During the more than 100 years since, we find that the highest density of between-country spread occurs within Asia and the region continues to be a global source, seeding transmission in Africa and the Americas. Global spread is, however, not balanced. Even though there is more travel to South America, we show that DENV lineages primarily enter North America (mostly from South Asia) through a link defined by seasonal synchrony across the Northern Hemisphere. Along with a substantial increase in global travel starting ~2010, the rate of spread to the Americas is increasing, establishing new lineages associated with large recent outbreaks. DENV lineages do not frequently spread back to Asia from the Americas or Africa, and we posit this is due to higher genetic diversity in Asia, leading to stiffer competition for invading lineages. Within the Americas, the network of DENV dispersal initiates in North America, primarily the Caribbean, and then is maintained by major hubs in South America. Overall, we find that travel volume, rather than climate factors, is mostly responsible for long-term patterns of DENV spread globally and within the Americas. Our results highlight key nodes in the global dynamics of DENV lineages, and suggest spread will continue to accelerate in a more connected world.

genomics↗

bioRxiv2026

MIRCID: Inferred Hub-miRNAs Drive Cross-Task Improvements in Drug Mechanistic Modeling

Drug mechanism-of-action (MoA) modeling commonly relies on perturbational transcriptomes, but matched microRNA (miRNA) measurements are often unavailable. Inferred regulatory features offer a scalable way to reuse these data. Here, we present MIRCID, a framework comparing gene expression with inferred transcription factor (TF) activity and miRNA expression across pathway classification and similarity-based MoA retrieval. HubmiRNet infers 414 pan-cancer hub miRNAs (HubmiRs) from 977 L1000 landmark genes, achieving a Pearson correlation coefficient of 87.72\%; its 1,298-output variant also outperformed SiCmiR on the full-miRNA task (71.21\% versus 67.30\%). In the evaluated comparisons, miRNA augmentation provided more consistent gains than TF activity. Generic embedding controls showed model-dependent utility, while complementarity analyses identified a distinct, partially linearly recoverable representation that retained gene-derived structure. Illustrative rescue cases linked improved classification to biologically plausible miRNA patterns in samples with weak transcriptional signatures. These findings support inferred HubmiRs as a biologically informed recoding of transcriptomic data for perturbational drug modeling, while leaving recovery of measured perturbational miRNA responses to further validation.

bioinformatics↗

bioRxiv2026

Circulating apoB-containing lipoproteins constituted blood-borne innate immune barrier limits ZIKV dissemination and congenital infection risk

Following establishment of primary viremia, flaviviruses require prompt penetration through vascular endothelium to achieve systemic dissemination, yet most infections remain asymptomatic or self-limited. Canonical defenses, including complement and phagocyte clearance, cannot fully explain this containment, suggesting an additional blood-borne antiviral barrier. Here, we identify apoB containing lipoproteins as potent restriction factors against Zika virus (ZIKV), a devastating danger of global pregnancy. By displaying surface accessible phosphatidylserine (PS), they competitively occupy host PS receptors and block entry by viruses exploiting apoptotic mimicry, including flaviviruses, Ebola, and chikungunya virus. In vivo, increasing apoB lipoproteins enhances serum antiviral activity and limits systemic ZIKV dissemination. This barrier extends to the maternal fetal interface, where elevated maternal LDL attenuates vertical transmission and fetal abnormalities. Its efficacy is modulated by gestational fluctuations in maternal LDL level and low abundance in fetal circulating LDL. Collectively, apoB containing lipoproteins form a constitutive blood-borne innate immune barrier that restrains apoptotic-mimicry viruses during the intravascular phase, limits viral dissemination and reduces congenital infection risk. This study provides a rationale for developing diet augmentation strategy to strengthen this innate barrier in at-risk individuals.

microbiology↗

bioRxiv2026

Mechanosignaling Promotes Macrophage Apoptosis Resistance in Pulmonary Fibrosis via Metabolic Reprogramming

The mechanisms underlying the progression of pulmonary fibrosis in idiopathic pulmonary fibrosis (IPF) and other interstitial lung diseases remain unclear. Increased extracellular matrix stiffness is a hallmark of fibrotic lung diseases. Monocyte-derived macrophages can promote fibrosis progression. However, there is limited understanding of how the mechanical properties of the fibrotic microenvironment influence macrophage phenotypes. Profibrotic macrophages are apoptosis-resistant, and this phenotype is modulated by enhanced mitochondrial bioenergetics. The objective of the study was to determine how lung tissue stiffness impacts macrophage phenotypes and fibrotic progression. We demonstrate that mechanoactivated macrophages exhibit apoptosis-resistance, increased expression of the antiapoptotic protein Bcl-xL and increased mitochondrial oxidative phosphorylation. Critically, the metabolic reprogramming observed in mechanoactivated macrophages is dependent on increased glutaminolysis. Inhibition of glutaminolysis attenuated apoptosis resistance in mechanoactivated macrophages. Moreover, inhibition of Bcl-xL in vivo protected mice against experimental pulmonary fibrosis. Lastly, mechanoactivated primary IPF macrophages produce more profibrotic cytokines and promote extracellular matrix production in precision-cut lung slices. We describe a mechanism for acquired macrophage apoptosis resistance dependent on metabolic reprogramming regulated by extracellular matrix stiffness. Our results identify mechanoactivated apoptosis-resistant macrophages as pro-fibrotic mediators, suggesting a novel therapeutic target in IPF and related fibrotic disorders.

molecular biology↗

bioRxiv2026

An interoperable research agent network for scientific discovery

Recent advances in agentic systems have enabled the autonomous execution of research tasks across scientific domains. However, the rapid emergence of specialized scientific agents for areas such as computational pathology, microbiome research, gene editing, materials science, organic chemistry, and drug discovery has created a fragmented ecosystem of scientific capabilities. While these agents often demonstrate strong performance within their respective domains, limited interoperability makes it difficult to combine expertise across platforms and coordinate complex interdisciplinary workflows. Here we introduce GUIA (Guided-research Utilizing Intelligent Agents), an interoperable research-agent network built upon a flexible Agent-to-Agent (A2A) communication architecture. GUIA enables both in-house and third-party agents to collaborate within shared workflows, allowing scientific capabilities to accumulate through the integration of complementary expertise. We evaluated GUIA through four assessments spanning baseline benchmarking, third-party single-agent integration, third-party multi-agent integration, and cross-server agent collaboration. Furthermore, we demonstrate its practical utility through real-world applications involving therapeutic target discovery, drug discovery, and spatial proteomics analysis. Together, our results show that interoperable research-agent networks can coordinate specialized expertise across independently developed systems, providing a scalable framework for expanding scientific capabilities through collaboration.

bioinformatics↗

bioRxiv2026

Bridging Gene Expression and Morphology: A Cell Size Score and Its Applications Across Multiple Diseases and Physiological Contexts

Cell size is a critical morphological parameter determining cellular functional homeostasis, yet existing large-scale transcriptomic databases lack direct cell size measurement data. By integrating high-resolution immunofluorescence images with transcriptomics, we identified 457 genes significantly correlated with cell area. Based on these findings, we developed an algorithm, Cell Size Score (CSS), to predict cell size from gene expression profiles. Validation across multiple independent datasets, including human cell lines, mouse models, and single-cell spatial transcriptomics, confirmed that CSS accurately predicts cell size. Furthermore, we observed a significant positive correlation between CSS and broad-spectrum chemotherapy drug resistance, suggesting that increased cell volume confers survival advantages to cancer cells. Moreover, CSS analysis of aging revealed sex-dependent, tissue-specific patterns of change, wherein male adipose and cardiac tissues exhibited progressive hypertrophy with age, while female reproductive organs showed significant atrophy. Additionally, CSS significantly increased in skeletal muscle after exercise, indicating that this metric can capture dynamic physiological adaptation processes. This study establishes a bridge between transcriptomics and cell morphology, providing novel insights into retrospectively analyzing the role of cell size in pathological and physiological processes such as cancer and aging using existing omics data, as well as understanding the molecular mechanisms underlying cell size regulation.

bioinformatics↗

bioRxiv2026

Reconciling fast Hepatitis B evolutionary rates with ancient co-divergence

Estimating evolutionary rates and divergence times for hepatitis B virus (HBV) has long been complicated by conflicting calibration approaches and extensive rate variation. To unlock the full potential of ancient and modern HBV genomic data, we develop a Bayesian mixed-effects molecular clock model that accounts for various sources of rate variation including time-dependent rate decay. Our analyses reveal a pronounced decline in evolutionary rates over time, reconciling HBV divergence estimates with human migration events across both deep and more recent timescales. We show that HBV spread into Europe through both Neolithic farming expansions and later steppe migrations, paralleling patterns proposed for Indo-European language origins. Phylogeographic reconstructions suggest that the Neolithic-associated lineage dispersed at approximately 1 km/year, consistent with archaeological estimates, while genotype D expanded during the Bronze Age at an almost threefold higher rate, plausibly driven by technological innovations underlying steppe expansions. Historical overlap between these lineages facilitated recombination, giving rise to genotype E, which has become a dominant HBV genotype in Africa. These findings demonstrate that ancient viral genomes, when analyzed with models capturing complex rate dynamics, provide a powerful lens on human prehistory and the processes shaping pathogen diversity.

evolutionary biology↗

bioRxiv2026

Immune Aging is an Independent Risk Factor for Cardiovascular Disease

Cardiovascular disease remains the leading cause of mortality, yet current clinical predictors miss substantial disease-risk. While the immune system contributes to this residual risk, its complexity has hindered broadly applicable, clinically scalable metrics of immune-state. Here, we establish the prognostic relevance of IMM-AGE, a system-level metric of immune-aging, to cardiovascular disease. We learn reference-free, low-dimensional representations of IMM-AGE across cell, protein, and mRNA measurements, enabling high-fidelity quantification across modalities, blood fractions, and platforms, including standard hospital flow cytometers. Among UK-Biobank participants, 56.9% of IMM-AGE variation remained unexplained by routine clinical measures, and across diverse cohorts totaling ~48,000 individuals, elevated IMM-AGE was independently associated with future cardiovascular risk, intervention outcomes, and mortality. Moreover, incorporation of IMM-AGE into the PREVENT 10-year risk equation significantly improved risk stratification. These findings establish immune-aging as an independent biological dimension of cardiovascular disease-risk and support IMM-AGE as a practical tool for precision risk assessment.

immunology↗

bioRxiv2026

Reversible in vivo regulation of drug metabolizing enzyme CYP1A2 activity through a dTAG knock-in strategy

Drug-metabolizing enzymes determine therapeutic exposure, efficacy and toxicity, but defining their isoform-specific functions in vivo remains challenging. Cytochrome P450 enzymes (P450s) are central to drug metabolism and pharmacokinetics (DMPK) and mediate the phase I metabolism of [~]75% of all marketed drugs. However, conventional knockout models can induce develop-mental and compensatory adaptations, and selective inhibitors are unavailable for many P450 isoforms. Here, we report the use of an inducible chemical-genetic platform for acute and specific degradation of the endogenous P450 enzyme Cyp1a2 in mice. Using CRISPR-Cas9-mediated knock-in editing, we introduced an FKBP12F36V degron into the endogenous Cyp1a2 locus to generate Cyp1a2dTAG mice. Treatment with the dTAG degrader dTAG-13 recruited an E3 ubiquitin ligase to CYP1A2dTAG, resulting in rapid and reversible proteasomal depletion of CYP1A2dTAG in vivo. Temporally controlled CYP1A2dTAG loss altered caffeine pharmacokinetics as expected, validating this model as a functional tool for DMPK studies. By enabling reversible suppression of drug-metabolizing enzymes without permanent deletion or chronic inhibitor exposure, this work establishes targeted protein degradation as a broadly adaptable strategy for studying drug metabolism in vivo and provides a foundation for extending inducible DMPK control to other P450s, conjugating enzymes and transporters.

pharmacology and toxicology↗

bioRxiv2026

Protein structure-informed deep learning enables species-specific codon optimization

Codon usage bias is highly species-specific, posing a major challenge for heterologous protein expression. Existing deep learning approaches to codon optimization rely primarily on DNA or protein sequence information and largely neglect constraints imposed by protein structure and folding. Here, we present Protein structure-Informed Species-specific Codon Optimization (PISCO), a Geometric Vector Perceptron (GVP)-based model that integrates protein sequence, three-dimensional protein structure, and host codon usage statistics to generate optimal, host-specific codon sequences. Compared with protein-structure-agnostic models, PISCO improves codon recovery by 6% and substantially increases similarity to natural coding sequences, reducing divergence by at least 42% in Codon Similarity Index (CSI), 50% in Codon Frequency Distribution (CFD), and 14% in Dynamic Time Warping (DTW) metrics. Ablation analyses demonstrate that incorporating protein folding kinetics and host-specific information is critical to these gains. Moreover, by leveraging host codon usage statistics, PISCO generalizes to optimize codon sequences for species absent from the training data. An autoregressive variant of PISCO further enhances concordance with natural codon usage patterns, at the cost of a modest reduction in codon recovery rate. Wet-lab validation confirms that PISCO-optimized sequences significantly enhance protein solubility and functional expression. Together, these results establish protein structure as a key determinant of species-specific codon optimization and provide a transferable framework for structure-aware gene design.

molecular biology↗

bioRxiv2026

The emergence and molecular evolution of H5N1 influenza viruses in United States dairy cattle

Prior to 2024, highly pathogenic avian influenza H5N1 clade 2.3.4.4b viruses circulated predominantly in wild birds and poultry. In 2024 and 2025, 2.3.4.4b genotypes B3.13 and D1.1 were detected in United States dairy cattle. Using whole-genome and segment-specific phylodynamic inference, we estimate that B3.13 and D1.1 spilled over from wild birds into dairy cattle in late 2023 and late 2024, respectively. Spillover occurred shortly after the formation of the reassortant genotypes and was followed by months of cryptic transmission prior to detection. We found that both B3.13 and D1.1 evolved at higher rates in cattle relative to birds, primarily due to relaxed purifying selection. Site-specific analyses identified genomic sites under positive selection in cattle relative to birds, indicating adaptation and likely contributing to improved viral fitness after spillover. Intensified genomic surveillance in dairy cattle is essential as population immunity introduces additional selection pressures, with ever-changing risk for human emergence.

evolutionary biology↗

bioRxiv2026

Dispersal, adaptation and persistence of H5N1 in the sub-Antarctic and Antarctica

High pathogenicity avian influenza virus (HPAIV) H5N1 reached the sub-Antarctic and Antarctica in 2023, subsequently spreading to remote locations within this region where it had devastating impacts on seal, penguin and albatross populations. The threat to marine wildlife over this broad area exemplifies the need to understand H5N1 long-distance dispersal and evolution. We obtained 104 novel viral genomic sequences from samples that we collected at South Georgia, Kerguelen, Crozet, Prince Edward, Falklands/Malvinas Islands and the Antarctic Peninsula in a region spanning 8,000 kilometers. Using recent phylogeographic modeling advances we show that H5N1 spread encompassed numerous transmission events between distant locations, accumulating mammalian-adaptive mutations in the process. Seals are the most affected species, but we reveal that the long-distance eastward virus dispersal better aligns with the long-distance movements of large petrels and albatrosses. The risk of H5N1 endemisation, dispersal to other locations and ongoing evolution are highly concerning.

microbiology↗

bioRxiv2026

Artificial Recombination of Nonribosomal Peptide Synthetases Rapidly Evolves Natural Products

Efficient engineering of nonribosomal peptide synthetases (NRPSs) is a key strategy for expanding valuable peptide natural products. Current bioinformatics-guided approaches are often constrained by a set of predefined fusion sites, remaining experimentally challenging in most NRPS systems. Here we present the Recombineering Accelerated Evolution (RACE), which harnesses Red/ET recombineering mediated partially matched homologous recombination to recapitulate recombination-driven NRPS evolution on a highly accelerated timescale. Application of RACE to six known NRPS gene clusters generated 830 recombinants and yielded over 600 novel peptides including novel bis-lipopeptides. These recombinants reveal 112 previously unrecognized recombination fusion sites, providing an extensive landscape for NRPS evolution and more useful resources for guiding NRPS engineering. The RACE establishes a new paradigm for programmable NRPS evolution and enables rapid discovery of bioactive peptides.

synthetic biology↗

bioRxiv2026

Preserved Embedding of Neural Population Activity for Brain Computer Interfaces

Inferring neural population dynamics from motor cortex recordings is essential for brain-computer interface (BCI) control. However, neural activity patterns during similar behaviors exhibit systematic variability across recording sessions, subjects, and task contexts due to neural plasticity, anatomical differences, and varying movement distances. This variability degrades BCI decoder performanc when applied to different sessions, subjects, and task contexts. Here, we introduce Multi-Aligned Neural Data Transformer (MANDT) that extracts consistent embeddings of the motor cortical dynamics. By learning these consistent embeddings, MANDT enables generalization of BCI decoding: a decoder trained on data from one session maintains robust performance when applied to new sessions without recalibration. We validated our approach on neural recordings from rhesus monkeys performing the delayed-reach task. Lastly, we show that CEBRA can be used for the robotic control, enabling direct neural control of a humanoid robot, allowing monkeys to guide complex robotic manipulation tasks through brain signal.

neuroscience↗

bioRxiv2026

The Red Rice Bran Extract (RRBE) Mitigates Photoaging by Targeting Mitochondrial Oxidative Stress and Regulating Thermal Damage Responses

The photoprotective efficacy of natural skin-active complexes is well recognized, yet their ap-plication is often hindered by the challenge of deciphering their complex, multi-component, and multi-target mechanisms. To bridge the gap between established phenotypes and molecular mechanisms, we developed an AI-driven integrated platform that combines phytochemical profiling, network pharmacology, and deep learning-based target prediction with rigorous biophysical validation. We applied this platform to investigate RRBE, a bioactive complex refined from red rice bran extract. In vivo clinical studies confirmed that RRBE significantly accelerates the resolution of UV-induced erythema, while cellular and 3D tissue models demonstrated robust suppression of oxidative stress and DNA damage responses. To decode its material basis, the platform deconstructed RRBE into 10 distinct chemical modules. Leveraging our SCOPE-DTI deep learning model for global target prediction, we identified flavonoids (Module 1) and phenolic acids (Module 10) as the primary bioactive drivers relevant to photoprotection. These computational predictions were structurally supported by molecular docking and definitively validated in a physiological environment via Cellular Thermal Shift Assay-Mass Spectrometry (CETSA-MS). Mechanistically, RRBE functions through a synergistic polypharmacology: (1) Module 1 components, represented by procyanidin B2, target NDUFA7 to stabilize mitochondrial function and mitigate ROS; (2) Module 10 components, exemplified by caffeic acid, ferulic acid, and p-coumaric acid, engage FKBP11 and HSP90AA1 to regulate protein homeostasis and stress responses. This work not only deciphers the polypharmacological basis of RRBEs photoprotective action but also validates a scalable "AI-guided, cell-validated" discovery pipeline, offering a rational paradigm for uncovering the protective benefits of complex natural extracts.

systems biology↗

bioRxiv2025

Pharmacological Inhibition of FKBP51 Mitigates Early Life Adversity-Induced Social Deficits

Early life adversity (ELA) is a major risk factor for psychiatric disorders, but targeted preventative strategies are lacking due to poor mechanistic insight. The FKBP51 protein, a co-chaperone of the glucocorticoid receptor, is a key mediator of stress vulnerability. We tested if pharmacological inhibition of FKBP51 with the selective inhibitor SAFit2 prevents the long-term consequences of ELA. Mice exposed to ELA exhibited persistent deficits in social behavior, manifesting as social subordination in adolescence and adulthood. Early-life SAFit2 treatment fully rescued these ELA-induced behavioral impairments. Transcriptional profiling across six stress-relevant brain regions revealed that SAFit2 normalized ELA-driven gene expression changes, particularly in the medial prefrontal cortex and nucleus accumbens. Functional analysis showed the rescue converged on immunoregulatory and neuroactive ligand-receptor signaling pathways. Our findings establish FKBP51 as a critical pharmacological target for reversing the lasting impact of early life adversity on brain function, offering a path toward preventative treatment for ELA-related psychopathology.

neuroscience↗