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Current therapeutic options remain insufficient for sepsis, driving the search for alternative treatment approaches. Accumulating evidence suggests that resistin (RETN) serves as a crucial factor in sepsis initiation and development. Nevertheless, the specific pathways through which RETN influences sepsis pathophysiology have yet to be elucidated. Single-cell sequencing analysis reveals RETN is primarily expressed in monocytes/macrophages. RETN in macrophages is markedly upregulated in septic patients, exhibiting a marked positive correlation with pro-inflammatory cytokines and disease severity. Bioinformatics analysis and in vitro experiments reveal that knockdown of RETN alleviated macrophage pyroptosis. RNA-Seq analysis and in vitro experiments revealed that the overexpression of RETN markedly upregulates the expression of GBP5 and NLRP3. Further in vivo experiments revealed that RETN knockdown markedly downregulates GBP5 expression, inhibits NLRP3 activation, and mitigates macrophage pyroptosis. This consequently reduces organ (lung, spleen, and heart) damage and improves survival in sepsis. Finally, knocking down GBP5 can reverse the promoting effect of overexpressed RETN on macrophage pyroptosis, organ damage and sepsis lethality. This investigation initially demonstrates that the RETN/GBP5/NLRP3 signaling axis regulates macrophage pyroptosis to aggravate sepsis, providing new potential targets and theoretical support for the research on the pathogenic mechanism of sepsis and clinical treatment.

Fibrosis induced immune exclusion is a hepatocellular carcinoma (HCC) hallmark, underscoring the key role of cancer-associated fibroblasts (CAFs) in immune regulation. Through HCC spatial multi-omics data and integrating pan-cancer scRNA-seq profiles of CAFs under immune checkpoint blockade (ICB) treatment, we characterized a potential crosstalk between capillaries and CAFs mediated by the NOTCH signaling pathway. Specifically, endothelial DLL4-NOTCH3 signaling appears to be associated with matrix-producing CAFs (mCAFs) polarization, leading to extracellular matrix remodeling and the establishment of immune-restrictive niches that hinder T cell infiltration. Perturbation of NOTCH signaling attenuated mCAF differentiation and enhanced T cell infiltration in vitro, and was associated with improved ICB response in both spontaneous and orthotopic HCC mouse models. Collectively, our findings suggest that capillary-mCAFs communication through the NOTCH pathway, particularly NOTCH3 activation, may contribute to fibrosis-driven immune exclusion in HCC. Targeting this axis could provide a promising strategy to alleviate stromal barriers and potentiate immunotherapy efficacy.

Traditional 16S rRNA gene and Internal Transcribed Spacer region amplicon sequencing provides only relative abundance, often leading to biased ecological interpretations. To overcome this limitation, we developed Accu16S/AccuITS, an absolute quantification method for bacterial and fungal amplicons based on synthetic internal spike-in DNA with known copy numbers. By adding internal standards prior to Polymerase Chain Reaction and sequencing, absolute microbial abundances can be calculated using standard curve regression. Accu16S/AccuITS exhibits sensitivity and consistency comparable to quantitative Polymerase Chain Reaction and is applicable to diverse sample types. A single sequencing run simultaneously yields relative abundance, total absolute abundance, and taxon-specific absolute abundance. Case studies across diverse ecosystems demonstrate that absolute quantification provides ecologically and functionally meaningful insights beyond those obtained from relative abundance analyses.

Influenza A virus (IAV) infection has a wide clinical spectrum, from mild illness to life-threatening pneumonia, yet the underlying immune determinants of disease remain poorly defined. Here, we generated a large-scale single-cell transcriptomic atlas from peripheral blood, profiling more than 612,010 cells from 97 individuals, including healthy controls, and patients with mild, severe, or convalescent IAV infection. Our findings uncovered a core immune dichotomy that determines clinical severity: a protective, monocyte-centric antiviral state in mild disease versus a pathological, neutrophil- and myeloid-derived suppressor cell (MDSC)-driven hyperinflammatory state in severe infection. Severe disease was marked by a peripheral hyperinflammatory state, driven by specific monocyte and neutrophil subsets via the S100A8/9/12-TLR4/RAGE signaling axis, and was coupled with the expansion of granulocytic MDSCs that likely contribute to T cell paralysis. In contrast, mild disease was associated with a protective, monocyte-centric response characterized by robust antiviral interferon signaling and enhanced antigen presentation. This functional divergence extends to the adaptive immune system, where mild disease was associated with CD8+ T cells displaying a balance of high cytotoxicity and regulated exhaustion. In severe illness, however, T cells become profoundly dysfunctional, exhibiting signatures of metabolic stress and apoptosis alongside the emergence of pathogenic, pro-inflammatory regulatory T cells. Together, our atlas provides a high-resolution immunological blueprint of human IAV infection, delineates the cellular states and pathways that govern clinical trajectories and offers a critical resource for developing host-directed therapies.

Nontargeted metagenomic surveillance of the poultry enteric virome reveals underrecognized threats to poultry health and productivity in intensive production systems. In South Asia, avian rotavirus A (AvRV-A) and avian orthoreovirus (ARV) are frequently detected in broilers by conventional diagnostics, whereas chicken megrivirus genotype C (ChMeV-C) is often identified through metagenomic surveillance. Often present in both clinical disease and coinfections, these viruses may impair gut function, immune responses, and growth performance, yet their genomic diversity and evolutionary dynamics in poultry remain poorly characterized. Here, we report complete genomes of AvRV-A, ARV, and ChMeV-C strains co-detected via nontargeted metagenomic next-generation sequencing (ntNGS) in a pooled cloacal sample comprising 150 commercial broiler chickens (19 and 33 days old) collected from three commercial farms in Kamrup Rural District, Assam, Northeast India. Despite routine vaccination, all three flocks experienced > 10% mortality, poor weight gain, and postmortem lesions including pale kidneys and hepatomegaly. Phylogenetic analyses revealed segmental clustering in ARV and AvRV-A consistent with reassortment-driven divergence, though not supported by detectable recombination, while ChMeV-C clustered within a distinct C1 sublineage, suggesting intercontinental lineage connectivity and highlighting the need to expand regional genomic baseline data. We also identified nonsynonymous single nucleotide polymorphisms in several key viral proteins, including RNA-dependent RNA polymerases (VP1 of AvRV-A, λB of ARV, and 3D of ChMeV-C), capsid proteins (VP2 and VP7 of AvRV-A, λA and σB of ARV, and VP0 and VP1 of ChMeV-C), and replication-associated nonstructural proteins. These findings expand the genomic baseline for poultry enteric viruses in South Asia, reveal novel polymorphic signatures, and underscore the value of ntNGS-based metagenomic surveillance in virus detection, diversity monitoring, and informing vaccine and biosecurity strategies.

Extracorporeal membrane oxygenation-induced coagulopathy (ECMO-IC) represents a frequent and severe complication, contributing to oxygenator replacement and unfavorable outcomes. Currently, no reliable machine learning (ML) model exists for early identification. This study comprehensively assesses routine clinical characteristics to develop a reliable, accurate, and explainable ML model for estimating ECMO-IC risk and to identify modifiable factors. This study included two center cohorts with 266 patients undergoing ECMO from 2015 to 2024. Feature selection utilized the Boruta algorithm, followed by the implementation of a distinctive ML framework incorporating 12 ML algorithms to establish a consensus prediction model (ECMO-IC index). Model and feature variable assessment employed multiple analytical methods: Bootstrapping and fivefold cross-validation, subgroup and interaction analysis, restricted cubic spline (RCS) regression, and threshold effect analysis. Model interpretation and feature quantification relied on the Shapley Additive Explanations (SHAP) methodology for visualization purposes. Through Boruta algorithm selection, 17 characteristics were identified and incorporated into 12 ML methodologies, generating 105 permutations and an optimal algorithm for identifying ECMO-IC. The ECMO-IC index comprising 9 modifiable or nonmodifiable variables, namely platelet (PLT), lactate, systemic immune-inflammation index (SII), K, total protein (TP), shock index (SI), red blood cell volume distribution width (RDWCV), acute physiology and chronic health evaluation II (APACHE II), and Ca, demonstrated strong diagnostic capabilities, achieving a mean area under the curve (AUC) of 0.815 across derivation (AUC = 0.817) and validation (AUC = 0.813) cohorts, along with notable discriminatory power, model fit, and clinical utility. SHAP elucidates the importance of ranking features (PLT, lactate, K, Ca and APACHE II) and visualises global and individual ECMO-IC risk prediction. RCS regression and threshold effect analysis suggested a nonlinear link between model features (PLT: P for nonlinearity = 0.002, SII: P for nonlinearity = 0.001, K: P for nonlinearity = 0.006, Ca: P for nonlinearity = 0.008, and lactate: P for nonlinearity = 0.004) and ECMO-IC, and generated an inflection point for features (PLT = 95 × 109/L, lactate = 5.7 mmol/L, SII = 200, K = 4.4 mmol/L, TP = 45.6 g/L, SI = 0.8, RDWCV = 14%, APACHE II = 15, Ca = 1.03 mmol/L). To provide a more flexible predictive tool, the ECMO-IC model was constructed using a free, publicly available web-based calculator ( https://genglongliu.shinyapps.io/DynNomapp/ ). An optimised explainable ML model (ECMO-IC index) incorporating several modifiable parameters was established and internally validated to deliver an readily available and accurate diagnostic tool for ECMO-IC, with potential applications in ECMO clinical management.

Gut microbiome alterations are increasingly associated with hepatocellular carcinoma (HCC), highlighting the gut-liver axis as a key contributor to tumor progression and prognosis. Taxon-based HCC microbiome studies have shown limited reproducibility because they are affected by database dependency, taxonomic ambiguity, and overlooked ecological interactions. The Two Competing Guilds (TCG) model, based on stable gut microbiome interactions, provides a structurally grounded framework for robust, generalizable biomarkers. Using shotgun metagenomic data from a newly recruited cohort of 120 surgically resectable HCC cases and 76 benign liver tumor controls, we constructed co-abundance networks to identify stably correlated genome pairs and assembled a hepatic cancer-TCG (HCC-TCG) model composed of 142 genomes. Functionally, one Guild had more genes for butyrate production from carbohydrate fermentation while the other Guild was enriched in genes for virulence factors and antibiotic resistance, highlighting its potential proinflammatory roles. Classifiers trained on the abundance profiles of HCC-TCG genomes successfully distinguished HCC from benign liver tumors (area under the receiver operating characteristic, AUROC = 0.70) and from colorectal liver metastases (CRLM) (AUROC = 0.78). In an external validation cohort, the model further discriminated against HCC from intrahepatic cholangiocarcinoma (iCCA) (AUROC  =  0.72), and from healthy controls (AUROC  =  0.79-0.85), demonstrating its broad applicability for tumor stratification across clinical contexts. Moreover, HCC-TCG profiles predicted post-resection recurrence risk and response to adjuvant therapies (AUROC up to 0.83). Importantly, external validation in two independent cohorts of advanced HCC patients treated with PD-1/PD-L1 inhibitors demonstrated consistent predictive performance (AUROC  =  0.64-0.73), confirming the model's generalizability in nonsurgical and immunotherapy contexts. This genome-specific, ecologically structured, and database-independent framework identifies a conserved Guild-based microbiome signature for HCC. Our findings demonstrate that a fixed genome-resolved ecological structure retains transferable discriminatory signal across clinical contexts. The HCC-TCG framework provides a genome-specific, interaction-based foundation for future development of non-invasive microbiome stratification strategies requiring prospective validation.

FluNexus is a versatile platform for the antigenic prediction and visualization of influenza A viruses, including: (i) Online data preprocessing module. (ii) Online antigenic prediction module. (iii) Visualization module for mapping antigenic evolution.

This study investigates the systemic consequences of spinal cord injury (SCI), with a particular focus on alterations in the gut microbiome and multi-organ transcriptomic responses. We identify a rapid and severe disruption of the gut microbiota-termed "microbiome shock"-that emerges within 12 h post-SCI and persists before gradually resolving by 5 days post-injury. To support further research in this field, we established an open-access resource, the Spinal Cord Injury Gut Microbiome and Multi-Organ Gene Expression Atlas (SCIGAMA).

Develop a novel strategy for exploring a dual-functional microbial synthetic community. Invent the SynCom ARC, which achieves aflatoxin control and rhizobia nodulation induction coupling in peanut. SynCom ARC inhibits A. flavus growth and reduces peanut aflatoxin levels by 85.6% in 4 year field trials. SynCom ARC enhances peanut nodulation and nitrogenase activity, retains active nodules at harvest, and boosts yield without super nodulation penalty in 325 sites of 19 provinces. SynCom ARC inhibits multiple targets in A. flavus, recruits and activates nodulation and nitrogen fixation in rhizobia and peanut, and improves photosynthesis and carbon supply for aflatoxin prevention and nodulation induction, balancing yield increase.

We utilized single-cell RNA sequencing (scRNA-seq) to investigate cellular heterogeneity and signaling networks in aortic dissection (AD) tissues compared to adjacent normal tissues. The analysis identified five smooth muscle cell (SMC) subtypes, with SMC2 linked to fibrosis and SMC3 associated with inflammation. Thrombus-positive AD samples showed upregulated angiopoietin-like 4 (ANGPTL4) and increased M2 macrophages, indicating an immunosuppressive microenvironment. Cell-cell communication analysis revealed a shift in vascular endothelial growth factor A (VEGFA) signaling from SMCs to fibroblasts, disrupting vascular homeostasis. In vitro experiments confirmed SMC2-induced endothelial-to-mesenchymal transition and SMC3-driven inflammatory responses via mitogen-activated protein kinase (MAPK) pathways. Immunofluorescence validated elevated insulin-like growth factor binding protein 2 (IGFBP2), procollagen-lysine 2-oxoglutarate 5-dioxygenase 2 (PLOD2), and VEGFA in AD tissues, supporting their roles in matrix remodeling and angiogenesis. These findings highlight SMC phenotypic switching and altered VEGFA signaling as key drivers of AD, proposing novel therapeutic targets to restore vascular integrity.

Gemcitabine resistance poses a critical barrier to improving survival in pancreatic cancer, yet the microbial drivers remain elusive. By integrating 16S rRNA amplicon sequencing with large-scale culturomics across 114 clinical samples, we identified Enterobacter hormaechei as a key intratumoral pathogen. We demonstrate that E. hormaechei confers resistance by enzymatically converting the drug to its inactive metabolite dFdU via a unique long-isoform cytidine deaminase encoded by cdd L. Kinetic analysis revealed exceptional catalytic efficiency (K m = 0.22 mM, k cat = 194.05 s-1), and genetic ablation of cdd L fully restored drug sensitivity. In vivo, antibiotic co-treatment eliminated intratumoral bacteria and potentiated gemcitabine efficacy, enabling a 50% dosage reduction without comprising therapeutic outcome. Pan-cancer analysis further confirmed the broad prevalence of Enterobacter across multiple solid tumor types. These findings elucidate a cdd L-mediated mechanism of chemoresistance and identify intratumoral E. hormaechei as a tractable therapeutic target for optimizing gemcitabine-based regimens and improving patient outcomes.

The detection of circulating tumor cells (CTCs) through liquid biopsy offers a non-invasive approach for accurately monitoring cancer dissemination and evaluating therapeutic efficiency. However, their rarity and heterogeneity limit conventional tumor antigen labelling-based methods in identifying and tracing CTCs. Here, we developed a novel metric, termed chromatin unwinding state (CUS), which leverages activated transcriptional regions related to cell-identity processes from single-cell transcriptomic data while overcoming technical variances. Using CUS features, we trained attention-based neural network models, panCTC, to in situ identify and lineage trace rare single CTCs directly from 5 mL of peripheral blood mononuclear cells scRNA-seq without enrichment. We benchmarked panCTC on various in silico-simulated, public, and in-house sequenced data, demonstrating its robustness across sample types and platforms. PanCTC could provide real-time scRNA-seq profiles of fresh CTCs, supporting early cancer detection and targeted anti-metastatic therapy.

Glucocorticoid-induced myopathy is characterized by progressive muscle atrophy and impaired regeneration, yet effective microbiota-oriented interventions for preserving muscle homeostasis remain largely unexplored. Here, we demonstrate that dietary chondroitin sulfate (DCS) restores muscle mass and function through a microbiota-dependent gut-muscle metabolic axis. DCS failed to confer protection in germ-free or antibiotic-treated mice, establishing gut microbiota as a prerequisite for its efficacy. Microbiota transplantation and mono-colonization experiments identified Lactobacillus johnsonii Z-RW as a functionally relevant mediator capable of recapitulating muscle protection under controlled microbial conditions. Integrated metagenomic, metabolomic, and proteomic analyses revealed coordinated reprogramming of intestinal sugar utilization and bile acid metabolism following DCS administration. Notably, DCS promoted bile acid deconjugation and enrichment of secondary bile acids, coinciding with restoration of muscle regenerative and energetic programs, including upregulation of NMRK2, PAX7, and SIRT1. Metabolite supplementation further implicated bile acids as candidate mediators linking microbial metabolism to muscle phenotypes. To quantitatively integrate these shifts, we introduce the sugar-bile acid ratio as a systems-level descriptor of microbiota-driven metabolic remodeling. Our findings delineate a microbiota-dependent metabolic framework through which a functional polysaccharide reshapes intestinal biochemistry to influence distal muscle physiology. This work highlights bile acid-associated signaling as a central relay within the gut-muscle axis and provides a conceptual foundation for microbiota-targeted strategies to mitigate muscle wasting.

Immune checkpoint inhibitors (ICIs) have shown promising antitumor efficacy in certain types of solid tumors. However, the efficacy of ICIs remains unsatisfactory owing to the dysregulation of signaling pathways in local tumor tissues. Here, we reveal that diacylglycerol kinase α (DGKα)-derived phosphatidic acid (PA) directly binds to nuclear factor-κB (NF-κB) and enhances the transcriptional activity of NF-κB to increase the expression of programmed cell death 1-ligand 1 (PD-L1) and facilitate the immune evasion of tumor cells and orchestrate immune microenvironment. Inhibition of DGKα activity decreases the intratumoral PD-L1 level and induces cytotoxic T lymphocytes (CTLs) infiltration and resultantly enhances the antitumor efficacy of ICIs. Plasma PA can function as a biomarker to evaluate the efficacy of ICIs in gastrointestinal cancers. Overall, our results identify the DGKα/PA axis as a metabolic driver of immune evasion and CTLs exclusion, representing a promising target to enhance ICIs' efficacy in gastrointestinal cancer treatments.

The gut microbiota derived from Parkinson's disease patients, enriched in Bacteroides fragilis (B. fragilis) and Phocaeicola vulgatus (P. vulgatus), may promote the elevations of deoxycholic acid, iso-deoxycholic acid, and ursodeoxycholic acid levels in the systemic circulation. The increased serum bile acids, in turn, contribute to the endothelial cell death and pericyte injury possibly through activating interferon alpha response and TGF-β signaling pathways at the blood-brain barrier in the midbrain, ultimately leading to the neurodegeneration and motor deficits in the germ-free mice.

Deciphering how plant-microbiota interactions achieve beneficial outcomes for crops will provide innovative strategies for sustainable agriculture. Here, we dissected rice-microbiota dynamics using a tailored gnotobiotic cultivation system that models the semiaquatic environment in a paddy field. Inoculation with native soil microbiota resulted in root-growth-promotion (RGP) and root-growth-inhibition (RGI) phenomena in different cultivars. This preference persisted in a simplified synthetic community and individual bacterial strains, indicating that cultivar-specific growth promotion is an intrinsic property of microbial inocula. Though stochastic process dominated the assembly of root microbiome in gnotobiotic cultivation, absolute quantification revealed that imbalance of detrimental and beneficial bacterial loads in roots correlated with RGP or RGI outcomes in different rice cultivars. From the host perspective, genetic screening identified that receptor-like kinase mutants, including OsFLS2 (FLAGELLIN-SENSITIVE 2), inverted microbiota functionality, converting RGP to RGI. In particular, over 4534 rice genes responded to microbiota inoculation and 46.1% of them were reprogrammed in osfls2 mutants, demonstrating the prominent regulatory role of OsFLS2 in rice-microbiota signaling. On the basis of these results, we propose that the rice-microbiota relationships are gated by cultivar-specific preferences of the bacterial microbiota and host immune receptor kinase, which provides a useful framework for crop microbiome engineering in the future.

A clinical study reported that Abelmoschus manihot (L.) Medic (A. manihot), in the form of Huangkui capsule (HKC), combined with irbesartan (IRB) is an effective therapy for patients with diabetic kidney disease (DKD). The bioactive components of HKC are total flavones extracted from A. manihot (TFA). To explore the pharmaceutical molecular mechanisms underlying the efficacy of A. manihot in the treatment of DKD, we have combined SpaTial Enhanced REsolution Omics-sequencing (at 0.25 μm resolution) with single-cell full-length RNA sequencing. We employed the db/db mouse model of type 2 diabetes and DKD. These experimental methods generated the first single-cell resolution pharmacopathological spatial atlas in kidneys of db/db mice that were treated with TFA or IRB. TFA exhibited therapeutic effects on DKD comparable to those of TFA combined with IRB. Following genome-wide gene screening and molecular docking simulation, we have identified the key renal receptors (Itga3, Itga5, Tgfbr1, etc.) and regulators (Jun, Junb, Stat1, etc.) underlying the therapeutic action of TFA in DKD. This study provides novel insights into the pharmaceutical mechanisms of A. manihot in the treatment of DKD.