Whether Lepidoptera harbor a conserved core gut microbiome has long remained contentious. Through large-scale microbiome profiling of folivorous larvae, their host plants, and associated soils across three climatically distinct regions of China, we identify two soil-derived generalist bacteria, Ralstonia insidiosa and Delftia sp., that colonize 97.92% of larval species examined, attaining mean relative abundances exceeding 47%, with the soil microbial reservoir as their principal source. Strikingly, these two taxa exhibit strong mutual exclusion within the larval gut yet govern host development through diametrically opposed metabolic strategies: R. insidiosa promotes larval weight gain, whereas Delftia sp. suppresses growth. This functional bifurcation, in which two widespread generalists exert opposite phenotypic effects, represents a previously undescribed phenomenon in insect-microbe symbiosis. Our findings provide broad evidence that soil microbial reservoirs can shape aboveground herbivore fitness via horizontally acquired bacteria, offering mechanistic insights for microbiome-based ecological management.
Biomarkers play a pivotal role in contemporary medical research, particularly in the early diagnosis of diseases and personalized treatment. Although previous studies have systematically reviewed markers across various disease domains, an integrated framework that connects major physiological systems, encompasses multiple organs, and spans a broad spectrum of diseases is still lacking. In the context of modern health challenges, marked by the high prevalence of chronic diseases and widespread comorbidities, establishing a panoramic biomarker navigation system is imperative. This review offers the initial comprehensive elucidation of the biomarker interaction networks across diverse systems, organs, and diseases, and delineates the operational framework for establishing a "biomarker navigation system". The core contribution of this work is a transition from 'knowledge enumeration' to a more integrated, systems-level approach. This provides new perspectives in systems biology for understanding the shared pathological foundations of comorbidities. Furthermore, it provides a theoretical basis for a reorientation in medicine, from a focus on 'treating existing diseases' to 'preventive interventions' and from 'single-disease management' to a comprehensive, systems-based approach. To facilitate the exploration and application of this system-organ-disease-biomarker network, we developed the "Human Biomarker Navigator" web platform (http://www.hbiomarker.com), which allows researchers and clinicians to efficiently retrieve the functional characteristics and clinical significance of diverse biomarkers across different disease contexts.
Abstract Gestational diabetes mellitus (GDM) reflects metabolic dysregulation that becomes clinically apparent during pregnancy and shares key pathophysiological features with broader forms of diabetes. Gut microbiome‐host metabolic interactions may contribute to this process, yet their role in early pregnancy remains incompletely understood. In this prospective nested case‐control study within the Tongji‐Huaxi‐Shuangliu Birth Cohort, 784 pregnant women, including 222 who developed GDM, underwent first‐trimester gut metagenomic and plasma lipidomic profiling. Cross‐omics analyses were performed to identify microbiome‐lipid associations and potential mediation patterns. Women who later developed GDM showed reduced gut microbial diversity and altered microbial profiles in early pregnancy. We identified 26 microbial species associated with GDM risk, with seven species, including Ruminococcus bicirculans ( R. bicirculans ), showing concordant associations in external type 2 diabetes populations. Microbial pathways related to fatty acid and lipid biosynthesis were enriched in women at higher risk. Plasma lipidomics revealed widespread alterations, particularly among glycosphingolipid‐related metabolites. Integrated analyses suggested that lipidomic variation statistically accounted for part of the microbiome‐GDM association. A class‐level dihexosylceramide feature, DHC 24:1, consistent with lactosylceramide‐related metabolites, emerged as a potential mediator and was prioritized for exploratory follow‐up. Experimental analyses provided functional support for a microbiome‐lipid‐host interaction axis. R. bicirculans promoted lactosylceramide 24:1 production in vitro , bacterial colonization and metabolite administration improved insulin tolerance in vivo , and lactosylceramide 24:1 modulated insulin‐stimulated AKT signaling dynamics in hepatocytes. These findings identify a gut microbiome‐lipid axis associated with metabolic dysregulation in pregnancy and suggest a potential mechanism linking microbial metabolism to host insulin signaling.
Limosilactobacillus reuteri Y15 (LRY15)-derived polyunsaturated fatty acids (PUFAs) restore follicular cell-cell junctions and fertility. LRY15, isolated from the intestine of alginate oligosaccharides (AOS)-dosed mice, improves ovarian function and fertility in cisplatin-induced subfertile mice. LRY15 produces PUFAs, whereas deletion of holo-acyl carrier protein synthase (AcpS) in LRY15 reduces PUFA synthesis and weakens its protective effects. Docosahexaenoic acid (DHA) supplementation recapitulates LRY15-mediated improvements in follicle and embryo development. Stereo-seq analysis reveals that LRY15 restores ovarian cell-cell junctions, suggesting junction remodeling as an important cellular process associated with LRY15 treatment. The Fads2-deficient mouse model further validates that LRY15-derived PUFAs restore follicular junction integrity and improve fertility.
Integrated single-cell RNA sequencing, spectral flow cytometry, immune-receptor repertoire profiling, and plasma proteomics revealed coordinated peripheral immune remodeling during acute scrub typhus. Patients exhibited expansion of cytotoxic, clonally amplified CD8+ T cells with exhaustion-associated features, depletion and apoptosis of CD4+ T cells, B-cell contraction with enhanced plasmablast/plasma-cell differentiation, and inflammatory myeloid remodeling. CD14+CXCR6+ and CD14+GNLY+ myeloid-cell frequencies positively correlated with bacterial burden, defining prominent immune features of acute Orientia tsutsugamushi infection.
Early-life gut microbiome assembly is a pivotal determinant of lifelong health; however, the integrated frameworks governing this process across developmental milestones remain insufficiently defined. This review establishes a multidimensional framework by delineating the crosstalk between the gut microbiome and the host throughout the preconception, prenatal, postpartum, and early childhood stages. We first highlight the emerging paradigm of biparental microbial contributions during the preconception period, detailing how paternal and maternal niches jointly prime offspring development. Moving into pregnancy, we examine the maternal reservoir, integrating the role of gut microbiota-derived metabolites across multiple trimesters in prenatal priming and vertical transmission. For the postpartum period, we discuss the development of the multikingdom gut microbiome and address the impacts of delivery modes and clinical interventions. Here, we articulate a critical knowledge gap: the discrepancy between taxonomic "catch-up" and true functional restoration, particularly in vulnerable cohorts such as preterm infants. Furthermore, we propose a "developmental synchronization" model within the maternal-infant-microbiome continuum. This model posits that early-life "windows of opportunity" are defined by the obligate temporal coupling of host physiological maturation with stage-specific microbial metabolic signals. From a translational perspective, we discuss how this framework informs the development of precision interventions, such as stage-specific probiotics, prebiotics, or metabolic modulators. These therapies aim to restore not only the microbial composition but also the synchronized functional dialog between the microbiome and host development. By mapping the "microbiota-metabolite-host target-physiological phenotype" network, we provide a systematic roadmap for precision-targeted interventions during the first 1000 days of life.
Abstract Plants are best understood as evolutionary holobionts, in which the host and its associated microbiomes operate as an integrated unit to influence growth, health, and stress resilience. This comprehensive review synthesizes the most current knowledge of plant‐associated microbiomes across key ecological compartments, including the rhizosphere, endosphere, phyllosphere, and seeds, highlighting their assembly drivers, functional mechanisms, and translational potential. We dissect the molecular foundations of rhizobial and arbuscular mycorrhizal (AM) symbioses, the plant‐AM fungus‐bacterium continuum, alongside emerging concepts including the aerial root mucilagesphere, phyllosphere homeostasis, and the pathobiome. We further explore host genetic, metabolic, and environmental determinants of microbiome assembly, and present cutting‐edge methodologies ranging from quantitative profiling to artificial intelligence‐driven synthetic community design. Finally, we outline a strategic blueprint for harnessing standardized synthetic microbiomes and precision microbiome engineering to advance sustainable agriculture. This integrative framework bridges fundamental ecology with practical applications, delineating a path toward climate‐resilient crop production.
The gut microbiota has emerged as a metabolically active endocrine-like organ with a crucial role in coronary artery disease (CAD), yet its contribution to adverse clinical outcomes remains incompletely understood. In this prospective cohort of 319 participants, we integrated metagenomic and metabolomic profiling with longitudinal follow-up over a median of 1.85 years. Fecal microbiota transplantation from patients with CAD transmitted susceptibility to atherosclerosis in antibiotic-treated ApoE -/- mice, accompanied by microbiota-induced vascular inflammation mediated through LPS-TLR4 signaling. Gut microbiota-derived aromatic amino acid metabolism was associated with thrombotic risk and major adverse cardiac events (MACE). Machine learning identified gut microbial features that improved prospective prediction of MACE. Together, these findings implicate the gut microbiome in CAD progression and support the development of microbiome-based strategies for cardiovascular risk prediction and prevention.
The human microbiota contributes to breast cancer pathogenesis and treatment response through interactions involving the oral, gut, and breast microbial niches. (Left) Multi-site dysbiosis is characterized by altered microbial composition and abundance across different anatomical sites. (Middle) Microbial components and metabolites influence tumor progression and the tumor microenvironment through mechanisms including immune, metabolic, and inflammatory regulation. (Right) The microbiota and their metabolites further affect therapeutic efficacy and toxicity in immunotherapy, chemotherapy, endocrine therapy, and human epidermal growth factor receptor 2 (HER2)-targeted therapy, highlighting the translational potential of microbiota-targeted strategies in breast cancer.
Climate change demands accelerated plant adaptation and de novo domestication. Yet current enviromics focuses disproportionately on external environments, neglecting internal dynamics-gene expression, metabolic flux, and signal transduction-within predictive envirotyping frameworks. This gap constrains plant-environment adaptation research and crop improvement. Integrating multi-scale envirotyping with plant-environment interaction networks could catalyze a paradigm shift from empirical selection to mechanism-informed design breeding. Four challenges remain: (1) constructing adaptive multi-dimensional networks, (2) engineering transgenerational epigenetic reprogramming, (3) scaling domestication pipelines, and (4) predicting adaptive trajectories. Future efforts should converge on five domains: high-throughput microprobe envirotyping arrays, spatiotemporally resolved multi-omics, decoding epigenetic memory carriers, artificial intelligence (AI)-guided genome design, and phenotype prediction models. Ultimately, advancing from multi-omics dissection and mechanistic interpretation to targeted de novo design will enable the precise engineering of crop adaptive responses to environmental change.
Decidualization deficiency is a hallmark pathology of unexplained recurrent spontaneous abortion (URSA), but the undefined molecular drivers hinder the development of effective therapies. Hyperoside, a bioactive flavonoid from Hypericum perforatum, exhibits therapeutic potential against URSA, yet its underlying mechanism of action remains unknown. In this study, we employed an integrated multi-omics approach coupled with a multi-dimensional validation framework that spanned URSA patient decidual tissues, in vivo mouse models, and in vitro telomerase-immortalized human endometrial stromal cell (T-hESC) decidualization system, to systematically investigate hyperoside's mechanism in URSA, with a focus on R-loop-driven endometrial stromal cell senescence. We found that hyperoside dose-dependently reduced embryo resorption and rescued decidualization deficiency by preventing stromal cell senescence. Mechanistically, hyperoside effectively alleviated aberrant intracellular R-loop accumulation, thereby suppressing excessive activation of the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway which contributes to the initiation of the cellular senescence program. Further target identification and validation experiments confirmed that DExH-box helicase 9 (DHX9) was the functional molecular target of hyperoside, with the Thr419 residue serving as the critical binding site. Functional validation revealed that DHX9 knockdown or introduction of the T419A point mutation markedly attenuated the anti-senescence and pro-decidualization effects of hyperoside. In vivo experiments further confirmed that uterine-specific knockdown of DHX9 reduced hyperoside's protective effects against R-loop accumulation, cGAS-STING pathway activation, and embryo loss. Collectively, these findings demonstrate that hyperoside alleviates stromal cell senescence and decidualization deficiency in URSA through DHX9-dependent resolution of R-loops and subsequent suppression of cGAS-STING-associated senescence signaling. More broadly, this work identifies R-loop-mediated genomic stress as a previously underappreciated contributor to URSA-associated decidual dysfunction and provides a mechanistic basis for the protective effects of hyperoside through DHX9-dependent R-loop homeostasis.
Current therapies for allergic rhinitis primarily provide symptomatic relief but fail to establish durable antigen-specific immune tolerance. Here, we developed an oral nano-dietary fiber (NDF) platform that integrates allergen delivery with microbiota-driven immunomodulation, using dextran as both an antigen carrier and a fermentable substrate for intestinal bacteria. NDF enabled coordinated allergen release and sustained short-chain fatty acid (SCFA) production in the gut, promoting SCFA-FFAR2-dependent expansion of regulatory B and T cells and their trafficking to the nasal mucosa. In murine models, NDF slowed disease progression, improved airway function, and reduced allergen-specific IgE levels. Together, these findings establish NDF as an oral tolerogenic vaccine that provides durable protection against allergic airway disease through the gut-nasal axis.
Graphical overview of explainable artificial intelligence (XAI) for farm-to-fork postharvest preservation. Postharvest deterioration accumulates across orchard, packhouse, refrigerated transportation, warehouse, and distribution stages under fluctuating temperature, humidity, atmosphere, and mechanical stress. Multimodal data streams, including host omics, microbiome profiles, environmental sensing, RGB/hyperspectral/thermal imaging, spectroscopy, key genes, and logistics records, are integrated through a data lakehouse and analyzed by postharvest XAI models. Explainable modules, including SHapley Additive exPlanations (SHAP)/local attribution, graph neural network (GNN) explanation, pathway-constrained models, counterfactual reasoning, and stability/faithfulness auditing, convert black-box spoilage-risk prediction into interpretable biological mechanisms. These mechanisms guide actionable interventions such as antioxidant coating, elicitor spray, biocontrol consortia, packaging optimization, and gene-targeted strategies. Validation through storage trials, sensory evaluation, microbial testing, and sequencing closes the loop from prediction to explanation, intervention, and validated decision support.
Abstract Nanopore direct RNA sequencing (DRS) has transformed transcriptomics by enabling single‐molecule, long‐read sequencing of native RNA without the need for reverse transcription or amplification. In contrast to short‐read RNA‐seq and cDNA‐based long‐read approaches, DRS can simultaneously capture multiple RNA modifications, full‐length transcript architecture, alternative splicing patterns, and poly(A) tail features within individual molecules, thereby providing an integrated view of transcriptomic and epitranscriptomic regulation. In this comprehensive review, we outline the biophysical principles underlying nanopore DRS and trace its technological evolution. We compare its performance with short‐read RNA sequencing, long‐read cDNA sequencing, and conventional RNA‐modification mapping strategies, highlighting its advantages in isoform‐resolved quantification and multilayer RNA feature integration, while also clarifying contexts in which alternative or combined approaches may be more appropriate for robust biological interpretation. We further summarize optimized experimental workflows, including library construction strategies tailored to diverse RNA biotypes (mRNA, rRNA, tRNA, circRNA, miRNA, and nonpoly(A) transcripts), as well as recommended quality‐control procedures and sequencing optimization practices. Emphasizing recent computational advances and translational applications of DRS, we cover state‐of‐the‐art algorithms for RNA modification detection, transcript reconstruction, and isoform quantification. We also propose analytical pipelines for poly(A) tail length inference and integrative frameworks that jointly analyze these regulatory layers. We distinguish direct nanopore signals from computational inferences to define confidence levels and emphasize benchmarking and orthogonal validation of readouts. Practical implementation examples are included to facilitate reproducible analysis. Finally, we highlight emerging applications of integrated DRS, including the resolution of complex transcriptomes, the characterization of coordinated epitranscriptomic regulation, and the identification of disease‐associated RNA signatures. We also discuss current technical challenges and future perspectives, particularly in relation to multi‐omics integration and the broader deployment of DRS in precision medicine as well as in plant and animal research.
Global agricultural productivity is increasingly destabilized by climate change-driven droughts, floods, extreme heat, and severe storms. Although the climate-smart agriculture (CSA) framework addresses these challenges, implementation has focused mainly on plant genetics and agronomic inputs, leaving the adaptive potential of the crop microbiome underexplored. Here, we examine the agricultural use of synthetic microbial communities (SynComs) through the "crop holobiont" concept, in which plants and their associated microbiota function as an integrated, responsive system rather than through plant genomes alone. Pioneer plants in extreme environments may serve as reservoirs of stress-adapted microbes and provide a strategic toolkit for advancing CSA. SynComs assembled from these microbes can act not only as nutrient suppliers but also as dynamic physiological modulators that enhance crop phenotypic plasticity under climatic stress. We propose a roadmap for crop microbiology that integrates synthetic ecological engineering, with broad implications for CSA.
Immunotherapy resistance presents a formidable challenge in tumor biology. While fibroblast growth factor receptor 3 (FGFR3) serves as a pivotal oncogenic driver in a multitude of cancers, the exploration of its role in immune checkpoint inhibitor (ICI) resistance remains scarce, thus impeding a deeper understanding of the tumor immune microenvironment (TIME) in the era of immunotherapy. Employing patient-derived urothelial carcinoma (UC) organoids and co-cultured systems, along with single-cell RNA sequencing (scRNA-seq), whole-exome sequencing (WES), bulk RNA-seq, and CUT& Tag epigenomics in UC cohorts, we identified and characterized the key downstream mediators of FGFR3. The TIME associated with FGFR3 mutations exhibited a depletion of NK and CD8+ T cells, while simultaneously harboring an accumulation of exhausted effectors, correlating with diminished ICI response. Erdafitinib reprogrammed this "cold" TME into an inflamed state through a novel FGFR3-STAT5-IRF2 signaling cascade. These findings, corroborated by a wealth of evidence, advocate for the combination of FGFR3-targeted therapy with immunotherapy for UC, bridging critical pre-clinical and clinical insights.
Liver cirrhosis is associated with profound disruption of host-microbiome metabolic interactions. Using paired oral and fecal metagenomics combined with genome-scale metabolic modeling, we investigated how microbial translocation along the oral-gut axis influences microbial metabolism at different cirrhosis severities. Reactobiome-based functional profiling revealed progressive metabolic convergence between oral and gut microbiomes, quantified by a decrease in oral-gut metabolic distance. Translocation-associated microbial species enriched in patients with cirrhosis were predicted to have elevated capacities for ammonia and acetate production. Microbial-community and host metabolic modeling further suggested that these microbial metabolic shifts may influence host energy metabolism and redox balance across the liver, brain, and skeletal muscle. Together, these findings suggest a potential acetate-ammonia metabolic axis linking oral-gut microbial translocation with systemic metabolic stress in advanced cirrhosis.
Longitudinal multi-omics profiling of a nonhuman primate Rett syndrome (RTT) model reveals early systemic alterations. RTT monkeys exhibited postnatal growth retardation, intestinal structural abnormalities, and low-grade systemic inflammation. Gut microbiome analysis showed delayed microbial maturation and age-discordant dysbiosis, including altered Firmicutes/Bacteroidetes ratios and persistent community restructuring. Fecal metabolomics revealed reduced short-chain fatty acids (SCFAs), disrupted microbe-metabolite networks, and broad alterations in lipid, amino acid, and energy metabolism. Electrocardiogram (ECG) identified prolonged corrected QT interval (QTc) and subclinical cardiac electrophysiological changes. Integrated multi-omics analyses indicate that RTT involves early, coordinated dysregulation across gut microbial, metabolic, immune, and peripheral physiological systems, supporting its characterization as a systemic disorder from the early postnatal stage.
Intracellular metal dyshomeostasis has emerged as a key regulator of specialized regulated cell death (RCD) programs, challenging classical views that regard necrosis as entirely accidental. This review systematically delineates the molecular architecture and translational trajectories underlying metal-dependent RCD, including iron-driven ferroptosis, copper-mediated cuproptosis, and additional emerging modalities such as calcicoptosis, necrosis by sodium overload (NECSO), and the newly designated zincoptosis, mnoptosis, and coptosis. We examined distinct execution mechanisms, ranging from membrane lipid peroxidation and lipoylation-targeted proteotoxic stress to organelle-specific bioenergetic failure, which arise following disruption of compartmentalized metal-buffering networks. To bridge the persistent knowledge gap between foundational metallobiology and clinical application, we evaluated a bidirectional therapeutic framework: exploiting synthetic lethality and metabolic gating via clinical inducers (e.g., sorafenib, elesclomol) to selectively eliminate therapy-resistant malignancies while deploying targeted pathway inhibitors and systemic agonists (e.g., dipyridamole, omaveloxolone) to limit pathological tissue degeneration in ischemic and neurodegenerative disorders. Recognizing that off-target multiorgan toxicity and complex in vivo crosstalk among interconnected death pathways (e.g., disulfidptosis and PANoptosis) represent major translational challenges, we assessed advanced materials-science strategies designed to overcome these barriers. Specifically, we highlighted the integration of single-atom catalysts, stimuli-responsive nanomedicines, and biomimetic carriers engineered to spatiotemporally confine catalytic oxidative flux. Finally, we examined the systemic immunological consequences of targeted metal dysregulation, detailing how metal-induced immunogenic cell death and cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway hyperactivation reshape immunosuppressive microenvironments and modulate sterile inflammation, thereby enhancing responsiveness to immune checkpoint blockade, providing a definitive molecular blueprint for next-generation precision therapeutics.
Abstract Spatial omics technologys help overcome key limitations of conventional omics approaches that lack spatial information, by providing a panoramic perspective from the molecular level to the microenvironment scale for addressing spatially resolved biological questions in life sciences. With the rapid advancement of this field, there are significant differences among technology platforms, algorithms, and research workflows, which bring three core challenges to interdisciplinary researchers: the detailed explanation of technical principles, the selection of appropriate algorithms, and the future development directions. This review systematically summarizes the technical platforms and analytical algorithms of spatial omics, compares their advantages and disadvantages in the context of specific tasks and presents application cases across multiple biological fields. It also outlines the emerging research directions and advances in large model integration. It ultimately aims to provide a reference for researchers from diverse disciplines to design and implement spatial omics studies.
