Polyunsaturated fatty acid-derived oxylipins regulate systemic inflammation and exert cardiovascular effects, yet their role in ST-segment elevation myocardial infarction (STEMI) remains unclear. Herein, we used targeted metabolomics and machine learning algorithms to develop an oxylipin-based risk model to accurately predict recurrent major adverse cardiovascular events (MACE) after STEMI in two independent prospective cohorts with 2 years of follow-up. The in vivo effects of significant oxylipin predictors were explored via a murine myocardial ischemia‒reperfusion model and functional metabolomics. Among the 130 plasma oxylipins detected in discovery cohort (n = 645), patients with and without recurrent MACE exhibited significant differences in a variety of oxylipin subclasses. We constructed an oxylipin-based prediction model that showed powerful performance in predicting recurrent MACE in the discovery cohort (predictive accuracy: 91.5%). The predictive value of the oxylipin marker panel was confirmed in an independent external validation cohort (predictive accuracy: 89.9%; n = 401). Furthermore, we found that the anti-inflammatory/pro-resolving oxylipin (ARO) predictor panel showed better prognostic performance than the pro-inflammatory oxylipin predictor panel in both cohorts. Compared with the treatment of pro-inflammatory oxylipin predictor panel, combined treatment of six ARO predictors, including 14,15 epoxy-eicosatrienoic acid, 14(15)-epoxy-eicosatetraenoic acid, 12,13-epoxy-octadecenoic acid, lipoxin A4, resolving D1, and 6 keto-prostaglandin F1 showed significant cardiac activities and synergistic metabolic actions in myocardial infarction‒reperfusion model mice. We also mechanistically identified an important role of ARO predictors in restraining ceramide/lysophosphatidylcholine synthesis and inhibiting inflammatory responses. Overall, the present study depicted the landscape of oxylipin profiles in the largest panel of STEMI patients worldwide. Our results also highlight the great potential of bioactive oxylipins in prognostic prediction and therapeutics after STEMI.

Microbial dysbiosis, characterized by an imbalanced microbial community structure and function, has been linked to hypertension. While prior research has primarily focused on differential abundances, our study highlights the role of non-differential microbes in hypertension. We propose that non-differential microbes contribute to hypertension through their ecological interactions, as defined by co-abundances (pairs of microbes exhibiting correlated abundance patterns). Using gut microbiome data from the Guangdong Gut Microbiome Project, which includes 2355 hypertensive and 4644 non-hypertensive participants across 14 regions, we identified replicable hypertension-related microbial interactions. Notably, most co-abundances involved non-differential microbes, which were found to correlate with both hypertension severity and hypertension-related microbial metabolic pathways. These findings emphasize the importance of microbial interactions in hypertension pathogenesis and propose a novel perspective for microbiome-based therapeutic strategies.

In metabarcoding research, such as taxon identification, phylogenetic placement plays a critical role. However, many existing phylogenetic placement methods lack comprehensive features for downstream analysis and visualization. Visualization tools often ignore placement uncertainty, making it difficult to explore and interpret placement data effectively. To overcome these limitations, we introduce a scalable approach using treeio and ggtree for parsing and visualizing phylogenetic placement data. The treeio-ggtree method supports placement filtration, uncertainty exploration, and customized visualization. It enhances scalability for large analyses by enabling users to extract subtrees from the full reference tree, focusing on specific samples within a clade. Additionally, this approach provides a clearer representation of phylogenetic placement uncertainty by visualizing associated placement information on the final placement tree.

Recent advances in understanding the modulatory functions of gut and gut microbiota on human diseases facilitated our focused attention on the contribution of the gut to the pathophysiological alterations of many extraintestinal organs, including the liver, heart, brain, lungs, kidneys, bone, skin, reproductive, and endocrine systems. In this review, we applied the “gut–X axis” concept to describe the linkages between the gut and other organs and discussed the latest findings related to the “gut–X axis,” including the underlying modulatory mechanisms and potential clinical intervention strategies.

The discovery of comammox Nitrospira in low pH environments has reshaped the ammonia oxidation process in acidic settings, providing a plausible explanation for the higher nitrification rates observed in weakly acidic soils. However, the response of comammox Nitrospira to varying pH levels and its ecological role in these environments remains unclear. Here, a survey across soils with varying pH values (ranging from 4.4 to 9.7) was conducted to assess how comammox Nitrospira perform under different pH conditions. Results showed that comammox Nitrospira dominate ammonia oxidation in weakly acidic soils, functioning as a K-strategy species characterized by slow growth and stress tolerance. As a key species in this environment, comammox Nitrospira may promote bacterial cooperation under low pH conditions. Genomic evidence suggested that cobalamin sharing is a potential mechanism, as comammox Nitrospira uniquely encode a metabolic pathway that compensates for cobalamin imbalance in weakly acidic soils, where 86.8% of metagenome-assembled genomes (MAGs) encode cobalamin-dependent genes. Additionally, we used DNA stable-isotope probing (DNA-SIP) to demonstrate its response to pH fluctuations to reflect how it responds to the decrease in pH. Results confirmed that comammox Nitrospira became dominant ammonia oxidizers in the soil after the decrease in pH. We suggested that comammox Nitrospira will become increasingly important in global soils, under the trend of soil acidification. Overall, our work provides insights that how comammox Nitrospira perform in weakly acidic soil and its response to pH changes.

The gut microbiota influences host immunity and metabolism, and changes in its composition and function have been implicated in several non-communicable diseases. Here, comparing germ-free (GF) and specific pathogen-free (SPF) mice using spatial transcriptomics, single-cell RNA sequencing, and targeted bile acid metabolomics across multiple organs, we systematically assessed how the gut microbiota's absence affected organ morphology, immune homeostasis, bile acid, and lipid metabolism. Through integrated analysis, we detect marked aberration in B, myeloid, and T/natural killer cells, altered mucosal zonation and nutrient uptake, and significant shifts in bile acid profiles in feces, liver, and circulation, with the alternate synthesis pathway predominant in GF mice and pronounced changes in bile acid enterohepatic circulation. Particularly, autophagy-driven lipid droplet breakdown in ileum epithelium and the liver's zinc finger and BTB domain-containing protein (ZBTB20)-Lipoprotein lipase (LPL) (ZBTB20-LPL) axis are key to plasma lipid homeostasis in GF mice. Our results unveil the complexity of microbiota–host interactions in the crosstalk between commensal gut bacteria and the host.

Cholesterol gallstones (CGS) still lack effective noninvasive treatment. The etiology of experimentally proven cholesterol stones remains underexplored. This cross-sectional study aims to comprehensively evaluate potential biomarkers in patients with gallstones and assess the effects of microbiome-targeted interventions in mice. Microbiome taxonomic profiling was conducted on 191 samples via V3−V4 16S rRNA sequencing. Next, 60 samples (30 age- and sex-matched CGS patients and 30 controls) were selected for metagenomic sequencing and fecal metabolite profiling via liquid chromatography-mass spectrometry. Microbiome and metabolite characterizations were performed to identify potential biomarkers for CGS. Eight-week-old male C57BL/6J mice were given a lithogenic diet for 8 weeks to promote gallstone development. The causal relationship was examined through monocolonization in antibiotics-treated mice. The effects of short-chain fatty acids such as sodium butyrate, sodium acetate (NaA), sodium propionate, and fructooligosaccharides (FOS) on lithogenic diet-induced gallstones were investigated in mice. Gut microbiota and metabolites exhibited distinct characteristics, and selected biomarkers demonstrated good diagnostic performance in distinguishing CGS patients from healthy controls. Multi-omics data indicated associations between CGS and pathways involving butanoate and propanoate metabolism, fatty acid biosynthesis and degradation pathways, taurine and hypotaurine metabolism, and glyoxylate and dicarboxylate metabolism. The incidence of gallstones was significantly higher in the Clostridium glycyrrhizinilyticum group compared to the control group in mice. The grade of experimental gallstones in control mice was significantly higher than in mice treated with NaA and FOS. FOS could completely inhibit the formation of gallstones in mice. This study characterized gut microbiome and metabolome alterations in CGS. C. glycyrrhizinilyticum contributed to gallstone formation in mice. Supplementing with FOS could serve as a potential approach for managing CGS by altering the composition and functionality of gut microbiota.

Shotgun metagenomics has become a pivotal technology in microbiome research, enabling in-depth analysis of microbial communities at both the high-resolution taxonomic and functional levels. This approach provides valuable insights of microbial diversity, interactions, and their roles in health and disease. However, the complexity of data processing and the need for reproducibility pose significant challenges to researchers. To address these challenges, we developed EasyMetagenome, a user-friendly pipeline that supports multiple analysis methods, including quality control and host removal, read-based, assembly-based, and binning, along with advanced genome analysis. The pipeline also features customizable settings, comprehensive data visualizations, and detailed parameter explanations, ensuring its adaptability across a wide range of data scenarios. Looking forward, we aim to refine the pipeline by addressing host contamination issues, optimizing workflows for third-generation sequencing data, and integrating emerging technologies like deep learning and network analysis, to further enhance microbiome insights and data accuracy. EasyMetageonome is freely available at https://github.com/YongxinLiu/EasyMetagenome.

The rapid growth of microbiome research has generated an unprecedented amount of multi-omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi-omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R-based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research.

Dietary fiber influences the composition and metabolic activity of microbial communities, impacting disease development. Current understanding of the intricate fiber-microbe-disease tripartite relationship remains fragmented and elusive, urging a systematic investigation. Here, we focused on microbiota disturbance as a robust index to mitigate various confounding factors and developed the Bio-taxonomic Hierarchy Weighted Aggregation (BHWA) algorithm to integrate multi-taxonomy microbiota disturbance data, thereby illuminating the complex relationships among dietary fiber, microbiota, and disease. By leveraging microbiota disturbance similarities, we (1) classified 32 types of dietary fibers into six functional subgroups, revealing correlations with fiber solubility; (2) established associations among 161 diseases, uncovering shared microbiota disturbance patterns that explain disease co-occurrence (e.g., type II diabetes and kidney diseases) and distinct microbiota patterns that discern symptomatically similar diseases (e.g., inflammatory bowel disease and irritable bowel syndrome); (3) designed a body-site-specific microbiota disturbance scoring scheme, computing a disturbance score (DS) for each disease and highlighting the pronounced capacity of Crohn's disease to disturb gut microbiota (DS = 14.01) in contrast with food allergy's minimal capacity (DS = 0.74); (4) identified 1659 fiber-disease associations, predicting the potential of dietary fiber to modulate specific microbiota changes associated with diseases of interest; (5) established murine models of inflammatory bowel disease to validate the preventive and therapeutic effects of arabinoxylan that notably perturbed the Bacteroidetes and Firmicutes phyla, as well as the Bacteroidetes and Lactobacillus genera, aligning with our model predictions. To enhance data accessibility and facilitate targeted dietary intervention development, we launched an interactive webtool—mDiFiBank at https://mdifibank.org.cn/.