Food allergy (FA) has received increased attention in recent years. Multiple studies have highlighted the crucial role of short-chain fatty acids (SCFAs) in the development of IgE-mediated FA. Here, a case-control approach was employed to analyze SCFAs profiles in children with FA, while an ovalbumin (OVA)-sensitized mouse model was utilized to explore the underlying mechanism by which SCFAs mitigate FA. Children with food-sensitized tolerance (FST) (n = 20) or FA (n = 20), and healthy controls (HC) (n = 20) were recruited to analyze SCFAs profiles. The HC group exhibited higher SCFAs levels in fecal samples than the FST, FA, and FST + FA groups. Data from an OVA-sensitized mouse model showed that butyrate exhibited a more significant effect on reducing allergic reactions compared to other SCFAs. Compared to the negative control group, OVA-induced oxidative stress (OS) triggered excessive Notch signaling activation, which subsequently impaired both tight junctions integrity and mucosal barrier function in murine intestinal epithelial cells (IECs). Gut dysbiosis induced mucus layer erosion, thereby elevating IECs exposure to food antigens and OS, which potentiated Notch signaling activation. However, butyrate counteracted this loop by restoring microbiota structure and suppressing reactive oxygen species (ROS)/Notch cascades. Strikingly, low-dose butyrate (0.25–1 mM) protected rat small intestine crypt epithelial cells (IEC-6) by inhibiting ROS, whereas high-dose (2–5 mM) exacerbated oxidative injury and triggered activation of Notch signaling. Our study revealed the potential molecular mechanisms through which butyrate alleviates food allergy, providing a potential therapeutic strategy for its management.
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.
Aging-related decline and adaptation are complex, multifaceted processes that affect various tissues and increase risk of chronic diseases. To characterize key changes in cross-tissue aging, we performed comprehensive proteomic and metabolomic analyses across 21 solid tissues and plasma samples, alongside shotgun metagenomic profiling of fecal microbial communities in young and aged mice. Our findings revealed widespread aging-rewired chronic inflammation, characterized by complement system activation in plasma and universal immunoglobulins accumulation across multiple solid tissues. This inflammatory remodeling significantly enhanced vulnerability to aging-related tissue injury. Moreover, we identified organ-specific and organ-enriched proteins with high functional specificity. Among these, aging-related proteins were closely linked to disorders arising from lipid metabolism dysfunction. Analysis of multi-tissue metabolomic and fecal metagenomic profiles revealed that aging significantly disrupted inter-tissue metabolic coupling, activities of polyunsaturated fatty acids metabolism, and gut microbiota homeostasis. Aged mice exhibited a marked decrease in Escherichia and an increase in Helicobacter, strongly correlating with alterations in omega-3 and omega-6 fatty acid abundances. Through multi-omics integration, we identified key molecular hubs driving organismal responses to aging. Collectively, our study uncovers extensive aging-associated alterations across tissues, emphasizing the interplay between systemic inflammation and dysbiosis-driven fatty acid remodeling. These findings provide deeper insights into the development of healthy aging from a cross-tissue perspective.
Investigating the genetic regulatory mechanisms underlying complex traits forms the foundation for crop improvement. Verticillium wilt (VW), caused by Verticillium dahliae (V. dahliae), is one of the most devastating diseases affecting crop production worldwide. However, the genetic basis underlying crop resistance to V. dahliae remains largely obscure, hindering progress in the genomic selection for VW resistance breeding. Here, we unraveled the genetic architectures and regulatory landscape of VW resistance in cotton by combining genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) using 1152 transcriptomes derived from 290 cotton accessions. We identified 10 reliable quantitative trait loci (QTLs) associated with VW resistance across multiple environments. These QTLs showed a pyramiding resistance effect and exhibited promising efficacy in the genomic prediction of cotton's VW resistance supported by an F2:3 population. Moreover, trace analysis of these elite alleles revealed a notably increased utilization of Lsnp1, Lsnp4, Lsnp5, Lsnp8, and Lsnp9, which potentially contribute to the improvement of VW resistance in Chinese cotton breeding since the 1990s. We also identified remarkable gene modules and expression QTL (eQTL) hotspots related to the regulation of reactive oxygen species (ROS) homeostasis and immune response. Furthermore, 15 candidate causal genes were prioritized by TWAS. Knocking down eight genes with a negative effect significantly enhanced cotton resistance to V. dahliae. Among them, GhARM, encoding an armadillo (ARM)-repeat protein, was verified to modulate cotton resistance to V. dahliae by regulating ROS homeostasis. Overall, this study updates the understanding of the genetic basis and regulatory mechanisms of cotton's VW resistance, providing valuable strategies for VW management through genomic selection in cotton breeding.
Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state-of-the-art methodologies, and analytical workflows essential to metaproteomics. Topics covered include experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches.
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.
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.
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).
Acute chemoradiotherapy-induced intestinal injury (ACRIII) is a common and debilitating complication in patients with colorectal cancer, significantly impairing both quality of life and treatment outcomes. This study aimed to investigate the role of the gut microbiome in mitigating ACRIII. Through bioinformatics analysis of clinical fecal samples and fecal microbiota transplantation (FMT) experiments in mice, we identified a strong association between a high abundance of Lactobacillus species and the absence of ACRIII. From the fecal samples of rectal cancer patients who achieved complete remission without experiencing ACRIII during chemoradiotherapy, 10 novel Lactobacillus strains were isolated and characterized. Among these, Lacticaseibacillus rhamnosus DY801 exhibited a robust capacity to synthesize methionine through metB. This microbial methionine production modulated methionine metabolism in host gut lymphoid tissue inducer (Lti) cells, without diminishing the therapeutic efficacy of chemoradiotherapy. Supplementation with methionine increased intracellular levels of S-adenosylmethionine and enhanced histone H3 lysine 4 trimethylation (H3K4me3) in Lti cells. These epigenetic modifications led to the suppression of pro-inflammatory cytokines interleukin-17A (IL-17A) and interleukin-22 (IL-22), ultimately reducing ACRIII severity. Our findings suggest that specific Lactobacillus strains derived from patients with exceptional treatment responses may offer a novel therapeutic avenue for preventing or alleviating ACRIII. This microbiome-based approach holds significant potential for improving patient outcomes and enhancing the tolerability of chemoradiotherapy in colorectal cancer.