Biology subjects

Jin, X.

Publications and source records attributed to Jin, X..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Decoding Immune Reconstitution Failure in People Living with HIV through Single-cell Genomics

Immune non-responders (INRs), a subset of people living with HIV (PLWH), fail to achieve full immune reconstitution and remain at increased risk of morbidity, mortality, and non-AIDS-related illnesses. As the mechanisms underlying this impaired immune recovery remain poorly understood, we performed single-cell multi-omics profiling on peripheral blood mononuclear cells from 43 INRs, 47 immune responders and 53 healthy donors. Our dataset comprises 2,744,009 transcriptomes and 1,226,658 chromatin-accessibility profiles across 58 identified immune cell types. INRs exhibited markedly elevated inflammatory signaling, increased apoptotic activity, and dysregulation of immune-activating ligand-receptor interactions. Additionally, we identified 2,996 interaction cis-eGenes and 5,938 cis-caPeaks, and validated key cell type-specific cis-xQTL effects associated with immune reconstitution failure. Furthermore, we identified 3,551 sc-eQTLs and 872 cell-state interaction eQTLs (ieQTLs) in CD4+ T cells. A genotype-specific upregulation of OAS3 in INRs, likely regulated by STAT1, was identified based on these results. We developed scPRISM (single cell Predictive Reconstitution Immune Status Model), a multi-modal framework that integrates scRNA-seq and scATAC-seq data to perform the dual tasks of differentiating disease states and predicting gene expression from chromatin accessibility, thereby deciphering the cell type-specific cis-regulatory basis of the disease. Our study delineates the transcriptional and epigenomic landscape of immune dysregulation in INRs and provides new genetic insights into HIV-associated immune reconstitution failure. These findings offer a foundation for developing biomarkers and precision therapies to restore immune function in PLWH.

genetics↗

bioRxiv2026

Helminth-remodeled microbial indole-3-lactic acid drives AhR-dependent disease tolerance

Helminths systemically suppress host immunity, yet whether they impose immune tolerance by rewiring host-associated microbial metabolism remains unclear. Here we show that Trichinella spiralis infection remodels intestinal tryptophan metabolism to generate an AhR-dependent regulatory immune state. T. spiralis infection enriched the commensal bacterium Ligilactobacillus murinus, which converted tryptophan into indole-3-lactic acid (ILA), a microbial metabolite that directly engaged the aryl hydrocarbon receptor. Antibiotic-mediated microbiota depletion abolished infection-induced ILA accumulation, AhR activation and Treg/Th17 rebalancing, whereas fecal microbiota transplantation from infected donors or supplementation with L. murinus or ILA restored these effects. Pharmacological blockade or genetic deletion of AhR eliminated the ability of T. spiralis, L. murinus and ILA to restrain LPS-induced cytokine-storm-like lung inflammation, establishing AhR as an essential host node in this circuit. Extending these findings to viral inflammatory disease, oral ILA improved survival and reduced pulmonary immunopathology in SARS-CoV-2-infected K18-hACE2 mice. Re-analysis of human COVID-19 metabolomic data further revealed reduced circulating ILA in severe disease. These findings define a helminth-remodeled microbial tryptophan metabolic pathway that promotes disease tolerance and identify the ILA-AhR axis as a candidate postbiotic strategy for limiting hyperinflammatory tissue injury.

immunology↗

bioRxiv2026

sigNATURE maps cohort-specific T-cell states to reproducible programs of ICI response

Immune checkpoint inhibitors (ICIs) can induce durable responses across cancers, yet T-cell biomarkers of response remain difficult to reproduce across single-cell RNA-seq studies. A major reason is that T-cell states are typically defined de novo within each cohort, making reported marker genes sensitive to cohort composition and analytic choices rather than stable cellular programs. Here we present sigNATURE (signature Normalization and Atlas-based T-cell Understanding for Reproducibility and Evaluation), a reference-guided framework that maps query cells onto large CD4+ and CD8+ T-cell atlases, evaluates published ICI-response markers in an atlas-aligned coordinate system, and quantifies the atlas support of mapped cells through a cell-level identifiability score. We applied sigNATURE to two independent ICI scRNA-seq cohorts comprising 36 non-small cell lung cancer patients and 15 skin cancer patients (11 basal cell carcinoma and 4 squamous cell carcinoma). Across cohorts, sigNATURE-derived features more robustly resolved response-associated T-cell structure than cohort-derived state definitions, yielding clearer unsupervised separation of responders and non-responders, enabling integrated analysis of independent studies in a shared atlas-aligned space, and improving mean response-prediction AUC from 0.469 to 0.746. Using identifiability score, we further identify terminally differentiated effector CD8+ T cells and regulatory CD4+ T cells as prominent response-associated states across studies, prioritizing published markers in terms of robust, atlas-resolvable cell states. Using this framework. Together, these results establish sigNATURE as a framework for improving the reproducibility, cross-cohort comparability, and mechanistic interpretability of single-cell ICI biomarkers.

immunology↗

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

Polyclonal-Monoclonal Transition in Lung Squamous Cell Carcinoma Evolution

The evolutionary trajectory of lung squamous cell carcinoma (LUSC) remains poorly defined, hindering the development of effective therapies. By integrating genomic and transcriptomic sequencing from human LUSC specimens, we delineated a polyclonal-to-monoclonal evolutionary trajectory during LUSC progression. This evolutionary pattern was corroborated by single-cell RNA sequencing, which revealed consistent tumor cell heterogeneity. Specifically, the SBS5 mutational signature was enriched and correlated with poor prognosis independent of tumor stage. By further utilizing the spontaneous LUSC mouse model to identify key genomic and genetic events in LUSC progression, we observed that the JNK pathway was inhibited and that cytoskeleton-related pathways were dysregulated during LUSC development, and identified the mutations in the JNK pathway (e.g., DACT1) and cytoskeletal regulators (e.g., KIF26A). Collectively, these findings established a polyclonal-monoclonal evolution paradigm for LUSC, potentially regulated by JNK pathways, which could benefit LUSC precision therapeutics.

genomics↗

bioRxiv2026

Complete genome-derived metabolic interactions reveal impact of gut ecology on human health

Metabolic interactions govern gut microbiome assembly, yet functional rules remain obscured by genomic incompleteness and fragmentation. Here, we leverage 1,150 complete genomes to construct genome-scale metabolic models, demonstrating that draft genomes introduce systemic artifacts missing critical transport functions. We observe that metabolic interactions are intrinsically driven by genomic traits and niche specialization rather than random association. We stratifies strains into four ecological groups, including active players, resource predators, utilizers, and contributors, each of which exhibits distinct metabolite exchange/competition signatures. This functional architecture is strongly coupled to secondary metabolism. Applying this framework to inflammatory bowel disease revealed distinct temporal dynamics of ecological groups across disease subtypes, where group-specific dysbiosis predicted clinical phenotypes better than whole community profiles. Furthermore, integrating metabolic and co-occurrence networks to identify keystone for classifiers as features significantly improved disease diagnosis. Our study provides novel insights into gut metabolic interaction landscape, bridging the gap between genomic potential and clinical utility for precision microbiome therapeutics.

microbiology↗

bioRxiv2026

Complete Genomes of Cultivated Gut Bacteria Reveal Mobile Genetic Element-Driven Functional Diversity with Therapeutic Implications

Cultivated bacterial isolates are essential for elucidating gut microbiota functions, yet reliance on fragmented draft genomes and limited mobile genetic element (MGE) annotation has constrained high-resolution analyses. Here, we present the Cultivated Complete Genome Reference (CCGR), a comprehensive compendium of 1,150 fully circularized human gut bacterial genomes. Overcoming draft limitations, CCGR achieves chromosome-scale resolution and reveals distinct evolutionary strategies shaping gene topological organization in response to bacterial growth. Furthermore, we decode the dynamic landscape of MGEs, demonstrating that broad-host-range phages follow strict positional patterns by integrating at conserved chromosomal hotspots flanked by nutrient transporter loci. Importantly, we provide mechanistic in vivo evidence that accessory genomic elements drive strain-level functional heterogeneity. Specifically, the plasmid-encoded scrK gene in Levilactobacillus brevis involved in fructose catabolism is required to mitigate fructose-diet-exacerbated colitis. Collectively, CCGR establishes a framework linking chromosomal architecture and MGEs to bacterial evolution and host health, enabling precise, strain-resolved functional investigations.

microbiology↗

bioRxiv2026

Optimizing resource allocation in Miscanthus breeding with sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse biobased products. Increasing biomass yield will increase profitability and environmental benefits, so is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; GxE interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for GxE interaction presented the highest PA and the lowest MSE for CNN (PA: [~]0.77, MSE: [~]0.5) and YDY (PA: [~]0.70, MSE: [~]1.3) while for TCM and AIL these ranged from [~]0.28 to 0.41 and [~]1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

genomics↗

bioRxiv2026

Multi-trait Multi-environment Genomic Prediction Strategies for Miscanthus sacchariflorus Populations

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (GxExT) and (2) two single-trait multi-environment (STME) models (with and without GxE interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

genomics↗

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

Genome-scale functional mapping of the mammalian whole brain with in vivo Perturb-seq

Functional genomics studies have provided critical insights into cell type-specific gene regulatory programs, but to date most have been conducted in wild-type tissues or cell cultures. Here, we present a gene expression functional atlas across the mouse brain. We use an enhanced in vivo Perturb-seq platform to analyze transcriptome-wide responses to loss of 1,947 disease-associated genes, profiling over 7.7 million cells spanning major brain regions and neuronal populations. We find striking cell-type-specific essentiality and transcriptional programs and show that closely related disease genes such as two NMDA receptor subunits can drive opposing transcriptional programs. Together, this work reveals insights into the genetics and mechanisms of neurodevelopmental, psychiatric, and neurodegenerative diseases in vivo, paving the way for the design of future genetic medicine.

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

In vivo multiomic Perturb-seq with enhanced nuclear gRNA capture

In vivo CRISPR screening with joint transcriptomic and chromatin readouts has been limited by inefficient recovery of gRNAs from nuclei. Here, we developed in vivo multiomic Perturb-seq, an effective platform combining nuclear transcript anchoring with gRNA-specific capture and amplification to enable high-fidelity, high-recovery gRNA assignment and scalable perturbation-resolved single-nucleus multiomics. Applying this platform to interrogate neurodevelopmental disorder risk genes in the developing cortex reveals cell-type-specific transcriptomic and epigenomic perturbation phenotypes.

genomics↗

bioRxiv2026

Chiral Histidine-Modified Gold Nanoclusters Loaded into Cationic Lipid Nanoparticles for Treatment of Biofilm-associated Infections

Antimicrobial resistance has heightened the risk of device-associated infections, in which Staphylococcus aureus (S. aureus) biofilms persist through extracellular-matrix protection and marked physiological heterogeneity. Gold nanoclusters (AuNCs) can disrupt biofilms via redox-associated stress, but limited in vivo stability and poor local retention constrain their translational potential. Here, we synthesized chiral histidine-modified AuNCs (D-, L-, and DL-AuNCs) and formulated them into mildly cationic lipid nanoparticles (AuNCs@LNP) by microfluidic assembly to enhance biofilm engagement while preserving nanocluster identity. The resulting particles were [~]100-120 nm with high gold loading and a modest charge reversal. In established S. aureus USA300 biofilms, free DL-AuNCs reduced viable burden by up to 5.6 log10 CFU/mL, suppressed metabolic activity, and increased ROS signaling. Notably, LNP encapsulation maintained bactericidal activity and further improved killing by [~]1 log10 CFU at matched Au dose, while substantially enhancing biomass removal (crystal violet residual biomass: 63.77% vs 45.17%), consistent with carrier-mediated matrix destabilization. In a subcutaneous implant infection model, a single perilesional dose of DL-AuNCs@LNP reduced implant-associated bacterial burden by 2.6 log10 CFU per implant versus PBS. These results establish a modular antibiofilm platform that couples nanocluster-driven killing with lipid-facilitated biofilm disruption and defines an efficacy-tolerability window to guide optimization of locally delivered antibiofilm nanomedicines. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=61 SRC="FIGDIR/small/707929v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@119134forg.highwire.dtl.DTLVardef@142e7b2org.highwire.dtl.DTLVardef@1799550org.highwire.dtl.DTLVardef@139c33a_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

jazzPanda: A hybrid approach to find spatial markergenes in imaging-based spatial transcriptomics data

MotivationSpatial transcriptomics resolves the organisation of tissues and their cellular neigh-bourhoods, where cell type identification depends on reliable marker gene detection. Existing marker methods were developed for single-cell RNA sequencing and ignore the spatial coordinates of cells and transcripts, a particular problem for imaging-based platforms where transcript counts per gene per cell are extremely sparse. ResultsWe present jazzPanda, a method for detecting spatially informative marker genes in imaging-based spatial transcriptomics. Transcript and cell coordinates are aggregated into one-dimensional vectors by spatial binning, and gene vectors can be built directly from transcript coordinates without cell segmentation. Markers are identified either by permutation-based rank correlation for single-sample data, or by a lasso-regularised generalised linear model that accommo-dates multiple samples and platform-specific background signal from negative-control probes. To our knowledge, jazzPanda is the only marker detection method to account jointly for spatial distri-bution, replication across samples, and platform background. Benchmarked against the Wilcoxon rank sum test and t-tests on public Xenium and CosMx data, jazzPanda recovers markers with stronger spatial concordance and greater specificity, yielding smaller, more interpretable marker sets. The same vector framework also extends to cluster- and gene-level co-location analysis. Availability and implementationjazzPanda is implemented as an open-source R/Bioconductor package, freely available at https://bioconductor.org/packages/jazzPanda. Analysis code for this article is at https://github.com/phipsonlab/jazzPanda_paper, with an accompanying analysis website at https://phipsonlab.github.io/jazzPanda_workflowr/. Datasets and scripts are deposited at https://zenodo.org/records/18149456. Contactphipson.b@wehi.edu.au

bioinformatics↗

bioRxiv2026

ATLAS: Population-Level Disease Locus Discovery via Differential Attention in Genomic Language Models

Identifying disease-associated genetic variants remains a key challenge in genomics, especially in small cohorts or for rare and complex mutation types where genome-wide association studies (GWAS) often fall short. We introduce ATLAS, a population-level framework that leverages attention signals from pretrained genomic language models (gLMs) to detect disease-associated genes and loci directly from raw sequences--without requiring explicit variant calls or supervised training. ATLAS first performs gene-level differential attention analysis to prioritize candidate genes, followed by base-level analysis to localize disease-associated regions at single-haplotype resolution. We validate ATLAS on synthetic and {beta}-thalassemia datasets, demonstrating robust performance across diverse allele frequencies (down to 10%), cohort sizes (below 200 individuals per group), and genomic scales. Compared to GWAS, ATLAS achieves higher recall of known loci and captures haplotype-specific signals missed by traditional methods. Cross-model benchmarking further shows that precise localization depends on both model size and pretraining on diverse human genomes. In summary, ATLAS offers a scalable, sequence-native alternative to traditional statistical genetics.

genetics↗

bioRxiv2026

3D Cell-Matrix Mechanical Interaction Models for Cancer Invasion and Drug Evaluation

Cancer cells breach the extracellular matrix (ECM) using both protease-driven degradation and force-driven physical remodeling, yet most anti-metastatic drug screens still rely on biochemical assays that overlook cell-matrix mechanical reciprocity. Here, we present a fully synthetic 3D invasion platform based on cellular force-responsive polyisocyanide (PIC) hydrogels that isolates biophysical invasion mechanisms. Cell-generated forces align and densify the PIC fibrous network, reproducing hallmark matrix remodeling seen in the tumor microenvironment. A constitutive model, parameterized by the critical stress for strain stiffening effect, links matrix nonlinear elasticity to pericellular stiffening, long-range mechanotransmission, and intercellular coupling. Using this system, we show that breast cancer cells invade by pulling and pushing the network even when matrix metalloproteinases are inhibited, revealing a physical bypass of protease blockade. Accordingly, broad-spectrum metalloproteinase inhibitors that suppress invasion in Matrigel fail to inhibit invasion here, exposing a limitation of current drug-evaluation pipelines. In co-culture, cancer-associated fibroblasts markedly accelerate invasion by generating aligned fiber tracks through higher contractility, implicating CAF-driven mechanical remodeling as a key route for breaching barriers during metastasis. The platform is thermoresponsive, compatible with standard Transwell formats, enables direct imaging of fiber architecture and invasion fronts, and decouples biophysical from biochemical cues for mechanism-aware, animal-free assessment of anti-metastatic therapies.

cancer biology↗

bioRxiv2026

Harnessing FBXO31 with terminal amide-functionalized molecules for targeted protein degradation

Targeted protein degradation (TPD) is a powerful strategy for controlling protein abundance. Here, we establish FBXO31 as a TPD-competent E3 ligase by exploiting its recognition of C-terminal amide-bearing degrons. Using an amidated Ala-Phe motif as a chemical recruiter, multiple small-molecule binders can be transformed into FBXO31-dependent degraders that induce rapid and potent target degradation. Mechanistic studies confirm FBXO31-mediated ternary complex formation and identify key residues in FBXO31 required for recruiter engagement and target degradation. We further show that an FBXO31-based multi-kinase degrader exhibits a distinct and broader degradation profile than a CRBN-based degrader, highlighting a potentially expanded degradable target space beyond CRBN.

biochemistry↗