{"title":"Linking dietary fiber to human malady through cumulative profiling of microbiota disturbance","authors":"Xin Zhang, Huan Liu, Yu Li, Yanlong Wen, Tianxin Xu, Chen Chen, Shuxia Hao, Jielun Hu, Shaoping Nie, Fei Gao, Gengjie Jia","doi":"10.1002/imt2.70004","DOIUrl":"https://doi.org/10.1002/imt2.70004","url":null,"abstract":"<p>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 (<i>DS</i>) for each disease and highlighting the pronounced capacity of Crohn's disease to disturb gut microbiota (<i>DS</i> = 14.01) in contrast with food allergy's minimal capacity (<i>DS</i> = 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 <i>Bacteroidetes</i> and <i>Firmicutes</i> phyla, as well as the <i>Bacteroidetes</i> and <i>Lactobacillus</i> 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/.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"4 1","pages":""},"PeriodicalIF":23.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Defeng Bai, Chuang Ma, Jiani Xun, Hao Luo, Haifei Yang, Hujie Lyu, Zhihao Zhu, Anran Gai, Salsabeel Yousuf, Kai Peng, Shanshan Xu, Yunyun Gao, Yao Wang, Yong-Xin Liu
{"title":"MicrobiomeStatPlots: Microbiome statistics plotting gallery for meta-omics and bioinformatics","authors":"Defeng Bai, Chuang Ma, Jiani Xun, Hao Luo, Haifei Yang, Hujie Lyu, Zhihao Zhu, Anran Gai, Salsabeel Yousuf, Kai Peng, Shanshan Xu, Yunyun Gao, Yao Wang, Yong-Xin Liu","doi":"10.1002/imt2.70002","DOIUrl":"https://doi.org/10.1002/imt2.70002","url":null,"abstract":"<p>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.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"4 1","pages":""},"PeriodicalIF":23.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"EasyMetagenome: A user-friendly and flexible pipeline for shotgun metagenomic analysis in microbiome research","authors":"Defeng Bai, Tong Chen, Jiani Xun, Chuang Ma, Hao Luo, Haifei Yang, Chen Cao, Xiaofeng Cao, Jianzhou Cui, Yuan-Ping Deng, Zhaochao Deng, Wenxin Dong, Wenxue Dong, Juan Du, Qunkai Fang, Wei Fang, Yue Fang, Fangtian Fu, Min Fu, Yi-Tian Fu, He Gao, Jingping Ge, Qinglong Gong, Lunda Gu, Peng Guo, Yuhao Guo, Tang Hai, Hao Liu, Jieqiang He, Zi-Yang He, Huiyu Hou, Can Huang, Shuai Ji, ChangHai Jiang, Gui-Lai Jiang, Lingjuan Jiang, Ling N. Jin, Yuhe Kan, Da Kang, Jin Kou, Ka-Lung Lam, Changchao Li, Chong Li, Fuyi Li, Liwei Li, Miao Li, Xin Li, Ye Li, Zheng-Tao Li, Jing Liang, Yongxin Lin, Changzhen Liu, Danni Liu, Fengqin Liu, Jia Liu, Tianrui Liu, Tingting Liu, Xinyuan Liu, Yaqun Liu, Bangyan Liu, Minghao Liu, Wenbo Lou, Yaning Luan, Yuanyuan Luo, Hujie Lv, Tengfei Ma, Zongjiong Mai, Jiayuan Mo, Dongze Niu, Zhuo Pan, Heyuan Qi, Zhanyao Shi, Chunjiao Song, Fuxiang Sun, Yan Sun, Sihui Tian, Xiulin Wan, Guoliang Wang, Hongyang Wang, Hongyu Wang, Huanhuan Wang, Jing Wang, Jun Wang, Kang Wang, Leli Wang, Shao-kun Wang, Xinlong Wang, Yao Wang, Zufei Xiao, Huichun Xing, Yifan Xu, Shu-yan Yan, Li Yang, Song Yang, Yuanming Yang, Xiaofang Yao, Salsabeel Yousuf, Hao Yu, Yu Lei, Zhengrong Yuan, Meiyin Zeng, Chunfang Zhang, Chunge Zhang, Huimin Zhang, Jing Zhang, Na Zhang, Tianyuan Zhang, Yi-Bo Zhang, Yupeng Zhang, Zheng Zhang, Mingda Zhou, Yuanping Zhou, Chengshuai Zhu, Lin Zhu, Yue Zhu, Zhihao Zhu, Hongqin Zou, Anna Zuo, Wenxuan Dong, Tao Wen, Shifu Chen, Guoliang Li, Yunyun Gao, Yong-Xin Liu","doi":"10.1002/imt2.70001","DOIUrl":"https://doi.org/10.1002/imt2.70001","url":null,"abstract":"<p>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.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"4 1","pages":""},"PeriodicalIF":23.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cross-tissue multi-omics analyses reveal the gut microbiota's absence impacts organ morphology, immune homeostasis, bile acid and lipid metabolism","authors":"Juan Shen, Weiming Liang, Ruizhen Zhao, Yang Chen, Yanmin Liu, Wei Cheng, Tailiang Chai, Yin Zhang, Silian Chen, Jiazhe Liu, Xueting Chen, Yusheng Deng, Zhao Zhang, Yufen Huang, Huanjie Yang, Li Pang, Qinwei Qiu, Haohao Deng, Shanshan Pan, Linying Wang, Jingjing Ye, Wen Luo, Xuanting Jiang, Xiao Huang, Wanshun Li, Elaine Lai-Han Leung, Lu Zhang, Li Huang, Zhimin Yang, Rouxi Chen, Junpu Mei, Zhen Yue, Hong Wei, Kristiansen Karsten, Lijuan Han, Xiaodong Fang","doi":"10.1002/imt2.272","DOIUrl":"https://doi.org/10.1002/imt2.272","url":null,"abstract":"<p>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.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"4 1","pages":""},"PeriodicalIF":23.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hypertriglyceridemia-modulated gut microbiota promotes lysophosphatidylcholine generation to aggravate acute pancreatitis in a TLR4-dependent manner","authors":"Xiaofan Song, Lei Qiao, Xina Dou, Jiajing Chang, Xiaonan Zeng, Tianjing Deng, Ge Yang, Peiyun Liu, Cheng Wang, Qinhong Xu, Chunlan Xu","doi":"10.1002/imt2.70003","DOIUrl":"https://doi.org/10.1002/imt2.70003","url":null,"abstract":"<p>Hypertriglyceridemia (HTG) can lead to the disorder of gut microbiota in mice, resulting in the increase of endotoxin content. HTG can also aggravate the damage of intestinal barrier function and intestinal bacterial translocation in acute pancreatitis (AP) mice. Toll-like receptor 4 gene <i>(Tlr4)</i> knockout can significantly reduce gut permeability and endotoxin invasion in AP mice. In addition, HTG-modulated gut microbiota could up-regulate glycerophospholipid metabolism and increase lysophosphatidylcholine (LysoPC) content in a TLR4-dependent manner, thereby aggravating pancreatic injury in AP.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"4 1","pages":""},"PeriodicalIF":23.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Comammox Nitrospira act as key bacteria in weakly acidic soil via potential cobalamin sharing","authors":"Yuxiang Zhao, Jiajie Hu, Jiaqi Wang, Xiangwu Yao, Tong Zhang, Baolan Hu","doi":"10.1002/imt2.271","DOIUrl":"https://doi.org/10.1002/imt2.271","url":null,"abstract":"<p>The discovery of comammox <i>Nitrospira</i> 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 <i>Nitrospira</i> 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 <i>Nitrospira</i> perform under different pH conditions. Results showed that comammox <i>Nitrospira</i> 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 <i>Nitrospira</i> may promote bacterial cooperation under low pH conditions. Genomic evidence suggested that cobalamin sharing is a potential mechanism, as comammox <i>Nitrospira</i> 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 <i>Nitrospira</i> became dominant ammonia oxidizers in the soil after the decrease in pH. We suggested that comammox <i>Nitrospira</i> will become increasingly important in global soils, under the trend of soil acidification. Overall, our work provides insights that how comammox <i>Nitrospira</i> perform in weakly acidic soil and its response to pH changes.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"4 1","pages":""},"PeriodicalIF":23.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.271","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luoyang Ding, Yifan Wang, Linna Zhang, Chengfang Luo, Feifan Wu, Yiming Huang, Yongkang Zhen, Ning Chen, Limin Wang, Li Song, Kelsey Pool, Dominique Blache, Shane K. Maloney, Dongxu Liu, Zhiquan Yang, Xiaoyan Huang, Chuang Li, Xiang Yu, Zhenbin Zhang, Yifei Chen, Chun Xue, Yalan Gu, Weidong Huang, Lu Yan, Wenjun Wei, Yusu Wang, Jinying Zhang, Yifan Zhang, Yiquan Sun, Rui Dai, Shengbo Wang, Xinle Zhao, Haodong Wang, Ping Zhou, Qing-Yong Yang, Mengzhi Wang
{"title":"The HTIRDB: A resource containing a transcriptional atlas for 105 different tissues from each of seven species of domestic herbivore","authors":"Luoyang Ding, Yifan Wang, Linna Zhang, Chengfang Luo, Feifan Wu, Yiming Huang, Yongkang Zhen, Ning Chen, Limin Wang, Li Song, Kelsey Pool, Dominique Blache, Shane K. Maloney, Dongxu Liu, Zhiquan Yang, Xiaoyan Huang, Chuang Li, Xiang Yu, Zhenbin Zhang, Yifei Chen, Chun Xue, Yalan Gu, Weidong Huang, Lu Yan, Wenjun Wei, Yusu Wang, Jinying Zhang, Yifan Zhang, Yiquan Sun, Rui Dai, Shengbo Wang, Xinle Zhao, Haodong Wang, Ping Zhou, Qing-Yong Yang, Mengzhi Wang","doi":"10.1002/imt2.267","DOIUrl":"https://doi.org/10.1002/imt2.267","url":null,"abstract":"<p>Here, we describe the Herbivore Transcriptome Integrated Resource Database (HTIRDB, https://yanglab.hzau.edu.cn/HTIRDB#/). The HTIRDB comprises the self-generated transcriptomic data from 100 to 105 tissues from two female domestic herbivores from six species (cattle, donkey, goat, horse, rabbit, and sika deer) and two breeds of sheep, and an extra 28,710 related published datasets. The HTIRDB user-friendly interface provides tools and functionalities that facilitate the exploration of gene expression between tissues and species. The tools for comparative transcriptomics can be used to identify housekeeping genes, tissue-specific genes, species-specific genes, and species-conserved genes. To date, the HTIRDB is the most extensive transcriptome data resource for domestic herbivores that is freely available.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"4 1","pages":""},"PeriodicalIF":23.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree","authors":"Meijun Chen, Xiao Luo, Shuangbin Xu, Lin Li, Junrui Li, Zijing Xie, Qianwen Wang, Yufan Liao, Bingdong Liu, Wenquan Liang, Ke Mo, Qiong Song, Xia Chen, Tommy Tsan-Yuk Lam, Guangchuang Yu","doi":"10.1002/imt2.269","DOIUrl":"https://doi.org/10.1002/imt2.269","url":null,"abstract":"<p>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 <i>treeio</i> and <i>ggtree</i> for parsing and visualizing phylogenetic placement data. The <i>treeio</i>-<i>ggtree</i> 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.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"4 1","pages":""},"PeriodicalIF":23.7,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Non-differential gut microbes contribute to hypertension and its severity through co-abundances: A multi-regional prospective cohort study","authors":"Lu Liu, Qianyi Zhou, Tianbao Xu, Qiufeng Deng, Yuhao Sun, Jingxiang Fu, Muxuan Chen, Xiaojiao Chen, Zhenchao Ma, Quanbin Dong, Beining Ma, Yuwen Jiao, Yan Zhou, Tingting Wu, Huayiyang Zou, Jing Shi, Yifeng Wang, Yanhui Sheng, Liming Tang, Chao Zheng, Wei Wu, Wenjun Ma, Wei Sun, Shixian Hu, Hongwei Zhou, Yan He, Xiangqing Kong, Lianmin Chen","doi":"10.1002/imt2.268","DOIUrl":"https://doi.org/10.1002/imt2.268","url":null,"abstract":"<p>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.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"4 1","pages":""},"PeriodicalIF":23.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhiyong Du, Yingyuan Lu, Ying Ma, Yunxiao Yang, Wei Luo, Sheng Liu, Ming Zhang, Yong Wang, Lei Li, Chun Li, Wei Wang, Hai Gao
{"title":"The prognostic and therapeutic significance of polyunsaturated fatty acid-derived oxylipins in ST-segment elevation myocardial infarction","authors":"Zhiyong Du, Yingyuan Lu, Ying Ma, Yunxiao Yang, Wei Luo, Sheng Liu, Ming Zhang, Yong Wang, Lei Li, Chun Li, Wei Wang, Hai Gao","doi":"10.1002/imt2.266","DOIUrl":"https://doi.org/10.1002/imt2.266","url":null,"abstract":"<p>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 (<i>n</i> = 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%; <i>n</i> = 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.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"4 1","pages":""},"PeriodicalIF":23.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.266","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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