Biology subjects

Xing, J.

Publications and source records attributed to Xing, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Single-cell multi-omics maps clonal IEL expansion and epithelial remodelling in refractory coeliac disease

Background Refractory coeliac disease type 1 (RCD1) lacks defining molecular markers, and the immune and epithelial mechanisms sustaining intestinal injury remain poorly understood. Objective To define the clonal immune and epithelial states that distinguish RCD1 from active coeliac disease (ACD) and determine their spatial organisation in the duodenal mucosa. Design We integrated single-cell RNA sequencing, CITE-seq surface proteomics and paired T-cell receptor sequencing of duodenal immune and epithelial compartments from Healthy controls (n = 6), ACD (n = 7), RCD1 (n = 9) and RCD2 (n = 2), with spatial transcriptomics in a subset of biopsies. Results RCD1 showed widespread TCR{beta} and TCR{gamma}{delta} clonal expansion across multiple IEL states, extending beyond previously defined mutation-bearing aberrant clones. Distinct IEL populations converged on a shared programme of adaptive persistence, innate-like signalling, metabolic fitness and cytoskeletal remodelling; GZMK expression marked both clonally expanded and non-clonal disease-associated states. In parallel, RCD1 epithelium showed loss of mature absorptive cell states and expansion of stress-associated, immune-interacting and regenerative programmes. Transit-amplifying cells acquired differentiation and tissue-remodelling signatures, while enteroendocrine cells expanded and developed a sensory-neurosecretory programme involving TRPA1, TRPV1, vesicle trafficking and NEUROD1 regulon activity. Spatial transcriptomics localised regenerative and enteroendocrine-associated epithelial programmes adjacent to immune-visible epithelial regions and KLRK1/GZMK-expressing IEL-rich niches in refractory tissue. Conclusion RCD1 represents a distinct mucosal state characterised by coordinated clonal IEL adaptation and epithelial remodelling, rather than simple amplification of ACD, providing a cellular framework for persistent tissue injury and disease stratification.

immunology↗

bioRxiv2026

An Open Benchmark for Systems Vaccinology: Insights from the CMI-PB Challenges

Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.

immunology↗

bioRxiv2026

Assessing tensor decomposition quality of immune profiling data from a dictionary learning perspective

Systems-level immune profiling data arising from longitudinal studies of vaccination or infection has an inherent multi-index array structure. While tensor decomposition of such datasets has gained popularity, choosing a rank and trial for a decomposition is not straightforward. We show that taking into account the experimental data model can inspire the development of new metrics to assess the quality of a Non-negative CANDECOMP/PARAFAC (NCPD) decomposition, and can thus be used to choose a rank and trial for the decomposition. Moreover, we show how framing the results via a dictionary learning framework can better enable interpretation of the components of the decomposition.

bioinformatics↗

bioRxiv2026

Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease

Gene expression changes along cell lineages, but most single-cell RNA-seq analyses treat cells as independent snapshots and ignore their phylogenetic relationships. Here we present LaVOUS, a lineage-aware probabilistic framework for modeling sparse single-cell gene-expression counts on reconstructed lineage trees. LaVOUS couples Brownian motion and Ornstein-Uhlenbeck models of latent transcriptional dynamics with negative-binomial observation models and scalable variational inference, enabling likelihood-based tests for gene-expression heritability, branch-specific expression shifts, and ancestral expression reconstruction. In simulations, LaVOUS improved detection of lineage-associated expression changes over Gaussian phylogenetic models and accurately reconstructed expression histories across expression levels. Applied to lineage-resolved single-cell datasets from metastatic lung cancer, class-switching B cells, and the developing brain, LaVOUS identified expression changes associated with metastatic progression, isotype switching, and neuronal differentiation. LaVOUS provides a general framework for studying single-cell expression dynamics across development and disease.

bioinformatics↗

bioRxiv2026

Robust semi-supervised scRNA-seq integration from virtual adversarial learning

Single-cell RNA sequencing integration methods that rely solely on transcriptomic data often struggle to preserve fine-grained distinctions between closely related cell subtypes. As a result, cell populations that are separable in the raw data may become over-mixed after integration, reducing biological resolution and interpretability. Incorporating marker gene information can potentially address these issues; however, the variability and complexity of available marker sets limit their effective application. To address this, we introduce scCRAFT+, a semi-supervised integration model that innovatively incorporates marker gene information through Virtual Adversarial Training (VAT). By jointly optimizing marker-derived supervision and transcriptome-wide representations, VAT enforces local prediction smoothness among transcriptionally similar cells, improving robustness to noisy marker annotations while enhancing both integration quality and cell type auto-annotation. This targeted approach significantly enhances annotation accuracy and robustness, particularly when faced with incomplete or incorrect marker gene sets. Benchmarking shows that scCRAFT+ achieves consistently stronger performance than current unsupervised and supervised integration approaches, resulting in improved integration quality and biologically meaningful sub-cell type auto-annotations.

bioinformatics↗

bioRxiv2026

Trichome acylsugar defenses are equally effective against leaf-mining and free-living insects

Trichomes are epidermal projections, crucial components of plant defense systems. Whether these epidermal defenses are effective against leafminers, which infest the internal mesophyll layers, remains unclear. Using the devasting invasive tomato pest, Phthorimaea absoluta (tomato leafminer), and the trichome-rich wild tomato, Solanum pennellii, we demonstrate that trichome produced acylsugars effectively inhibit the growth of endophagous P. absoluta larvae feeding inside leaves. High spatial resolution leaf- and frass-metabolite analysis by MALDI-MSI and Stimulated Raman Scattering microscopy revealed that trichome-synthesized acylsugars are translocated into the mesophyll tissues when larvae infest leaves. We identified an ATP-binding cassette (ABC) transporter, SpABCB5, as the mediator of this translocation and as an essential component of defense against leafminers. These data reveal that epidermal trichomes not only defend against free-living (ectophagous) herbivores through direct contact or rapid deposition of chemical defenses onto leaf surfaces, but also defend against endophagous leafminers by rapidly depositing chemical defenses into internal tissues. By delivering mines against leaf miners, trichomes offer new avenues for breeding crops with resistance to leafminers.

plant biology↗

bioRxiv2026

A bacterial NO-binding sensor domain evolved through acquisition of a cytochrome-derived c-type heme-binding motif

Diverse bacterial sensory domains detect intracellular and extracellular cues and relay this information to downstream signaling pathways. Per-Arnt-Sim (PAS) domains are well known to bind cofactors such as heme for intracellular sensing, whereas Cache domains - the largest family of bacterial extracytoplasmic sensor modules - are thought to function exclusively as cofactor-independent ligand-binding domains. Here, we identify and characterize a large family of Cache domains that contain a covalently bound c-type heme. Systematic sequence analysis of the DUF3365/Tll0287 family of unknown function reveals exceptional conservation of a canonical CXXCH heme-binding motif, establishing c-type heme binding as a defining feature of this domain family. Structure- and sequence-based comparisons demonstrate that, despite prior annotation as PAS-like, these domains belong to the Cache superfamily and share a conserved Cache fold augmented by a distinct structural element harboring the heme-binding motif. Heme containing Cache domains are widely distributed across bacterial phyla, with strong enrichment in Gracilicutes, and frequently occur as stand-alone modules, an atypical presentation of Cache domains. Phylogenetic analyses support insertion of a short cytochrome c-derived fragment containing the heme-binding motif into a preexisting Cache scaffold, representing a previously unrecognized mode of sensor-domain evolution. Spectral and ligand-binding analyses indicate that heme containing Cache domains fused to signal-transduction modules have properties typical of nitric oxide, but not oxygen, sensors; whereas stand-alone domains likely have redox-related roles. Our findings expand the functional repertoire of Cache domains to include covalently bound cofactors and reveal fragment-level recruitment of enzymatic motifs as a route for the emergence of bacterial sensory systems.

microbiology↗

bioRxiv2026

Lhcf2 in the peripheral antenna is essential for non-photochemical quenching and Lhcx1 accumulation in the diatom Chaetoceros gracilis

Photosynthetic organisms must continuously balance efficient light harvesting with protection against excess excitation energy, a challenge met by nonphotochemical quenching (NPQ). Although the molecular components involved in NPQ have been extensively studied, how the essential energy-quenching site is assembled remains poorly understood, particularly in marine diatoms. Here, we show that in the centric diatom Chaetoceros gracilis, which belongs to one of the most abundant and diverse genera in marine phytoplankton, the light-harvesting complex protein Lhcf2 is required for energy-dependent NPQ (qE). Targeted knockout of Lhcf2 abolished qE by preventing the stable accumulation of Lhcx1, the canonical NPQ effector in this species. Lhcf2 localizes to the peripheral antenna system and associates with Lhcx1 in a higher-order complex suggested by biochemical and functional analyses. In contrast, other established NPQ-related factors, including the trans-thylakoid proton gradient and the accumulation of diatoxanthin, were not affected by the loss of Lhcf2. These results identify a non-Lhcx-type light-harvesting complex protein as an essential structural component for qE-NPQ and establish a general design principle for the cooperative assembly of photoprotective energy-quenching sites in eukaryotic photosynthesis, with implications for marine carbon fixation. Significance StatementPhotosynthetic organisms must balance efficient light harvesting with protection against excess excitation energy. Nonphotochemical quenching (NPQ) is a conserved photoprotective mechanism, yet how the essential energy-quenching site is assembled remains unclear. Here, we show that in the marine diatom Chaetoceros gracilis, the peripheral light-harvesting complex protein Lhcf2 is strictly required for energy-dependent NPQ by enabling the stable accumulation and functional organization of the canonical NPQ effector Lhcx1. Our results reveal that photoprotective energy dissipation depends on cooperative interactions between distinct classes of light-harvesting complex proteins rather than on a single specialized factor. This study establishes a general design principle for NPQ machinery in eukaryotic photosynthesis, with implications for marine carbon fixation under fluctuating light environments.

plant biology↗

bioRxiv2025

An ancient transcription factor functions as the master regulator of primary cilia formation

How ancient transcription factors are repurposed during evolution to drive the functional diversification of conserved organelles remains a fundamental question in biology. Although highly conserved in basic morphology, eukaryotic cilia vary extensively in their sizes and functions. Previously, we showed that an evolutionarily ancient transcription factor, X chromosome-associated protein 5 (Xap5), controls motile ciliogenic transcriptional programs during mouse spermatogenesis (Wang et al., 2025). Here we show that Xap5 functions as the master regulator of primary ciliogenesis. Xap5 associates with the nuclear protein Non-POU domain-containing octamer-binding protein (Nono) to form a regulatory module that directly binds and activates the transcription factors Sox5 and Sox9. Genetic ablation of Xap5 or Nono impairs primary ciliogenesis, and loss of Sox5 disrupts the downstream ciliogenic program, consistent with the established role of Sox9. Collectively, our results provide new insight into how complex ciliary transcription factor networks determine ciliary diversity during evolution, and suggest that defects in this regulatory axis may contribute to the etiology of human ciliopathies.

cell biology↗

bioRxiv2025

Evaluation and Aggregation of Active Module Identification Algorithms

1High-throughput sequencing methods have generated vast amounts of genetic data for candidate gene studies. However, the complexity of the disease genetic structure often results in a large number of candidate genes and poses a significant challenge for these studies. To explore the multi-gene interactions and elucidate the genetic mechanism, candidate genes are often analyzed through Gene-Gene interaction (GGI) networks. These networks can become very large, necessitating efficient methods to reduce their complexity. Active Module Identification (AMI) is a common method to analyze GGI networks by identifying enriched subnetworks representing relevant biological processes. Multiple AMI algorithms have been developed for biological datasets, and a comparative analysis of their behaviors across a variety of datasets is crucial to their application. In this study, we introduce a framework to compare and aggregate the modules produced by multiple AMI algorithms. We first used a modified Empirical Pipeline to validate the output of four AMI algorithms - PAPER, DOMINO, FDRnet, and HotNet2 - and find that no single algorithm performs well across the different datasets. Using the Earth Movers Distance to measure pairwise module similarity, we find that the outputs of different algorithms are structurally distinct, suggesting that each captures different aspects of the underlying biology. These findings suggest that a comprehensive analysis requires the aggregation of outputs from multiple algorithms. We propose two methods to this end: a spectral clustering approach for module aggregation, and an algorithm that combines modules with similar network structures called Greedy Conductance-based Merging (GCM). The merging algorithm not only allows researchers to obtain a set of cohesive modules from multiple algorithms, it also has the potential of identifying "hidden" genes that are not present in the original input data from the network. Overall, our results advance our understanding of AMI algorithms and how they should be applied. Tools and workflows developed in this study will facilitate researchers working with GGI and AMI algorithms to enhance their analyses. Our code is freely available at https://github.com/LiuJ0/AMI-Benchmark/.

bioinformatics↗

bioRxiv2025

Expression screening and functional verification of recombinant hirudin from novel chassis cell Chlamydomonas reinhardtii

Hirudin is a potent anticoagulant peptide whose clinical application is limited by scarce natural supplies and the suboptimal activity of recombinant versions from conventional microbial hosts lacking key post-translational modifications. Here, for the first time, we establish the "Generally Regarded as Safe" (GRAS) microalga, Chlamydomonas reinhardtii, as a novel biological chassis for producing fully functional hirudin. Using high-density heterotrophic fermentation, we generated a bioactive lyophilized algal powder suitable for oral delivery. Crucially, the algal-derived hirudin underwent proper tyrosine sulfation--a key modification absent in prokaryotic hosts--conferring exceptionally high thrombin-inhibitory activity (up to 20,000 ATU/mg). When orally administered to a murine thrombosis model, this hirudin-loaded alga demonstrated potent antithrombotic efficacy. It also exhibited a superior safety profile, showing no signs of the thrombocytopenia or bleeding associated with traditional anticoagulants. This study establishes a green, scalable biomanufacturing and oral delivery platform for therapeutics, highlighting transformative potential at the nexus of agricultural bioengineering, functional foods, and biomedicine.

synthetic biology↗

bioRxiv2025

Bayesian inference of tissue-migration histories in metastatic cancer from cell-lineage tracing data

Cell-lineage tracing now enables direct study of tissue migration in metastatic cancer, but current reconstruction algorithms are limited by a reliance on strong parsimony assumptions and pre-estimated cell-lineage phylogenies. Here, we introduce a probabilistic modeling and inference framework, called BEAM (Bayesian Evolutionary Analysis of Metastasis), that provides richer information about complex metastatic histories. Based on the flexible BEAST 2 platform for Bayesian phylogenetics, BEAM infers a full posterior distribution over cell-lineage phylogenies and tissue migration graphs, complete with timing information. We show using simulated data that BEAM reliably outperforms current methods for inference of tissue migration graphs, especially for more complex histories. We then apply BEAM to public data sets for lung and prostate cancer, finding support for distinct modes of migration across clones and reseeding of primary tumors. Overall, BEAM serves as a powerful framework for revealing the modes, timing, and directionality of tissue migration in metastatic cancer.

cancer biology↗

bioRxiv2025

Targeting PI3K Signaling for Broad Inhibition of β-Coronavirus Infection

The phosphatidylinositol 3-kinase (PI3K) signaling pathway plays a central role in regulating key cellular processes such as survival, metabolism, and immune responses. Aberrant activation of this pathway is associated with tumorigenesis, and several PI3K inhibitors have been developed as anticancer agents. Emerging evidence suggests that viruses, including {beta}-coronaviruses, have evolved mechanisms to exploit host PI3K signaling for their replication and immune evasion. In this study, we evaluated the antiviral efficacy of a panel of PI3K inhibitors against {beta}-coronaviruses, including mouse hepatitis virus (MHV), human OC43 (HuCoV-OC43) and four major SARS-CoV-2 variants using both cell line and organoid models. Our findings reveal that these compounds exhibit low micromolar potency in inhibiting viral replication. Notably, the inhibitor C20 (PWT33597) demonstrated broad-spectrum activity against multiple {beta}-coronaviruses, including SARS-CoV-2, MHV, and HuCoV-OC43, in conventional cell lines as well as in air-liquid interface (ALI)-cultured, differentiated primary human nasal and bronchial epithelial cells. Given that cytokine storm is a major contributor to SARS-CoV-2-related multiorgan failure and mortality, we further explored the impact of PI3K inhibition on host inflammatory responses. We found that MHV infection markedly increased cytokine expression in 17CL-1 fibroblasts and RAW264.7 macrophages. Interestingly, treatment with C20 further amplified cytokine production in this context, suggesting complex immunomodulatory effects that warrant further investigation. Together, our findings support the therapeutic potential of repurposing PI3K inhibitors as broad-spectrum antivirals. These compounds not only suppress viral replication but may also influence host immune responses, providing a promising avenue for intervention against current and emerging coronavirus threats.

microbiology↗

bioRxiv2025

Multi-omics analyses reveal the effect of DNA methylation on iridoid glycosides biosynthesis in Rehmannia glutinosa

Rehmannia glutinosa roots produce a group of lipophilic bioactive components known as iridoid glycosides. However, the molecular mechanisms by which DNA methylation regulates the biosynthesis of iridoid glycosides in R. glutinosa remain unknown. Herein, the development of R. glutinosa roots and the content of iridoid glycosides in the Wenxian region were significantly higher than those in Xinxiang. Low methylation level contributed to the accumulation of iridoid glycosides and the expression of related enzyme genes. Demethylation promoted both roots growth and development, as well as the accumulation of iridoid glycosides. Up-regulated RgALDH13, RgHDR1, RgG10H4, RgDXR1, RgG10H3, and RgUPD1, along with transcription factors (TFs), form the regulatory network for the biosynthesis of iridoid glycosides. Furthermore, the primary active region of the RgG10H4 promoter is located in the -164 bp region, where the RgMYB2 protein specifically binds to the TAACCA motif in the RgG10H4 promoter. Collectively, low levels of DNA methylation enhance the expression of core genes, followed by inducing the accumulation of iridoid glycosides, which suggests that RgMYB2-RgG10H4 plays a positive role in this process. These findings will contribute to a deeper understanding of the role of DNA methylation in the accumulation of iridoid glycosides. HighlightLow levels of DNA methylation contribute to the accumulation of iridoid glycosides and the expression of key enzyme genes in R. glutinosa.

molecular biology↗

bioRxiv2025

Multi ligand sensing by a bacterial histidine kinase through inhibition of dimerization

Two component systems (TCS) mediate bacterial signal transduction in response to specific environmental conditions. The two components are the sensor kinase (SK), which senses the signal and autophosphorylates on a histidine residue, and a response regulator (RR), which is phosphorylated by the kinase and modifies gene expression. Despite intensive study, the mechanisms of signal sensing by sensor kinases are incompletely defined and the mechanisms by which SKs can sense multiple ligands are unclear. Mycobacterium tuberculosis PdtaS/PdtaR is a soluble TCS pair that participates in the Rip1 signal transduction cascade to control virulence by responding to copper and nitric oxide (NO). In contrast to paradigmatic ligand activated SKs, PdtaS is constitutively active without ligand and directly inhibited by Cu or NO, yet it remains unclear how such chemically diverse ligands are sensed. Here we show that PdtaS is a dimeric kinase that constitutively autophosphorylates in trans. Cu and NO both inhibit PdtaS phosphorylation by inhibiting dimerization. Phylogenetic analysis of the PdtaS family reveals conservation of the GAF/PAS dimer interface rather than the ligand binding pockets and mutations in the GAF dimer interface that alter dimerization impair multi-ligand sensing both in vitro and in M. tuberculosis cells. These results indicate that a single bacterial kinase can sense chemically diverse inputs through inhibition of dimerization dependent phosphorylation.

microbiology↗

bioRxiv2025

Epithelial-mesenchymal transition couples with cell cycle arrest at various stages

Numerous computational approaches have been developed to infer cell state transition trajectories from snapshot single-cell data. Most approaches first require projecting high-dimensional data onto a low-dimensional representation; however, this can distort the dynamics of the system. Using epithelial-to-mesenchymal transition (EMT) as a test system, we show that both biology-guided low-dimensional representations and trajectory simulations in high-dimensional state space, not representations obtained with brute force dimensionality-reduction methods, reveal two broad paths of TGF-{beta} induced EMT. The paths arise from the coupling between cell cycle and EMT at either the G1 or G2/M phase, contributing to cell-cycle related EMT heterogeneity. Subsequent multi-plex immunostaining studies confirmed the multiple predicted paths at the protein level. The present study highlights the heterogeneity of EMT paths, emphasizes that caution should be taken when inferring transition dynamics from snapshot single-cell data in two- or three-dimensional representations, and shows that incorporating dynamical information can improve prediction accuracy.

systems biology↗

bioRxiv2025

Optimizing Metagenome Analysis for Early Detection of Colorectal Cancer: Benchmarking Bioinformatics Approaches and Advancing Cross-Cohort Prediction

Colorectal cancer (CRC) continues to be a major global public health challenge. Extensive research has underscored the critical role of the gut microbiome for diagnostics of CRC. However, early-stage prediction of CRC, particularly at the precancerous adenomas (ADA) stage, remains challenging due to the instability of microbial features across cohorts. In this study, we conducted a systematic analysis of 2,053 gut metagenomes from 14 globally-sampled public cohorts and a newly recruited cohort. Despite substantial regional and cohort-level heterogeneity in microbiome composition, we elucidated that the consistent dynamic patterns of microbial signatures are the fundamental for CRC detection. These patterns enabled robust performance in both inter-cohort and independent validations using an optimized bioinformatics framework. In contrast, such basis was lacking in ADA-associated microbial markers, limiting the generalizability of early detection models. To address this, we developed an instance-based transfer learning approach, Meta-iTL, which effectively leveraged knowledge from existing datasets to detect CRC risk at the ADA stage in the newly recruited cohort. Thus, Meta-iTL overcomes challenges posed by cohort-specific variability and limited data availability, advances the application of non-invasive approaches for the early screening and prevention of CRC.

bioinformatics↗

bioRxiv2025

LivecellX: A Deep-learning-based, Single-Cell Object-Oriented Framework for Quantitative Analysis in Live-Cell Imaging

Live-cell imaging uniquely captures single-cell dynamics in space and time, but robust analysis is limited by segmentation and tracking errors that accumulate across frames. We present LivecellX, a deep-learning-based pipeline that integrates instance-level segmentation error correction with trajectory refinement, leveraging temporal context to recover accurate cell tracks. LivecellX also introduces a benchmark dataset with detailed annotations of common error classes, providing a resource for method development and evaluation. Beyond error correction, the framework incorporates modules for classifying biological processes, reconstructing cell lineages, and analyzing dynamic behaviors. Users can interact with the system programmatically or through a Napari-based graphical interface, enabling flexible integration into diverse workflows. By coupling error-aware correction with comprehensive lineage and dynamics analysis, LivecellX establishes an open, extensible platform that advances the accuracy and scalability of live-cell imaging studies.

biophysics↗