Biology subjects

Jiang, C.

Publications and source records attributed to Jiang, C..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Simultaneous detection and estimation in olfactory sensing

The mammalian olfactory system shows an exceptional ability for rapid and accurate decoding of both the identity and concentration of odorants. Previous works have used the theory of compressed sensing to elucidate the algorithmic basis for this capability: decoding odor information from the responses of a restricted repertoire of receptors is possible because only a few relevant odorants are present in any given sensory scene. However, existing circuit models for olfactory decoding still cannot contend with the complexity of naturalistic olfactory scenes; they are limited to detection of a handful of odorants. Here, we propose a model for olfactory compressed sensing inspired by simultaneous localization and mapping algorithms in navigation, in which the set of present odors and their concentrations are inferred separately. We implement this split inference in a biologically-plausible recurrent circuit by introducing separate dynamics matched to the distinct nature of presence and concentration, and drawing on the framework of Mirrored Langevin Dynamics. This model can accurately infer presence and concentration at scale. Moreover, its circuit structure can be mapped onto the primary cell types of the olfactory bulb, giving a possible normative account for functional differences between mitral and tufted cells. Our approach offers a general path towards circuit algorithms for probabilistic inference--in olfactory sensing and beyond--that both perform well in naturalistic environments and make experimentally-testable predictions for neural response dynamics.

neuroscience↗

bioRxiv2025

LRP8 is a Receptor for Yellow Fever Virus

Yellow fever virus (YFV), an arbovirus causing substantial human morbidity and mortality, was the first human virus discovered over a century ago. The live-attenuated 17D vaccine is among the most successful vaccines in medicine. Despite the importance of YFV, its receptor has remained unknown. Here, we performed a barcoded, genome-wide human ORF library screen and identified LRP8 (also named APOER2) as a receptor for YFV. We show that LRP8 expression specifically boosts YFV infection in cell lines by promoting entry. AAV-mediated expression of human LRP8 in mouse liver aggravates infection and pathology. LRP8 knockdown abolishes YFV infection in brain cells, primary human hepatocytes, and notably in mosquitoes. Biochemically, LRP8 directly interacts with YFV particles via the viral envelope protein. This function of LRP8 is conserved across species, particularly in mosquitoes and primates. A soluble LRP8 decoy protein can block YFV infection in vitro and in mice, providing a potential therapeutic or prophylactic strategy. Our findings provide groundwork for understanding YFV entry, tropism, and pathogenesis, and may enable development of novel therapeutics to treat YFV infection.

microbiology↗

bioRxiv2025

An ultra-long acting insulin enables glucose-synchronised release

Delayed and weak glucose-responsive kinetics, coupled with short plasma exposure and complicated formulation, remain major obstacles to the clinical translation of glucose-responsive insulin formulations. Here we report a rapidly absorbable, ultra-long acting, and glucose-responsive insulin analogue via engineering recombinant human insulin with two phenylboronic acids. The insulin analogue has a long circulating half-life (t1/2{beta} [~]150 h) due to its unique, unprecedented, glucose-responsive binding to blood circulation-associated proteins. This interaction drives the formation of a unique systemic reservoir that allows synchronised dynamic response in insulin levels to glucose fluctuations. In type 1 diabetic mice and minipigs, a single subcutaneous dose can maintain normoglycaemia for over one week (47.5-fold compared to insulin glargine). After the last injection of four consecutive weekly administrations in mice, the glucose-lowering effect can last 800 hours. Administering this glucose-responsive insulin analogue with a fully automated insulin delivery system can leverage dual closed loops and achieve a glucose time in range of over 85% and a coefficient of variation of approximately 30%, better than the recommended targets for human therapy. Importantly, no toxicity was identified in the study.

bioengineering↗

bioRxiv2025

spCorr: flexible and scalable inference of spatially varying correlation in spatial transcriptomics

Spatial transcriptomics has transformed our ability to explore gene expression within its tissue context, enabling us to dissect subtle yet biologically significant variations in situ. While numerous computational methods have been proposed for detecting Spatially Varying Genes (SVGs) expression by modeling each individual gene separately, much less effort has been devoted to understanding how correlations between genes change across space. Such Spatially Varying Correlations (SVCs) are critical for understanding biological processes such as gene regulatory mechanisms shaped by local tissue environments, yet existing tools remain limited for this task. To address this gap, we present spCorr, a flexible and scalable regression framework for studying SVCs. spCorr provides interpretable, spot-level estimates of gene correlation and detects gene pairs whose correlations vary across locations or between tissue domains. Through extensive simulations and real-data analyses, we show that spCorr achieves high detection power, reliably controls the False Discovery Rate (FDR), and is computationally efficient. Importantly, spCorr reveals biologically meaningful correlation patterns that highlight fine-scale tissue structures, gene module functions, and region-specific interactions, offering new opportunities to study coordinated gene regulation in spatial transcriptomics.

bioinformatics↗

bioRxiv2025

An immunocompetent murine model of virus-elicited liver fibrosis and hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third deadliest cancer worldwide. Over 75% of HCC cases are associated with chronic viral infections. Mechanistic studies and preclinical therapeutic development for virus-associated HCC have been limited by a paucity of small animal models of chronic hepatotropic virus infection that faithfully recapitulate human disease. Here we demonstrate the induction of chronic hepatitis, progressive liver fibrosis, and HCC in immunocompetent laboratory mice upon chronic viral infection with Norway rat hepacivirus (NrHV) - a virus closely related to hepatitis C virus (HCV). NrHV-elicited tumors resemble HCV-associated tumors and liver transcriptome analyses reveal numerous similarities between chronic NrHV and HCV. These findings establish an experimentally tractable, physiologically relevant, and immunocompetent mouse model of virus-elicited progressive liver fibrosis and oncogenesis.

pathology↗

bioRxiv2025

Genome-wide SNVs revealed reticulate evolutionary history of an Eurasia-African songbird radiation

Phylogenomics has the power to uncover complex evolutionary hypothesis across taxa, thereby allowing important glimpses into the evolutionary history, such as incomplete lineage sorting (ILS) and introgression among closely related species. The Saxicola torquatus complex comprises several widely distributed insectivorous bird species across the Palearctic and Afrotropical realms. Nevertheless, the evolutionary history within this complex remains contentious, and previous findings suggest complication caused by cytonuclear discordance. Here, we conducted a phylogenomic analysis of nearly complete taxa within the complex using genome-wide SNVs to investigate potential causes of phylogenetic discordance across genomic regions and elucidate its evolutionary history. We detected multiple reticulate events during their diversification, leading to extensive phylogenetic discordance across the genome. The mitochondrial genome and W chromosome exhibited a consistent gene tree that conflicted with the species tree, with introgression being the most plausible explanation among several hypotheses. The Z chromosome was less affected by ILS/introgression and exhibited relatively stable local phylogenies compared to autosomes. Furthermore, we found that strongly introgressed regions were predominantly concentrated at the ends of smaller chromosomes, and the strength of introgression among species pairs showed consistently positive correlations, likely attributed to shared recombination hotspots in these regions. These findings stress the heterogeneous genomic landscape influenced by intricate evolutionary processes that endorse a reticulate evolution model for global speciation in the S. torquatus complex.

evolutionary biology↗

bioRxiv2025

Leveraging Large Language Models for Redundancy-Aware Pathway Analysis and Deep Biological Interpretation

Extracting coherent, biologically meaningful insights from vast, complex multi-omics data remains challenging. Currently, pathway enrichment analysis serves as a cornerstone for the functional interpretation of such data. However, conventional approaches often suffer from extensive functional redundancy caused by shared molecular components and overlapping pathway definitions across databases. This redundancy can obscure key biological signals and compromise the interpretability of pathway enrichment results. Here, we present MAPA (Functional Module Identification and Annotation for Pathway Analysis Results Using Large Language Models [LLM]), an open-source computational framework that resolves redundancy and enhances pathway analysis result interpretation. MAPA computes functional similarity between pathways using LLM-based text embeddings, enabling comparison across different databases. It constructs pathway similarity networks and identifies functional modules via community detection algorithms. Crucially, MAPA employs LLMs for automated functional annotation, integrating Retrieval-Augmented Generation (RAG) to generate comprehensive and real-time biological summaries and reduce hallucinations. Benchmarking demonstrated MAPAs superior performance: the biotext embedding similarity showed a large effect size (Cliffs {delta} = 0.96) compared with the Jaccard index ({delta} = 0.73), and module identification achieved high accuracy (Adjusted Rand Index [ARI] = 0.95) versus existing methods (ARI = 0.23-0.33). Human expert evaluation confirmed that MAPAs annotations match expert-quality interpretations. Finally, a multi-omics aging case study illustrates that MAPA uncovers coherent functional modules and generates insights extending beyond conventional pathway analyses. Collectively, MAPA represents a significant advance in redundancy-aware pathway analysis, transforming pathway enrichment results from fragmented lists into biologically coherent narratives. By leveraging the capabilities of LLMs, MAPA offers researchers a robust, scalable tool for deriving deep mechanistic insights from complex and vast multi-omics datasets, marking a new direction for AI-driven bioinformatics.

bioinformatics↗

bioRxiv2025

Decoding Lymphangioleiomyomatosis (LAM) Niche Environment via Integrative Analysis of Single Cell Multiomics and Spatial Transcriptomics

Lymphangioleiomyomatosis (LAM) is a rare, progressive lung disease characterized by cystic destruction and metastatic growth of smooth muscle-like cells. Despite advances in understanding its genetic basis, the cellular heterogeneity, regulatory mechanisms, and microenvironmental interactions driving LAM progression remain poorly defined. In this study, we employed an integrative multi-omics approach combining single-cell RNA sequencing (scRNA-seq), single-nucleus ATAC sequencing (snATAC-seq), and high-resolution spatial transcriptomics (Visium, Visium HD, and Xenium) to decode the LAM niche in its native environment. We identified two spatially and functionally distinct LAM subtypes: LAMCORE1 and LAMCORE2. LAMCORE1 cells exhibited a uterine smooth muscle-like phenotype, expressing associated markers (ACTA2, MYH11) and were enriched in MTORC1 signaling and myogenic pathways, supporting a uterine origin. In contrast, LAMCORE2 cells displayed fibroblast-like features, with upregulated extracellular matrix (ECM) remodeling genes (COL1A1, MMP11) and epithelial-to-mesenchymal transition (EMT) pathways, suggesting a role in niche formation. Pseudotime and regulon analyses revealed dynamic transitions between these subtypes, driven by distinct transcriptional networks (e.g., HOX/PBX in LAMCORE1, TWIST/ZEB in LAMCORE2). The presence of the two distinct LAM subtypes was further validated by RNAscope and immunofluorescence microscopy. We identified LAM-associated fibroblasts (LAFs) as activated stromal cells expressing canonical markers (FAP, S100A4, VIM, IGFBP7, SPARC) and localized within LAM lesions. Subpopulations of LAFs, LAF-seed (proximal to LAMCORE1) and LAF-niche (surrounding LAM niches), exhibited unique functional profiles, including ECM deposition, TGF-{beta} signaling, and myofibroblast activation. Regulatory network analysis pinpointed EGR1 as a central hub governing LAF phenotype. Our comprehensive spatial profiling revealed niche structures dominated by LAMCORE1 cells and surrounded by lymphatic endothelial cells (LECs), LAFs, scattered LAMCORE2 cells, macrophages, and reprogrammed alveolar epithelial cells (AT2). ECM remodeling and aberrant organization of cable-like structures (-smooth muscle actin+) of the LAM niches were further validated by second harmonic generation microscopy. These findings provide a high-resolution blueprint of LAM pathogenesis, highlighting the interplay between uterine-derived LAMCORE cells, activated fibroblasts, and the remodeled lung microenvironment. They significantly enhance our understanding of the LAM niche microenvironment and offer insights into potential therapeutic targets and strategies for managing this complex disease.

genomics↗

bioRxiv2025

BrainBeacon: A Cross-Species Foundation Model for Single-cell Resolved Brain Spatial Transcriptomics

The brains functional complexity emerges from spatiotemporal heterogeneity in molecular, cellular, and circuit organization, shaped by evolution, aging, and disease. Single-cell spatial transcriptomics provides a transformative framework to decode this complexity, resolving cell-type-specific expression patterns, region-specific regulation, evolutionary divergence, and disease-associated disruptions. Despite recent advances in spatial transcriptomics, a unified foundation model capturing cross-species, whole-brain cellular architecture remains lacking. Here, we introduce BrainBeacon, the first cross-species foundation model for whole-brain spatial transcriptomics. Trained on 133 million spatially resolved cells covering a total area of 210194 mm2 from human, macaque, marmoset and mouse brains profiled across five major platforms, BrainBeacon integrates gene expression rankings, spatial cellular organization, and evolutionarily conserved genetic relationships into a unified transformer-based architecture. A two-stage training strategy captures both intra-cellular transcriptional dependencies and inter-cellular spatial interactions, yielding biologically grounded and interpretable representations of brain structure. Subsequently, BrainBeacon enables cross-species alignment of brain cell types and anatomical regions, facilitating integrative analyses of conserved and divergent cellular architectures. Moreover, it uncovers spatial regulatory mechanisms of aging by predicting bidirectional perturbation responses between cells and their local niche. Together, BrainBeacon serves as a foundational cross-species model for whole-brain spatial transcriptomics, enabling virtual brain atlas construction, systematic in silico perturbation studies, and mechanistic insights into brain disease pathogenesis.

bioinformatics↗

bioRxiv2025

MetaNet: a scalable and integrated tool for reproducible omics network analysis

Network analysis is a powerful strategy for uncovering complex relationships in high-throughput omics datasets. However, current tools often lack scalability, flexibility, and native support for multi-omics integration, posing significant barriers for exploring complex biological networks. To address these limitations, we developed MetaNet, a high-performance R package designed to construct, visualize, and analyze biological networks from multi-omics datasets. MetaNet supports highly efficient correlation-based network construction, scalable to datasets with over 10,000 features, and includes extensive layout algorithms and visualization options compatible with both static and interactive platforms. It also provides a comprehensive suite of topological and stability metrics for in-depth network characterization. Benchmarking results show that MetaNet outperforms existing R packages by up to 100-fold in computation time and reduces memory usage by up to 50-fold. We demonstrate its utility through two case studies: (1) a longitudinal analysis of microbial co-occurrence networks showing the dynamics of the airborne microbiome, and (2) an integrative exposome-transcriptome network of more than 40,000 features, uncovering distinct regulatory impacts of biological and chemical exposures. MetaNet bridges the gap between network theory and omics application by offering a robust, reproducible, and biologically informed framework for large-scale, interpretable, and integrative network analyses across diverse omics platforms, advancing systems-level understanding in modern life sciences. MetaNet is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/MetaNet).

bioinformatics↗

bioRxiv2025

Epigenetic regulation of cilia stability and kidney by the chromatin remodeling SWI/SNF complexes

Cilia are important subcellular organelles, whose assembly are regulated by master regulator transcription factors including Foxj1 and Rfx proteins. However, whether and how cilia are regulated at epigenetic level remains unknown. We addressed this question by knocking down or knocking out of chromatin remodeling genes. Notably, depletion of multiple components of the switch/sucrose non-fermentable (SWI/SNF) complexes led to ciliopathy-like phenotypes in zebrafish embryos. Specifically, loss of Actl6a, an essential components of the SWI/SNF complexes, resulted in cilia disassembly and cystic kidney, without affectting cilia motility. Omics analyses revealed that in Actl6a-depleted pronephros or embryos, a set of cilia genes--including master regulators foxj1a and rfx2--were downregulated at transcriptional level, chromatin accessibility level and SWI/SNF binding level. Depletion of foxj1a or rfx2 in zebrafish also caused cilia disassembly and cystic kidney. Furthermore, overexpression of foxj1a or rfx2 mRNA partially rescued the cystic kidney and cilia disassembly phenotypes in actl6a mutants. Taken together, our study reveals that the SWI/SNF complexes maintain cilia stability and kidney homeostasis by directly modulating the expression of foxj1a or rfx2.

developmental biology↗

bioRxiv2025

Humans underestimate their body mass in microgravity: evidence from reaching movements during spaceflight

Astronauts consistently exhibit slower movements in microgravity, even during tasks requiring rapid responses. The sensorimotor mechanisms underlying this general slowing remain debated. Two hypotheses have been proposed: either the sensorimotor system adopts a conservative control strategy for safety and postural stability, or the system underestimates body mass due to reduced inputs from proprioceptive receptors. To dissociate these opinions, we studied twelve taikonauts aboard the China Space Station performing a classical hand-reaching task. Compared to their pre-flight performance and to an age-matched control group, participants showed increased movement durations and altered kinematic profiles in microgravity. Model-based analyses of motor control parameters revealed that these changes stemmed from reduced initial force generation in the feedforward control phase followed by compensatory feedback-based corrections. These findings providing support for the body mass underestimation hypothesis while being inconsistent with the strategic slowing hypothesis. Importantly, the sensory estimate of bodily property in microgravity is biased but immune from sensorimotor adaptation, calling for an extension of existing theories of motor learning.

neuroscience↗

bioRxiv2025

TidyMass2: Advancing LC-MS Untargeted Metabolomics Through Metabolite Origin Inference and Metabolic Feature-based Functional Module Analysis

Untargeted metabolomics provides a direct window into biochemical activities but faces critical challenges in determining metabolite origins and interpreting unannotated metabolic features. Here, we present TidyMass2, an enhanced computational framework for Liquid Chromatography-Mass Spectrometry (LC-MS) untargeted metabolomics that addresses these limitations. TidyMass2 introduces three major innovations compared its predecessor, TidyMass: (1) a comprehensive metabolite origin inference capability that traces metabolites to human, microbial, dietary, pharmaceutical, and environmental sources through integration of 11 metabolite databases containing 532,488 metabolites with source information; (2) a metabolic feature-based functional module analysis approach that bypasses the annotation bottleneck by leveraging metabolic network topology to extract biological insights from unannotated metabolic features; and (3) an intuitive graphical interface that makes advanced metabolomics analyses accessible to researchers without programming expertise. Applied to longitudinal urine metabolomics data from human pregnancy, TidyMass2 identified diverse metabolites originating from human, microbiome, and environment, and uncovered 27 dysregulated metabolic modules. It increased the proportion of biologically interpretable metabolic features from 5.8% to 58.8%, revealing coordinated changes in steroid hormone biosynthesis, carbohydrate metabolism, and amino acid processing. By expanding biological interpretation beyond conventionally MS2 spectra-based annotated metabolites, TidyMass2 enables more comprehensive metabolic phenotyping while upholding strict open-source principles of reproducibility, traceability, and transparency.

bioinformatics↗

bioRxiv2025

A Non-canonical Role for Hepatocyte MLKL in Promoting Mitochondrial Dysfunction and Senescence in the Aging Liver

Liver aging is characterized by chronic inflammation and metabolic dysfunction that contributes to the progression of metabolic dysfunction-associated steatotic liver disease (MASLD). Necroptosis, a form of inflammatory cell death, is activated in aging livers, and genetic (Ripk3-/-or Mlkl-/- mice) or pharmacological (RIPK1 inhibitor necrostatin-1s) inhibition of necroptosis attenuates liver inflammation and pathology. However, the cell type-specific role of necroptosis in liver aging remains unclear. Given that MLKL is expressed in hepatocytes, and its expression increases with age, we generated hepatocyte-specific MLKL-overexpressing mice (MLKLHepOE) to determine its role in liver aging. Unexpectedly, MLKL overexpression in hepatocytes did not induce necroptosis, but instead upregulated markers of cellular senescence (cell cycle arrest genes and SASP factors), increased macrophage infiltration, and elevated M1 macrophage marker expression. Electron microscopy and mitochondrial analyses revealed abnormal mitochondrial morphology, elevated oxidative stress, and disrupted mitochondrial dynamics, while lipidomics demonstrated alterations in hepatic lipid metabolites. In agreement with our observations in MLKLHepOE mice, MLKL overexpression in AML12 hepatocytes impaired mitochondrial respiration, increased proinflammatory extracellular vesicle (EV) release, and induced senescence markers, without triggering cell death. Together, these findings reveal a non-lethal, non-necroptotic role for MLKL in promoting hepatocyte senescence and metabolic dysfunction via mitochondrial impairment and EV-mediated inflammation. Our study highlights MLKL as a novel driver of liver inflammaging and a potential therapeutic target for age-related liver disease.

cell biology↗

bioRxiv2025

Bridging Stem Cell Models and Medicine: Integrated 3D Human Cerebral Organoids and Pediatric Serum Reveal Mechanisms and Biomarkers of Anesthetic-Induced Neurotoxicity

BackgroundGeneral anesthetics have been shown to cause acute pathological changes in the developing brain, including neuronal cell death. These effects contribute to long-term cognitive and behavioral impairments observed in animal models. Epidemiological and prospective clinical studies have also reported associations between early-life exposure to anesthesia and neurodevelopmental deficits in children, raising significant concerns about pediatric anesthesia and highlighting the urgent need to understand the molecular mechanisms and biomarkers of anesthetic-induced developmental neurotoxicity (AIDN) using human-relevant models. MethodsThis study employed human induced pluripotent stem cell-derived cerebral organoids that were exposed to varying doses of anesthetic propofol for 1 to 6 hours, including single and repeated exposures. In parallel, serum samples were collected pre- and post-surgery from pediatric patients under 4 years old (n = 10 per group) who underwent either short (<1 hour) or prolonged anesthesia (>3 hours) at the Childrens Hospital of Wisconsin between September 2018 and October 2019. Pathological changes in organoids were assessed using chemical assays, electron microscopy, and western blotting. Brain injury-related proteins in patient serum were quantified via ELISA. Genome-wide expression profiling was conducted on 18,855 mRNAs and 27,427 lncRNAs in organoids and serum using microarray and bioinformatics. ResultsHigher doses, longer duration, and repeated exposures to propofol led to increased apoptosis in organoids. Six-hour exposure induced autophagy as evidenced by LC3-II elevation and led to dysregulation of 553 mRNAs and 792 lncRNAs in organoids, along with their co-expressed signaling networks, affecting pathways related to synaptic integrity, mitochondrial function, and inflammation. ELISA and serum analysis of pediatric patients (<4 years old) exposed to anesthesia (>3 hours) demonstrated elevated brain cell injury-associated proteins (e.g., increased NSE) and brain cell type-specific gene expression changes. Serum findings corroborated organoid data, identifying 21 mRNAs and 12 lncRNAs that were dysregulated in both models and associated with cell injury, neuronal development, inflammation, and learning deficits. ConclusionsThis study represents the first integrative transcriptomic analysis of AIDN using both 3D human cerebral organoids and pediatric patient serum--two complementary, human-relevant models. By identifying consistently dysregulated coding and non-coding RNAs across both platforms, we provide compelling evidence of shared molecular signatures linked to neuronal injury, inflammation, and neurodevelopmental disruption. These findings offer not only mechanistic insights but also lay the groundwork for the development of minimally invasive biomarkers and future therapeutic strategies. This dual-model approach bridges experimental discovery with clinical relevance, advancing the translational understanding of pediatric anesthetic neurotoxicity and supporting efforts to improve long-term neurological outcomes in vulnerable patient populations.

neuroscience↗

bioRxiv2025

Characterization of the gut mycobiome in patients with non-alcoholic fatty liver disease and correlations with serum metabolome

BackgroundThe relationship between the gut mycobiome and non-alcoholic fatty liver disease (NAFLD) is largely unexplored. MethodsIn this study, we used a publicly available shotgun metagenomic sequencing dataset of fecal samples to compare the gut fungal communities of 90 NAFLD patients with those of 90 healthy controls. We examined their correlations with gut bacterial communities, host serum metabolite s, and the interactions of key taxa within these networks. ResultsOur results revealed a remarked dysbiosis in the gut mycobiome of NAFLD patients, cha racterized by a significant increase in four fungal taxa, including Pseudopithomyces sp. c174, Muc or sp. c176, Aspergillus sp. c25, and uncultured Ascochyta c213. Multi-omics analysis showed that the gut mycobiome significantly contributes to the host serum metabolome. Pseudopithomyces s p. c174 was notably associated with some beneficial blood metabolites, such as glycoursodeoxycholic acid and alpha-linolenic acid, whereas the NAFLD-enriched pro-inflammatory phenylacetic acid was linked to Aureobasidium sp. c170 and Basipetospora sp. c193. Network analysis revealed significant alterations in the gut microbiome of NAFLD patients, highlighting the potential roles of Alternaria alternata c42, Penicillium sp. c5, and uncultured Malassezia c303. Moreover, predictive models that integrated both bacterial and fungal taxa improved prediction accuracy, emphasizing the crucial role of gut fungi in NAFLD. ImportanceNon-alcoholic fatty liver disease (NAFLD) is a widespread liver condition with limited treatments. While gut bacteria have been extensively studied in NAFLD, the role of gut fungi (the mycobiome) remains poorly understood. This study fills that gap by characterizing gut fungal communities in NAFLD patients and linking these changes to their blood metabolite profiles. We found that NAFLD patients show distinct fungal imbalances, which correlate with key metabolic alterations in the host. These findings expand our understanding of NAFLD by spotlighting gut fungi as important contributors to disease progression. This insight opens new avenues for potential biomarkers and therapies targeting the gut-liver axis. ConclusionsOur findings provide a comprehensive understanding of the link between the gut mycobiome and NAFLD progression, which is essential for guiding future therapeutic research.

microbiology↗

bioRxiv2025

Ustilaginoidea virens suppresses floral immunity through promoting GA biosynthesis by the effector SCRE9

Rice false smut caused by Ustilaginoidea virens has become one of the most important rice diseases. U. virens specifically infects rice florets through stamen filaments. However, it is mysterious how U. virens inhibits floral immunity for successful colonization. Here, we show that a small cysteine-rich effector SCRE9 is a key virulence factor in U. virens. Expression of SCRE9 in rice not only suppresses immune responses and false smut resistance, but also significantly increases expression of expansin and gibberellin (GA) biosynthetic genes and GA content in young rice panicles. We further demonstrate that OsSIP1 (SCRE9-interacting protein 1) in rice is targeted by SCRE9 and interacts with the OsMADS63 and OsMADS68 transcription factors that transcriptionally regulate the GA synthesis gene GA3ox1. Interestingly, the chloroplast-localized OsSIP1 recruits OsMADS63/68 into the chloroplast. SCRE9 causes OsSIP1 to be translocated into the nucleus, thereby releasing OsMADS63/68 from the chloroplast into the nucleus to promote GA3ox1 transcription. Therefore, SCRE9 promotes GA biosynthesis and expression of expansins in rice florets, thus loosening cell walls and overcoming the physical barrier during U. virens infection. The finding uncovers an unidentified floret infection strategy that offsets the deficiency in cell wall degrading ability in the unique floret colonizing fungus.

plant biology↗

bioRxiv2024

Comprehensive analysis of macrophages infected with avirulent and virulent Mycobacterium tuberculosis uncovers distinct immune mechanisms and anti-TB effect of treated exosomes

Tuberculosis (TB) is now the worlds second deadliest infectious killer after COVID-19. Human-macrophages and their secreted exosomes play important roles in combating invading Mtb. However, the panoramic analysis of the underlying immune mechanism for the infected macrophages, package or secretion mechanism, and anti-TB effect of Mtb treated exosomes remain poorly understood. Here we conducted comprehensive analyses of the macrophages infected with avirulent and virulent Mtb (H37Ra & H37Rv) and their secreted exosomes, collected cells and corresponding exosomes for omics and phenotypic analysis. The results showed that avirulent Mtb stimulated robust immune-responses and apoptosis in macrophages to eliminate the invading Mtb; virulent Mtb induced severe necrosis and immune-escape for survival. The HMGB1 signaling pathway and TNFRSF1B plays important roles in the immune escape of virulent Mtb. Interestingly, our results suggest that macrophages kill Mtb in an IFN-{gamma} independent but simulative way, highlighting the central role of IFN signaling pathway in anti-TB immune response. Moreover, we observed selective transport of host and Mtb RNAs from macrophages to exosomes. Notably, "H37Ra-treated exosomes" displayed a higher anti-TB effect than "H37Rv-treated exosomes" due to some enriched pro-inflammation and immune-escape related Mtb proteins in these two exosomes, respectively. Conclusively, our findings shed new light on the immune mechanism of macrophages in response to Mtb infection, offering a new TB-treatment strategy and some promising vaccine candidates.

bioinformatics↗