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Prenatal exposure to glucocorticoids is linked to long-term health risks in offspring, but the role of maternal gut microbiota in mediating these effects remains unclear. Here, we demonstrate that prenatal prednisone therapy (PPT) in humans and prenatal prednisone exposure (PPE) in rats result in sex-specific long bone dysplasia in offspring, including reduced peak bone mass (PBM) and heightened osteoporosis risk in female offspring. Multi-omics profiling and fecal microbiota transplantation show that PPE alters maternal gut microbiota composition and depletes the microbial metabolite daidzein (DAI). DAI deficiency suppresses Hoxd12 expression, impairs osteogenesis, and leads to PBM decline in female offspring. In bone marrow-derived mesenchymal stem cells from PPE female offspring, DAI promoted Hoxd12 expression and osteogenic differentiation. Notably, DAI supplementation restored H3K9ac levels, enhanced Hoxd12 expression, and promoted osteogenic differentiation through the ERβ/KAT6A pathway. Furthermore, maternal DAI supplementation during pregnancy prevented osteoporosis susceptibility in PPE female offspring and alleviated functional abnormalities in multiple organs, including the liver, hippocampus, ovary, and adrenal gland. In conclusion, PPE induces multiorgan dysplasia and increases disease predisposition (e.g., osteoporosis) in female offspring by disrupting maternal gut microbiota and depleting DAI. Maternal DAI supplementation provides a promising preventive strategy to counteract these adverse outcomes.

The rapid advancement of multi-omics single-cell technologies has significantly enhanced our ability to investigate complex biological systems at unprecedented resolution. However, many existing analysis tools are complex, requiring substantial coding expertize, which can be a barrier for computationally less competent researchers. To address this challenge, we present single-cell analyst, a user-friendly, web-based platform to facilitate comprehensive multi-omics analysis. Single-cell analyst supports a wide range of data types, including six single-cell omics: single-cell RNA sequencing (scRNA-sequencing), single-cell assay for transposase accessible chromatin sequencing (scATAC-seq sequencing), single-cell immune profiling (scImmune profiling), single-cell copy number variation, cytometry by time-of-flight, and flow cytometry and spatial transcriptomics, and enables researchers to perform integrated analyses without requiring programming skills. The platform offers both online and offline modes, providing flexibility for various use cases. It automates critical analysis steps, such as quality control, data processing, and phenotype-specific analyses, while also offering interactive, publication-ready visualizations. With over 20 interactive tools for intermediate analysis, single cell analyst simplifies workflows and significantly reduces the learning curve typically associated with similar platforms. This robust tool accommodates datasets of varying sizes, completing analyses within minutes to hours depending on the data volume, and ensures efficient use of computational resources. By democratizing the complex process of multi-omics analysis, single-cell analyst serves as an accessible, all-encompassing solution for researchers of diverse technical backgrounds. The platform is freely accessible at www.singlecellanalyst.org.

Since its initial release in 2022, ggClusterNet has become a vital tool for microbiome research, enabling microbial co-occurrence network analysis and visualization in over 300 studies. To address emerging challenges, including multi-factor experimental designs, multi-treatment conditions, and multi-omics data, we present a comprehensive upgrade with four key components: (1) A microbial co-occurrence network pipeline integrating network computation (Pearson/Spearman/SparCC correlations), visualization, topological characterization of network and node properties, multi-network comparison with statistical testing, network stability (robustness) analysis, and module identification and analysis; (2) Network mining functions for multi-factor, multi-treatment, and spatiotemporal-scale analysis, including Facet.Network() and module.compare.m.ts(); (3) Transkingdom network construction using microbiota, multi-omics, and other relevant data, with diverse visualization layouts such as MatCorPlot2() and cor_link3(); and (4) Transkingdom and multi-omics network analysis, including corBionetwork.st() and visualization algorithms tailored for complex network exploration, including model_maptree2(), model_Gephi.3(), and cir.squ(). The updates in ggClusterNet 2 enable researchers to explore complex network interactions, offering a robust, efficient, user-friendly, reproducible, and visually versatile tool for microbial co-occurrence networks and indicator correlation patterns. The ggClusterNet 2R package is open-source and available on GitHub (https://github.com/taowenmicro/ggClusterNet).

A better understanding of the characteristic serum metabolites and microbiota from the gut and oral cavity in centenarians could contribute to elucidating the mutual connections among them and would help provide information to achieve healthy longevity. Here, we have recruited a total of 425 volunteers, including 145 centenarians in Suixi county — the first certified “International Longevity and Health Care Base” in China. An integrative analysis for the serum metabolites, gut, and oral microbiota of centenarians (aged 100–120) was compared with those of centenarians' lineal relatives (aged 24–86), the elderly (aged 65–88) and young (aged 23–54). Strikingly distinct metabolomic and microbiological profiles were observed within the centenarian signature, longevity family signature, and aging signature, underscoring the metabolic and microbiological diversity among centenarians and their lineal relatives. Within the centenarian between healthy and frail individuals, significant differences in metabolite profiles and microbiota compositions are observed, suggesting that healthy longevity is associated with unique metabolic and microbiota patterns. Through an integrative analysis, the tryptophan pathway has been revealed to be an important potential mechanism for individuals to achieve healthy longevity. Specifically, a key tryptophan metabolite, 5-methoxyindoleacetic acid (5-MIAA), was revealed to be associated with the genus Christensenellaceae R-7 group, and it exhibited effects of delaying cell senescence, promoting lifespan, and alleviating inflammation. Our characterization of the extensive metabolomic and microbiota remodeling in centenarians may offer new scientific insights for achieving healthy longevity.

Prokaryotic taxonomy based on short 16S rRNA sequences may lead to an overestimation of microbial diversity. In addition, a lack of sufficient coverage of previously reported taxa may lead to repetition or overestimation of novel taxa. In light of a recent study published in iMeta, we have issued a comment to remind microbial taxonomists of the importance of maintaining rigor and precision when delineating microbial species, urging researchers to avoid imprecise approaches.

This study explored the effect of plant-derived indigo supplementation on intestinal inflammation using in vivo, in vitro, and clinical sample analyses. Our results showed that indigo decreased mucosal inflammation by regulating CD4⁺ T cell differentiation in a gut microbiota-dependent manner. Microbes transferred from indigo-treated mice, indigo-induced enrichment of Roseburia intestinalis, and its metabolite butyrate played a role in Th17/Treg immunity similar to that of indigo in intestinal inflammation, which was involved in mTORC1/HIF-1α signal-mediated reprogrammed glucose metabolism. We further showed that patients with ulcerative colitis exhibited significant gut dysbiosis and CD4⁺ T cell differentiation abnormalities. Our findings provide new insights into the gut-immune axis in ulcerative colitis, offering a novel microbial-based immunotherapy for the treatment of inflammatory bowel disease.

The human body is inhabited by trillions of microorganisms that play a crucial role in health and diseases. Our understanding of the species and functional composition of the human gut microbiome is rapidly expanding, but it is still mainly based on taxonomic profiles or gene abundance measurements. As such, little is known about the species–function heterogeneity and dynamic activities in human microecosystem niches. By applying a novel gut-specific single-microbe ribonucleic acid (RNA) sequencing and analytical framework on three healthy donors with distinct enterotypes, we created a comprehensive transcriptional landscape of the human gut microbiome and dissected functional specialization in 38,922 single microbes across 198 species. We investigated the functional redundancy and complementarity involved in short-chain fatty acids related central carbon metabolism and studied the heterogeneity and covariation of single-microbe metabolic capacity. Comparing the human gut microbiome at different times throughout the day, we were able to map diurnal dynamic activities of the gut microbiome and discovered its association with sub-population functional heterogeneous. Remarkably, using single-microbe RNA sequencing, we systematically dissected the metabolic function heterogeneity of Megamonas funiformis, a keystone species in Asian populations. Together with in vitro and in vivo experimental validations, we proved M. funiformis can effectively improve mineral absorption through exogenous phytic acid degradation, which could potentially serve as a probiotic that reduces malnutrition caused by deficiency of mineral elements. Our results indicated that species-function heterogeneity widely exists and plays important roles in the human gut microbiome, and through single-microbe RNA sequencing, we have been able to capture the transcriptional activity variances and identify keystone species with specialized metabolic functions of possible biological and clinical importance.

Microbial communities play critical roles in various ecosystems. Despite extensive research on the taxonomic and functional diversity of microbial communities, effective approaches to regulate targeted microbial functions remain limited. Here, we present an innovative methodology that integrates core enzyme identification, protein structural characterization, regulator virtual screening, and functional validation to achieve precise microbiome functional regulation. As a proof of concept, we focused on the regulation of urea decomposition by the rumen microbiota in ruminants. Through metagenomic analysis, we identified the core urease gene and its corresponding microbial genome (MAG257) affiliated with the unclassified Succinivibrionaceae, and reconstructed its complete gene cluster. Structural analysis of the urease catalytic subunit (UreC) via cryo-electron microscopy (cryo-EM) revealed detailed features of its active site, guiding molecular docking studies that identified epiberberine, a natural compound with potent urease inhibitory activity. Validation in a rumen simulation system demonstrated that epiberberine significantly reduced urea decomposition and enhanced nitrogen utilization. This study establishes a robust framework that combines structural biology and computational screening to achieve targeted microbiome functional regulation, offering a promising tool for microbiome engineering and broader applications in animal productivity, human health, environmental improvement, and biotechnology.

Here, we conducted a large-scale investigation of vertebrate viruses in soils and found soil was a mediator of vertebrate viruses. Compared to natural soils, agricultural soils possessed distinct prevalence patterns, with a higher detection rate and richness for vertebrate viruses and higher potential health risks.

In this study, 102 cisplatin-ineligible patients with muscle-invasive bladder cancer (MIBC) who received neoadjuvant RC48-ADC combined with immunotherapy were included. We evaluated the pathological responses and explored multiple clinical characteristics to identify independent predictive indicators of the efficacy. The results showed that neoadjuvant RC48-ADC combined with immunotherapy had promising efficacy. Furthermore, we collected 11 MIBC samples and performed single-cell RNA sequencing. All BLCA epithelial cells were identified as four subclusters. We conducted differential gene expression/functional enrichment analysis, cell proportion analysis, cell cycle analysis, CNV analysis, and pseudotemporal analysis on all tumor cells to evaluate potential efficacy-predictive biomarkers and the evolutionary patterns of tumor cells during neoadjuvant treatment. The results indicated that the combined detection of HER2 and HSPA1A expression in C3 subcluster based on single-cell RNA sequencing is a potential strategy for predicting efficacy. In addition, C3 plays a dominant role in the emergence of drug-resistance during the evolution of BLCA epithelial cells.