mSystems最新文献

This study aimed to assess the impact of a high-fiber/low-fat agrarian diet (AD) on inflammation and metabolic outcomes in HIV-positive men who have sex with men (MSM). Since the gut microbiomes of MSM resemble those of individuals in agrarian cultures, including being Prevotella-rich and Bacteroides-poor, we hypothesized that they would have particularly strong health benefits from consumption of a diet matched to their microbiome type. Sixty-six participants, including 36 HIV-positive MSM [HIV(+)MSM], 21 HIV-negative MSM, and 9 HIV-negative men who have sex with women, were randomized to either an AD or a high-fat/low-fiber western diet (WD) for 4 weeks. Plasma, fecal, and colonic biopsy samples were obtained. Metabolic and inflammatory markers were measured in plasma. 16S ribosomal RNA sequencing was performed on fecal and biopsy samples, and shotgun metagenomic sequencing was performed on fecal samples. The AD reduced plasma low-density lipoprotein cholesterol (LDL-C) in HIV(+)MSM, with median reductions of 0.4138 mmoL/L at 2 weeks and 0.2845 mmol/L at 4 weeks. Greater LDL-C reductions were predicted by Prevotella-rich/Bacteroides-poor microbiomes with increased starch utilization potential, emphasizing the importance of personalized microbiome-dietary matching. The AD also reduced T cell exhaustion and pro-inflammatory intermediate monocytes and altered host transcription in the colonic mucosa.

Importance: Our findings suggest tailoring diet interventions to baseline microbiome types can promote metabolic health in Prevotella-rich/Bacteroides-poor MSM, a significant portion of people living with HIV at risk for metabolic syndrome.This study was registered at NCT02610374.

Investigating microbiome subnetworks and identifying central microbes in specific ecological niches is a critical issue in human microbiome studies. Traditional methods typically require control samples, limiting the ability to study microbiomes at distinct body sites without matched controls. Moreover, some clustering methods are not well-suited for microbial data and fail to identify central subcommunities across ecological niches after clustering. In this study, we present MNetClass, a novel microbial network clustering analysis framework. It utilizes a random walk algorithm and a rank-sum ratio-entropy weight evaluation model to classify key subnetworks and identify central microbes at any body site, without the need for control samples. We demonstrate its capabilities on both simulated and real microbiome data sets. Simulation results indicate that MNetClass outperforms current unsupervised microbial clustering methods. In applied case studies, the analysis of microbiome data from five distinct oral sites revealed site-specific microbial communities. Furthermore, MNetClass demonstrated superior predictive performance on cross-cohort Autism Spectrum Disorder data and identified age-related microbial communities across different oral sites, underscoring its broad applicability in microbiome research.IMPORTANCEMNetClass provides a valuable tool for microbiome network analysis, enabling the identification of key microbial subcommunities across diverse ecological niches. Implemented as an R package (https://github.com/YihuaWWW/MNetClass), it offers broad accessibility for researchers. Here, we systematically benchmarked MNetClass against existing microbial clustering methods on synthetic data using various performance metrics, demonstrating its superior efficacy. Notably, MNetClass operates without the need for control groups and effectively identifies central microbes, highlighting its potential as a robust framework for advancing microbiome research.

Studies using genetically engineered mouse (GEM) models are often performed over extended periods. The microbiomes of GEM colonies are expected to retain some of the microbial features present in the founder mice used to generate each GEM model and to acquire new features through dietary and environmental sources. The rate at which these processes occur over time likely varies between institutions. To assess the relative effect size of environment on the microbiome of GEMs used in biomedical research, we performed 16S rRNA metabarcoding of fecal samples from 275 distinct GEM lines (n = 351) maintained by 139 different laboratories at 84 different research institutions in 34 U.S. states or districts and seven other countries, and compared intra-strain, inter-strain, inter-lab, and inter-institution similarities. Reference data from mice harboring supplier-origin (SO) microbiomes (n = 1,171) were used to determine the relative contribution and nature of microbes from known and unknown sources. Paradoxically, the data indicate that the immediate laboratory-level environment is the dominant factor shaping the microbiome of GEM models, but that the microbiome of GEMs develops similarities in beta-diversity, regardless of other factors. Related to this, we detected an unexpectedly high prevalence and abundance of Helicobacter spp. in GEM microbiomes, the abundance of which correlated significantly with the abundance of multiple resident taxa colonizing the mucosa. These findings suggest a higher prevalence of Helicobacter spp. in laboratory mice than previously appreciated, and the possibility of positive and negative interactions with other taxa is found to affect GEM model phenotypes.IMPORTANCEThere are concerns regarding the reproducibility and predictive value of mouse models of human disease. Notwithstanding those legitimate concerns, genetically engineered mouse (GEM) models provide an invaluable platform to investigate gene function or effects of environmental factors in a biological system. The microbiome of GEM models significantly influences model phenotypes and thus represents a possible source of poor reproducibility. While the microbiome is often incorporated in research investigating disease mechanisms using GEMs, limited information is available regarding the similarity of the microbiome of GEM models within and between research labs at the same institution, or across institutions. Moreover, while the microbiome of founder mice from different suppliers is known to differ, the degree to which features present in supplier-origin microbiomes are retained in GEM colonies throughout experimentation is unclear. These data demonstrate the robust effect of lab-level environment and the need for sample collection concurrent with phenotyping.

Plant secondary metabolites are key mediators of plant-insect-microbiome interactions, yet their role in structuring functionally relevant insect-associated microbial communities remains poorly understood. Here, we combined a factorial experiment using Brassica napus genotypes differing in glucosinolate (GLS) content with distinct succession to investigate the eco-evolutionary dynamics of the microbiota of the root herbivore Delia radicum. Amplicon sequencing and microbial culturing revealed that both rhizospheric and gut microbial communities are shaped by plant genotype and soil legacy, with a subset of bacterial taxa shared across compartments. Notably, Pseudomonas brassicacearum, harboring the isothiocyanates (ITC) detoxifying gene saxA, was consistently recovered from both plant and insect habitats. Functional assays confirmed its capacity to degrade 2-phenylethyl isothiocyanate (PEITC), a major toxic GLS hydrolysis product. Other gut-derived microbial isolates exhibited heterogeneous responses to PEITC, ranging from growth inhibition, promotion, or growth recovery after a prolonged lag phase. Despite the toxicity of ITC, insect fitness proxies were enhanced on GLS +plants, suggesting microbiota-mediated adaptation to host chemical defenses. Our findings reveal a plant genotype-specific filtering of environmentally acquired microbes and highlight the role of detoxifying symbionts in Delia radicum performance.IMPORTANCEUnderstanding how herbivorous insects adapt to plant chemical defenses is important in the context of new agricultural practices. This study highlights that the host plant genotype shapes not only rhizospheric and gut microbial communities but also promotes the acquisition of symbiotic bacteria capable of detoxifying harmful isothiocyanates. These findings reveal a functional microbial pathway for insect adaptation to plant defenses, with potential implications for pest management strategies. By uncovering the role of plant-associated microbiota, the acquisition of beneficial microbes, and their functional contributions to host fitness, this work provides a foundation for innovative agroecological approaches that leverage plant-microbe-insect interactions.

Giant viruses (GVs) of the phyla Nucleocytoviricota and Mirusviricota are large double-stranded DNA viruses that infect diverse eukaryotic hosts and impact biogeochemical cycles. Their diversity and ecological roles have been well studied in the photic layer of the ocean, but less is known about their activity, population dynamics, and adaptive strategies in the aphotic layers. Here, we conducted eight seasonal time-series samplings of the surface and mesopelagic layers at a coastal site in Muroto, Japan, and integrated 18S metabarcoding, metagenomic, and metatranscriptomic data to investigate mesopelagic GVs and their potential hosts. The analysis identified 48 GV genomes including six that were exclusively detected in the mesopelagic layer. Notably, these mesopelagic-specific GVs showed persistent activity across seasons. To further investigate the distribution and phylogenomic features of GVs at a global scale across broader depths, we compiled 4,473 species-level GV genomes from the OceanDNA MAG project and other resources and analyzed 1,890 marine metagenomes. This revealed 101 deep-sea-specific GVs, distributed across the GV phylogenetic tree, indicating that adaptation to deep-sea environments has occurred in multiple lineages. One clade enriched with deep-sea-specific GVs included a GV genome identified in our Muroto data, which displayed a wide geographic distribution. Seventy-six KEGG orthologs and 74 Pfam domains were specifically enriched in deep-sea-specific GVs, encompassing functions related to the ubiquitin system, energy metabolism, and nitrogen acquisition. These findings support the scenario that distinct GV lineages have adapted to hosts in aphotic marine environments by altering their gene repertoire to thrive in this unique habitat.IMPORTANCEGiant viruses are widespread in the ocean surface and are key in shaping marine ecosystems by infecting phytoplankton and other protists. However, little is known about their activity and adaptive strategies in deep-sea environments. In this study, we performed metagenomic and metatranscriptomic analyses of seawater samples collected from a coastal site in Japan and discovered giant virus genomes showing persistent transcriptional activity across seasons in the mesopelagic water. Using a global marine data set, we further uncovered geographically widespread and vertically extensive groups of deep-sea-specific giant viruses and characterized their distinctive gene repertoire, which likely facilitates adaptation to the limited availability of light and organic compounds in the aphotic zone. These findings expand our understanding of giant virus ecology in the dark ocean.

Global regulators (GRs) are key transcription factors that orchestrate the expression of multiple genes, playing essential roles in stress responses, virulence, secondary metabolism, and antibiotic resistance-traits that make them powerful tools for synthetic biology applications. However, conventional approaches often fail to detect remote homologs and novel GR types, limiting our understanding of their regulatory diversity and evolutionary dynamics across prokaryotes. Here, we present a large-scale, protein language model-driven framework to systematically chart the global regulatory landscape across 14,800 bacterial and archaeal type strain genomes-the most taxonomically diverse prokaryotic data set analyzed to date. Using a deep learning-based GR identification model trained on 74,872 curated GR sequences, we systematically identified over 270,000 GR-like proteins, including 173,256 homologs of 214 experimentally validated GR types, 52 putative GR types, and 76,113 proteins of unknown function. This model demonstrated high sensitivity and generalization capability, enabling the discovery of remote homologs and cryptic regulators beyond the reach of similarity- or domain-based methods. This expanded GR catalog revealed lineage-specific distribution patterns, functionally diverse regulons with both conserved and niche-specific targets, and hierarchical cross-regulatory networks with shared and phylum-enriched hubs. By unveiling the hidden diversity and evolutionary structure of prokaryotic global regulators, this landscape of GRs provides foundational insights into microbial gene regulation and offers a powerful toolkit for the rational design of tunable, modular, and orthogonal genetic circuits in synthetic biology.IMPORTANCEGRs are master transcriptional regulators critical for microbial adaptation, stress tolerance, and metabolic control, and they serve as valuable components for synthetic biology. However, a comprehensive understanding of GR diversity and function across the prokaryotic domain has remained elusive due to the limitations of traditional detection methods. In this study, we developed a deep learning-based identification framework and applied it to 14,800 bacterial and archaeal type strain genomes, resulting in the discovery of over 270,000 GR-like proteins, including dozens of novel types. This work provides a comprehensive landscape of prokaryotic global regulators, revealing lineage-specific distribution patterns, both conserved and specialized regulons, and modular cross-regulatory network architectures. These insights not only deepen our understanding of transcriptional regulation in microbial evolution and ecology but also provide a scalable resource for the rational design of regulatory systems in synthetic biology.

Maladaptive host metabolic responses to infection are emerging as major determinants of infectious disease pathogenesis. However, the factors regulating these metabolic changes within tissues remain poorly understood. In this study, we used toxoplasmosis, as a prototypical example of a disease regulated by strong type I immune responses, to assess the relative roles of current local parasite burden, local tissue inflammation, and the microbiome in shaping local tissue metabolism during acute and chronic infections. Toxoplasmosis is a zoonotic disease caused by the parasite Toxoplasma gondii. This protozoan infects the small intestine and then disseminates broadly in the acute stage of infection, before establishing chronic infection in the skeletal muscle, cardiac muscle, and brain. We compared metabolism in 11 sampling sites in C57BL/6 mice during the acute and chronic stages of T. gondii infection. Strikingly, major spatial mismatches were observed between metabolic perturbation and local parasite burden at the time of sample collection for both disease stages. By contrast, a stronger association with indicators of active type I immune responses was observed, indicating a tighter relationship between metabolic perturbation and local immunity than with local parasite burden. Loss of signaling through the IL1 receptor in IL1R knockout mice was associated with reduced metabolic impact of infection. In addition, we observed significant changes in microbiota composition with infection and candidate microbial origins for multiple metabolites impacted by infection. These findings highlight the metabolic consequences of toxoplasmosis across different organs and potential regulators.IMPORTANCEInflammation is a major driver of tissue perturbation. However, the signals driving these changes on a tissue-intrinsic and molecular level are poorly understood. This study evaluated tissue-specific metabolic perturbations across 11 sampling sites following systemic murine infection with the parasite Toxoplasma gondii. Results revealed relationships between differential metabolite enrichment and variables, including inflammatory signals, pathogen burden, and commensal microbial communities. These data will inform hypotheses about the signals driving specific metabolic adaptation in acute and chronic protozoan infection, with broader implications for infection and inflammation in general.