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Li S, An J, Zhang T, Chen G, Zhang Z, Guo Z, Dai Z, Cheng X, Cheng S, Xiong X, Wang N, Jiang G, Xu B, Lei H. Integration of network pharmacology, UHPLC-Q exactive orbitrap HRMS technique and metabolomics to elucidate the active ingredients and mechanisms of compound danshen pills in treating hypercholesterolemic rats. JOURNAL OF ETHNOPHARMACOLOGY 2025; 336:118759. [PMID: 39209003 DOI: 10.1016/j.jep.2024.118759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Revised: 08/18/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Hypercholesterolemia (HLC) was a key risk factor for cardiovascular disease (CVD) characterized by elevated cholesterol levels, particularly LDL. While traditional Chinese medicine preparations Compound Danshen Pills(CDP) has been clinically used for hypercholesterolemia and coronary heart disease, its specific therapeutic effect on HLC remains understudied, necessitating further investigation into its mechanisms. AIM OF THE STUDY The aim of this study was to explore the potential of CDP in treating HLC and elucidate its underlying mechanisms and active components. MATERIALS AND METHODS A hypercholesterolemic lipemia rat model induced by a high-fat diet was employed. Network pharmacology combined with UHPLC-Q exactive orbitrap HRMS technique was used to predict the active components, targets and mechanisms of CDP for HLC. Histological analysis and serum biochemical assays were used to assess the therapeutic effect of CDP and its main active ingredient Sa B on hypercholesterolemic lipemia rat model. Immunofluorescence assays and western blotting were used to verify the mechanism of CDP and Sa B in the treatment of HLC. Metabolomics approach was used to demonstrate that CDP and Sa B affected the metabolic profile of HLC. RESULTS Our findings demonstrated that both CDP and its main active ingredient Sa B significantly ameliorated hypercholesterolemic lipemic lesions, reducing levels of TC, LDL, AST, ALT, and ALP. Histological analysis revealed a decrease in lipid droplet accumulation and collagen fiber deposition in the liver, as well as reduced collagen fiber deposition in the aorta. Network pharmacology predicted potential targets such as PPARα and CYP27A1. Immunofluorescence assays and western blotting confirmed that CDP and Sa B upregulated the expression of Adipor1, PPARα and CYP27A1. Metabolomics analyses further indicated improvements in ABC transporters metabolic pathways, with differential metabolites such as riboflavin, taurine, and choline showed regression in levels after CDP treatment and riboflavin, L-Threonine, Thiamine, L-Leucine, and Adenosine showed improved expression after Sa B treatment. CONCLUSION CDP and Sa B have been shown to alleviate high-fat diet-induced hypercholesterolemia by activating the PPAR pathway and improving hepatic lipid metabolism. Our study demonstrated, for the first time, the complex mechanism of CDP, Sa B in the treatment of hypercholesterolemia at the protein and metabolic levels and provided a new reference that could elucidate the pharmacological effects of traditional Chinese medicine on hypercholesterolemia from multiple perspectives.
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Affiliation(s)
- Shanlan Li
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, 102400, China
| | - Jin An
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, 102400, China
| | - Tong Zhang
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, 102400, China
| | - Guangyun Chen
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, 102400, China
| | - Zixuan Zhang
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, 102400, China
| | - Zhuoqian Guo
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, 102400, China
| | - Ziqi Dai
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, 102400, China
| | - Xuehao Cheng
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, 102400, China
| | - Sijin Cheng
- School of Nursing, Beijing University of Chinese Medicine, Beijing, 102488, China
| | | | - Nan Wang
- Aimin Pharmaceutical Group, Henan, 463500, China
| | | | - Bing Xu
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, 102400, China.
| | - Haimin Lei
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, 102400, China.
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Yin H, Duo H, Li S, Qin D, Xie L, Xiao Y, Sun J, Tao J, Zhang X, Li Y, Zou Y, Yang Q, Yang X, Hao Y, Li B. Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives. J Adv Res 2024:S2090-1232(24)00560-5. [PMID: 39647635 DOI: 10.1016/j.jare.2024.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2024] [Revised: 10/12/2024] [Accepted: 12/03/2024] [Indexed: 12/10/2024] Open
Abstract
BACKGROUND Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such as network module analysis, knowledge graph, drug repurposing, cell marker discovery, trajectory analysis, and cell communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically and thoroughly mining the biological information within DEGs remain lacking. AIM OF REVIEW This review aims to provide an overview of essential concepts and methodologies for the biological interpretation of DEGs, enhancing the contextual understanding. It also addresses the current limitations and future perspectives of these approaches, highlighting their broad applications in deciphering the molecular mechanism of complex diseases and phenotypes. To assist users in extracting insights from extensive datasets, especially various DEG lists, we developed DEGMiner (https://www.ciblab.net/DEGMiner/), which integrates over 300 easily accessible databases and tools. KEY SCIENTIFIC CONCEPTS OF REVIEW This review offers strong support and guidance for exploring DEGs, and also will accelerate the discovery of hidden biological insights within genomes.
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Affiliation(s)
- Huachun Yin
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China; Department of Neurosurgery, Xinqiao Hospital, The Army Medical University, Chongqing 400037, PR China; Department of Neurobiology, Chongqing Key Laboratory of Neurobiology, The Army Medical University, Chongqing 400038, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China
| | - Song Li
- Department of Neurosurgery, Xinqiao Hospital, The Army Medical University, Chongqing 400037, PR China
| | - Dan Qin
- Department of Biology, College of Science, Northeastern University, Boston, MA 02115, USA
| | - Lingling Xie
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China
| | - Yingxue Xiao
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China
| | - Jing Sun
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China
| | - Jingxin Tao
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China
| | - Xiaoxi Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China
| | - Yinghong Li
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, PR China
| | - Yue Zou
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China
| | - Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, PR China
| | - Xian Yang
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China.
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China.
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Wang C, Zhang J, Gao F, Zheng M, Fu X, Yang K. Investigating the effects of COVID-19 on sperm in male smokers: A prospective integrated proteomic and metabolomic study. Reprod Toxicol 2024; 130:108734. [PMID: 39406274 DOI: 10.1016/j.reprotox.2024.108734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2024] [Revised: 09/28/2024] [Accepted: 10/11/2024] [Indexed: 10/18/2024]
Abstract
Notable variations in semen parameters among non-smoking males have been documented post-COVID-19 pandemic. The role of smoking as a significant contributing factor to male infertility has been substantiated. Does the combined effect of smoking and SARS-CoV-2 infection impact male reproductive function? A prospective descriptive cohort study was performed using data from 90 smoking and 90 non-smoking males before and after coronavirus infection in a single center over a period of 3 months. Semen samples were collected before and within 15 days after COVID-19 infection, ensuring no more than three months elapsed between the two collections. The semen parameters evaluated included volume, concentration, progressive motility, normal morphology, and DNA fragmentation rate. Proteomic and metabolomic studies were further used to explore the differences between groups. Both non-smokers and smokers exhibited a marked reduction in sperm concentration, progressive motility, and normal morphology rate. Additionally, an increase in sperm DNA fragmentation index was noted for non-smokers and smokers. In the non-smoking group, dysregulation proteins including SEMG1, SEMG2 and DNAH5, and metabolites including L-glutamine, cis-9-Palmitoleic acid and Linoleamide were observed. In smokers, dysregulation proteins including SMCP, ROPN1B and IZUMO4, alongside metabolites including carnitine, gamma-Glutamylglutamic acid, and hypoxanthine were found. Comparative analysis between smoking and non-smoking patients post-COVID-19 also revealed significant differences in semen concentration, morphology and sperm DNA fragmentation rate. Dysregulated proteins including HSPA5, HSPA2 and PGK2, and metabolites such as acetylcarnitine, oxaloacetate and nicotinate were associated with impaired sperm function. Our study demonstrates that the virus also significantly compromises sperm quality in smoking males, who experience more pronounced declines post-infection compared to their non-smoking counterparts. This research underscores the necessity for comprehensive fertility assessments for smoking males after recovering from COVID-19.
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Affiliation(s)
- ChengLu Wang
- Center for Reproductive Medicine, Department of Reproductive Endocrinology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang 310014, China
| | - JiaCheng Zhang
- Department of Otolaryngology, Hangzhou Cancer Hospital, Hangzhou, Zhejiang 310005, China
| | - Fang Gao
- Center for Reproductive Medicine, Department of Reproductive Endocrinology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang 310014, China
| | - Min Zheng
- Center for Reproductive Medicine, Department of Reproductive Endocrinology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang 310014, China
| | - XiaoHua Fu
- Center for Reproductive Medicine, Department of Reproductive Endocrinology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang 310014, China.
| | - KeBing Yang
- Urology & Nephrology Center, Department of Urology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang 310014, China.
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Zhang T, Fu W, Zhang H, Li J, Xing B, Cai Y, Zhang M, Liu X, Qi C, Qian L, Hu X, Zhu H, Yang S, Zhang M, Liu J, Li G, Li Y, Xiang R, Qi Z, Hu J, Li Y, Zou C, Wang Q, Jin X, Pang R, Li P, Liu J, Zhang Y, Wang Z, Zhu ZJ, Shan B, Yuan J. Spermidine mediates acetylhypusination of RIPK1 to suppress diabetes onset and progression. Nat Cell Biol 2024; 26:2099-2114. [PMID: 39511379 DOI: 10.1038/s41556-024-01540-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 09/19/2024] [Indexed: 11/15/2024]
Abstract
It has been established that N-acetyltransferase (murine NAT1 (mNAT1) and human NAT2 (hNAT2)) mediates insulin sensitivity in type 2 diabetes. Here we show that mNAT1 deficiency leads to a decrease in cellular spermidine-a natural polyamine exhibiting health-protective and anti-ageing effects-but understanding of its mechanism is limited. We identify that mNAT1 and hNAT2 modulate a type of post-translational modification involving acetylated spermidine, which we name acetylhypusination, on receptor-interacting serine/threonine-protein kinase 1 (RIPK1)-a key regulator of inflammation and cell death. Spermidine supplementation decreases RIPK1-mediated cell death and diabetic phenotypes induced by NAT1 deficiency in vivo. Furthermore, insulin resistance and diabetic kidney disease mediated by vascular pathology in NAT1-deficient mice can be blocked by inhibiting RIPK1. Finally, we demonstrate a decrease in spermidine and activation of RIPK1 in the vascular tissues of human patients with diabetes. Our study suggests a role for vascular pathology in diabetes onset and progression and identifies the inhibition of RIPK1 kinase as a potential therapeutic approach for the treatment of type 2 diabetes.
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Affiliation(s)
- Tian Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Weixin Fu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- Nankai University, Tianjin, China
| | - Haosong Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jianlong Li
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Beizi Xing
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yuping Cai
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Mengmeng Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Xuheng Liu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Chunting Qi
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Lihui Qian
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Xinbo Hu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Hua Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Shuailong Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Min Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jianping Liu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Ganquan Li
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yang Li
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Rong Xiang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Zhengqiang Qi
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Junhao Hu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Ying Li
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Chengyu Zou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- Shanghai Key Laboratory of Aging Studies, Shanghai, China
| | - Qin Wang
- Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Renji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xia Jin
- Department of Anesthesiology, Key Laboratory of the Ministry of Education, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rui Pang
- Department of Anesthesiology, Key Laboratory of the Ministry of Education, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Peiying Li
- Department of Anesthesiology, Key Laboratory of the Ministry of Education, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junli Liu
- Shanghai Diabetes Institute, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yaoyang Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- Shanghai Key Laboratory of Aging Studies, Shanghai, China
| | - Zhaoyin Wang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, China.
| | - Bing Shan
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
| | - Junying Yuan
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, China.
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Zhao F, Tie N, Kwok LY, Ma T, Wang J, Man D, Yuan X, Li H, Pang L, Shi H, Ren S, Yu Z, Shen X, Li H, Zhang H. Baseline gut microbiome as a predictive biomarker of response to probiotic adjuvant treatment in gout management. Pharmacol Res 2024; 209:107445. [PMID: 39396767 DOI: 10.1016/j.phrs.2024.107445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Revised: 09/30/2024] [Accepted: 09/30/2024] [Indexed: 10/15/2024]
Abstract
Gout is characterized by dysregulation of uric acid (UA) metabolism, and the gut microbiota may serve as a regulatory target. This two-month randomized, double-blind, placebo-controlled trial aimed to investigate the additional benefits of coadministering Probio-X alongside febuxostat. A total of 160 patients with gout were randomly assigned to either the probiotic group (n = 120; Probio-X [3 × 1010 CFU/day] with febuxostat) or the placebo group (n = 40; placebo material with febuxostat). Coadministration of Probio-X significantly decreased serum UA levels and the rate of acute gout attacks (P < 0.05). Based on achieving a target sUA level (360 μmol/L) after the intervention, the probiotic group was further subdivided into probiotic-responsive (ProA; n = 54) and probiotic-unresponsive (ProB; n = 66) subgroups. Post-intervention clinical indicators, metagenomic, and metabolomic changes in the ProB and placebo groups were similar, but differed from those in the ProA group, which exhibited significantly lower levels of acute gout attack, gout impact score, serum indicators (UA, XOD, hypoxanthine, and IL-1β), and fecal gene abundances of UA-producing pathways (KEGG orthologs of K13479 and K01487; gut metabolic modules for formate conversion and lactose and galactose degradation). Additionally, the ProA group showed significantly higher levels (P < 0.05) of gut SCFAs-producing bacteria and UA-related metabolites (xanthine, hypoxanthine, bile acids) after the intervention. Finally, we established a gout metagenomic classifier to predict probiotic responsiveness based on subjects' baseline gut microbiota composition. Our results indicate that probiotic-driven therapeutic responses are highly individual, with the probiotic-responsive cohort benefitting significantly from probiotic coadministration.
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Affiliation(s)
- Feiyan Zhao
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China; Key Laboratory of Dairy Products Processing, Ministry of Agriculture and Rural Affairs, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China; Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Ning Tie
- Department of Rheumatology and Immunology, The Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, Hohhot, Inner Mongolia, China
| | - Lai-Yu Kwok
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China; Key Laboratory of Dairy Products Processing, Ministry of Agriculture and Rural Affairs, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China; Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Teng Ma
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China; Key Laboratory of Dairy Products Processing, Ministry of Agriculture and Rural Affairs, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China; Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Jing Wang
- Department of Rheumatology and Immunology, The Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, Hohhot, Inner Mongolia, China
| | - Dafu Man
- Department of Rheumatology and Immunology, The Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, Hohhot, Inner Mongolia, China
| | - Xiangzheng Yuan
- Physical examination center, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia, China
| | - Huiyun Li
- Department of Rheumatology, Inner Mongolia People's Hospital, Hohhot, Inner Mongolia, China
| | - Lixia Pang
- Department of Rheumatology and Immunology, Hulunbuir People's Hospital, Hohhot, Inner Mongolia, China
| | - Hui Shi
- Department of Rheumatology and Immunology, Inner Mongolia Baogang Hospital, Baotou, Inner Mongolia, China
| | - Shuiming Ren
- Department of Rheumatology and Immunology, Ordos School of Clinical Medicine, Inner Mongolia Medical University, Ordos, Inner Mongolia, China
| | - Zhongjie Yu
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China; Key Laboratory of Dairy Products Processing, Ministry of Agriculture and Rural Affairs, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China; Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Xin Shen
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China; Key Laboratory of Dairy Products Processing, Ministry of Agriculture and Rural Affairs, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China; Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Hongbin Li
- Department of Rheumatology and Immunology, The Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia Key Laboratory for Pathogenesis and Diagnosis of Rheumatic and Autoimmune Diseases, Hohhot, Inner Mongolia, China.
| | - Heping Zhang
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China; Key Laboratory of Dairy Products Processing, Ministry of Agriculture and Rural Affairs, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China; Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China.
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Wang X, Liang S, Yang W, Yu K, Liang F, Zhao B, Zhu X, Zhou C, Mur LAJ, Roberts JA, Zhang J, Zhang X. MetMiner: A user-friendly pipeline for large-scale plant metabolomics data analysis. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2024; 66:2329-2345. [PMID: 39254487 PMCID: PMC11583839 DOI: 10.1111/jipb.13774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 07/26/2024] [Accepted: 08/16/2024] [Indexed: 09/11/2024]
Abstract
The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user-friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large-scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user-friendly, full-functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large-scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant-specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co-expression network analysis strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.
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Affiliation(s)
- Xiao Wang
- State Key Laboratory of Crop Stress Adaptation and Improvement, Henan Joint International Laboratory for Crop Multi-Omics Research, School of Life Sciences, Henan University, Kaifeng, 475004, China
| | - Shuang Liang
- State Key Laboratory of Crop Stress Adaptation and Improvement, Henan Joint International Laboratory for Crop Multi-Omics Research, School of Life Sciences, Henan University, Kaifeng, 475004, China
| | - Wenqi Yang
- State Key Laboratory of Crop Stress Adaptation and Improvement, Henan Joint International Laboratory for Crop Multi-Omics Research, School of Life Sciences, Henan University, Kaifeng, 475004, China
| | - Ke Yu
- State Key Laboratory of Crop Stress Adaptation and Improvement, Henan Joint International Laboratory for Crop Multi-Omics Research, School of Life Sciences, Henan University, Kaifeng, 475004, China
| | - Fei Liang
- State Key Laboratory of Crop Stress Adaptation and Improvement, Henan Joint International Laboratory for Crop Multi-Omics Research, School of Life Sciences, Henan University, Kaifeng, 475004, China
| | - Bing Zhao
- State Key Laboratory of Crop Stress Adaptation and Improvement, Henan Joint International Laboratory for Crop Multi-Omics Research, School of Life Sciences, Henan University, Kaifeng, 475004, China
| | - Xiang Zhu
- Thermo Fisher Scientific, Shanghai, 201206, China
| | - Chao Zhou
- Waters Technologies Shanghai Ltd, Shanghai, 201206, China
| | - Luis A J Mur
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3FL, UK
| | - Jeremy A Roberts
- Faculty of Science and Engineering, School of Biological & Marine Sciences, University of Plymouth, PL4 8AA, UK
| | - Junli Zhang
- State Key Laboratory of Crop Stress Adaptation and Improvement, Henan Joint International Laboratory for Crop Multi-Omics Research, School of Life Sciences, Henan University, Kaifeng, 475004, China
| | - Xuebin Zhang
- State Key Laboratory of Crop Stress Adaptation and Improvement, Henan Joint International Laboratory for Crop Multi-Omics Research, School of Life Sciences, Henan University, Kaifeng, 475004, China
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7
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Yan J, Zhang X, Wang H, Jia X, Wang R, Wu S, Zhu ZJ, Tan M, Horng T. Macrophage NRF1 promotes mitochondrial protein turnover via the ubiquitin proteasome system to limit mitochondrial stress and inflammation. Cell Rep 2024; 43:114780. [PMID: 39325625 DOI: 10.1016/j.celrep.2024.114780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/09/2024] [Accepted: 09/05/2024] [Indexed: 09/28/2024] Open
Abstract
Macrophage elaboration of inflammatory responses is dynamically regulated, shifting from acute induction to delayed suppression during the course of infection. Here, we show that such regulation of inflammation is modulated by dynamic shifts in metabolism. In macrophages exposed to the bacterial product lipopolysaccharide (LPS), an initial induction of protein biosynthesis is followed by compensatory induction of the transcription factor nuclear factor erythroid 2-like 1 (NRF1), leading to increased flux through the ubiquitin proteasome system (UPS). A major target of NRF1-mediated UPS flux is the mitochondrial proteome, and in the absence of NRF1, ubiquitinated mitochondrial proteins accumulate to trigger severe mitochondrial stress. Such mitochondrial stress engages the integrated stress response-ATF4 axis, which limits mitochondrial translation to attenuate mitochondrial stress but amplifies inflammatory responses to augment susceptibility to septic shock. Therefore, NRF1 mediates a dynamic regulation of mitochondrial proteostasis in inflammatory macrophages that contributes to curbing inflammatory responses.
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Affiliation(s)
- Jiawei Yan
- School of Life Sciences and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xin Zhang
- School of Life Sciences and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Huiying Wang
- School of Life Sciences and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xinglong Jia
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Ruohong Wang
- University of Chinese Academy of Sciences, Beijing 100049, China; Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
| | - Shuangyang Wu
- Gregor Mendel Institute of Molecular Plant Biology, Austrian Academy of Sciences, Dr. Bohr-Gasse 3, 1030 Vienna, Austria
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China; Shanghai Key Laboratory of Aging Studies, Shanghai 201210, China
| | - Minjia Tan
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Tiffany Horng
- School of Life Sciences and Technology, ShanghaiTech University, Shanghai 201210, China.
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8
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Zhou M, Wu J, Wu L, Sun X, Chen C, Huang L. The utilization of N-acetylgalactosamine and its effect on the metabolism of amino acids in Erysipelotrichaceae strain. BMC Microbiol 2024; 24:397. [PMID: 39379811 PMCID: PMC11462708 DOI: 10.1186/s12866-024-03505-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 09/06/2024] [Indexed: 10/10/2024] Open
Abstract
BACKGROUND The metabolism of gut microbiota produces bioactive metabolites that modulate host physiology and promote self-growth. Erysipelotrichaceae is one of the most common anaerobic microorganism families in the gut, which has been discovered to play a vital role in host metabolic disorders and inflammatory diseases. Our previous study found that N-acetylgalactosamine (GalNAc) in caecal content of pigs significantly affected the abundance of Erysipelotrichaceae strains. However, it remains unknown how GalNAc feeding in vitro culture affects the expression levels of genes in the GalNAc metabolic pathway and the concentrations of intermediate metabolites in the Erysipelotrichaceae strain. Whether GalNAc feeding should influence the metabolism of other nutrients, such as amino acids, remains unrevealed. RESULTS In this study, whole-genome sequence, transcriptome, and metabolome data were analyzed to assess the utilization of a Erysipelotrichaceae strain on GalNAc. The results showed the presence of a complete GalNAc catabolism pathway in the genome of this Erysipelotrichaceae strain. GalNAc feeding to this Erysipelotrichaceae strain significantly changed the expression levels of genes involved in glycolysis and tricarboxylic acid (TCA) cycle. Meanwhile, the concentrations of lactate, pyruvate, citrate, succinate and malate from the glycolysis and TCA cycle were significantly increased. In addition, transcriptome analysis indicated that the genes involved in the metabolism of amino acids were affected by GalNAc, including lysA (a gene involved in lysine biosynthesis) that was significantly down-regulated. The intracellular concentrations of 14 amino acids in the Erysipelotrichaceae strain were significantly increased after feeding GalNAc. CONCLUSIONS Our findings comfirmed and extended our previous works that demonstrated the utilization of GalNAc by Erysipelotrichaceae strain, and explained the possible mechanism of GalNAc affecting the abundance of Erysipelotrichaceae strain in vitro.
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Affiliation(s)
- Mengqing Zhou
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Jiangxi Agricultural University, Nanchang, 330045, Jiangxi Province, PR China
| | - Jinyuan Wu
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Jiangxi Agricultural University, Nanchang, 330045, Jiangxi Province, PR China
| | - Lin Wu
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Jiangxi Agricultural University, Nanchang, 330045, Jiangxi Province, PR China
| | - Xiao Sun
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Jiangxi Agricultural University, Nanchang, 330045, Jiangxi Province, PR China
| | - Congying Chen
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Jiangxi Agricultural University, Nanchang, 330045, Jiangxi Province, PR China.
| | - Lusheng Huang
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Jiangxi Agricultural University, Nanchang, 330045, Jiangxi Province, PR China.
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9
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Shen J, Qiao L. Proteomic and metabolic analysis of Moorella thermoacetica-g-C 3N 4 nanocomposite system for artificial photosynthesis. Talanta 2024; 278:126479. [PMID: 38941811 DOI: 10.1016/j.talanta.2024.126479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 06/30/2024]
Abstract
Artificial photosynthesis by microbe-semiconductor biohybrid systems has been demonstrated as a valuable strategy in providing sustainable energy and in carbon fixation. However, most of the developed biohybrid systems for light harvesting employ heavy metal materials, especially cadmium sulfide (CdS), which normally cause environmental pollution and restrict the widespread of the systems. Herein, we constructed an environmentally friendly biohybirid system based on a typical acetogenic bacteria, Moorella thermoacetica, coupling with a carbon-based semiconductor, graphitic carbon nitride (g-C3N4), to realize light-driven carbon fixation. The proposed biohybrid system displayed outstanding acetate productivity with a quantum yield of 2.66 ± 0.43 %. Non-targeted proteomic analysis indicated that the physiological activity of the bacteria was improved, coupling with the non-toxic material. We further proposed the mechanisms of energy generation, electron transfer and CO2 fixation of the irradiated biohybrid system by proteomic and metabolomic characterization. With the photoelectron generated in g-C3N4 under illumination, CO2 is finally converted to acetate via the Wood-Ljungdahl pathway (WLP). Other associated pathways were also proved to be activated, providing extra energy or substrates for acetate production. The study reveals that the future focus of the development of biohybrid systems for light harvesting can be on the metal-free biocompatible material, which can activate the expression of the key enzymes involved in the electron transfer and carbon metabolism under light irradiation.
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Affiliation(s)
- Jiayuan Shen
- Department of Chemistry, and Minhang Hospital, Fudan University, Shanghai, 200000, China
| | - Liang Qiao
- Department of Chemistry, and Minhang Hospital, Fudan University, Shanghai, 200000, China.
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10
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Nguyen QH, Nguyen H, Oh EC, Nguyen T. Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review. Brief Bioinform 2024; 25:bbae498. [PMID: 39397425 PMCID: PMC11471905 DOI: 10.1093/bib/bbae498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 09/03/2024] [Accepted: 10/02/2024] [Indexed: 10/15/2024] Open
Abstract
Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.
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Affiliation(s)
- Quang-Huy Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, United States
| | - Ha Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, United States
| | - Edwin C Oh
- Department of Internal Medicine, UNLV School of Medicine, University of Nevada, Las Vegas, NV 89154, United States
| | - Tin Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, United States
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11
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Zhao M, Che Y, Gao Y, Zhang X. Application of multi-omics in the study of traditional Chinese medicine. Front Pharmacol 2024; 15:1431862. [PMID: 39309011 PMCID: PMC11412821 DOI: 10.3389/fphar.2024.1431862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 08/28/2024] [Indexed: 09/25/2024] Open
Abstract
Traditional Chinese medicine (TCM) is playing an increasingly important role in disease treatment due to the advantages of multi-target, multi-pathway mechanisms, low adverse reactions and cost-effectiveness. However, the complexity of TCM system poses challenges for research. In recent years, there has been a surge in the application of multi-omics integrated research to explore the active components and treatment mechanisms of TCM from various perspectives, which aids in advancing TCM's integration into clinical practice and holds immense importance in promoting modernization. In this review, we discuss the application of proteomics, metabolomics, and mass spectrometry imaging in the study of composition, quality evaluation, target identification, and mechanism of action of TCM based on existing literature. We focus on the workflows and applications of multi-omics based on mass spectrometry in the research of TCM. Additionally, potential research ideas for future exploration in TCM are outlined. Overall, we emphasize the advantages and prospects of multi-omics based on mass spectrometry in the study of the substance basis and mechanism of action of TCM. This synthesis of methodologies holds promise for enhancing our understanding of TCM and driving its further integration into contemporary medical practices.
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Affiliation(s)
| | | | | | - Xiangyang Zhang
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
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12
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Wang X, Strobel M, Aron AT, Phelan VV, Acharya DD, Brown CJ, Clevenger K, Hu J, Kretsch A, Mahood EH, Menegatti C, Xiong Q, Wang M. Network Topology Evaluation and Transitive Alignments for Molecular Networking. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:2165-2175. [PMID: 39133821 PMCID: PMC11516331 DOI: 10.1021/jasms.4c00208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Untargeted tandem mass spectrometry (MS/MS) is an essential technique in modern analytical chemistry, providing a comprehensive snapshot of chemical entities in complex samples and identifying unknowns through their fragmentation patterns. This high-throughput approach generates large data sets that can be challenging to interpret. Molecular Networks (MNs) have been developed as a computational tool to aid in the organization and visualization of complex chemical space in untargeted mass spectrometry data, thereby supporting comprehensive data analysis and interpretation. MNs group related compounds with potentially similar structures from MS/MS data by calculating all pairwise MS/MS similarities and filtering these connections to produce a MN. Such networks are instrumental in metabolomics for identifying novel metabolites, elucidating metabolic pathways, and even discovering biomarkers for disease. While MS/MS similarity metrics have been explored in the literature, the influence of network topology approaches on MN construction remains unexplored. This manuscript introduces metrics for evaluating MN construction, benchmarks state-of-the-art approaches, and proposes the Transitive Alignments approach to improve MN construction. The Transitive Alignment technique leverages the MN topology to realign MS/MS spectra of related compounds that differ by multiple structural modifications. Combining this Transitive Alignments approach with pseudoclique finding, a method for identifying highly connected groups of nodes in a network, resulted in more complete and higher-quality molecular families. Finally, we also introduce a targeted network construction technique called induced transitive alignments where we demonstrate effectiveness on a real world natural product discovery application. We release this transitive alignment technique as a high-throughput workflow that can be used by the wider research community.
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Affiliation(s)
- Xianghu Wang
- Department of Computer Science and Engineering, University of California Riverside, 900 University Ave., Riverside, California 92521, United States
| | - Michael Strobel
- Department of Computer Science and Engineering, University of California Riverside, 900 University Ave., Riverside, California 92521, United States
| | - Allegra T Aron
- Department of Chemistry and Biochemistry, University of Denver, 2101 East Wesley Ave, Denver, Colorado 80210, United States
| | - Vanessa V Phelan
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Anschutz Medical Campus, 12850 E Montview Blvd, Aurora, Colorado 80045, United States
| | - Deepa D Acharya
- Biologicals Research and Development, Corteva Agriscience, 9330 Zionsville Rd, Indianapolis, Indiana 46268, United States
| | - Christopher J Brown
- Regulatory Science, Corteva Agriscience, 9330 Zionsville Rd, Indianapolis, Indiana 46268, United States
| | - Ken Clevenger
- Biologicals Research and Development, Corteva Agriscience, 9330 Zionsville Rd, Indianapolis, Indiana 46268, United States
| | - Jie Hu
- Data Science, Corteva Agriscience, 9330 Zionsville Rd, Indianapolis, Indiana 46268, United States
| | - Ashley Kretsch
- Biologicals Research and Development, Corteva Agriscience, 9330 Zionsville Rd, Indianapolis, Indiana 46268, United States
| | - Elizabeth H Mahood
- Data Science, Corteva Agriscience, 9330 Zionsville Rd, Indianapolis, Indiana 46268, United States
| | - Carla Menegatti
- Biologicals Research and Development, Corteva Agriscience, 9330 Zionsville Rd, Indianapolis, Indiana 46268, United States
| | - Quanbo Xiong
- Biologicals Research and Development, Corteva Agriscience, 9330 Zionsville Rd, Indianapolis, Indiana 46268, United States
| | - Mingxun Wang
- Department of Computer Science and Engineering, University of California Riverside, 900 University Ave., Riverside, California 92521, United States
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13
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Song Q, Hu C, Zhang X, Ji P, Li Y, Peng H, Zheng Y, Zhang H. Integrated multi-omics approaches reveal the neurotoxicity of triclocarban in mouse brain. ENVIRONMENT INTERNATIONAL 2024; 191:108987. [PMID: 39217723 DOI: 10.1016/j.envint.2024.108987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 08/12/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024]
Abstract
Triclocarban (TCC) is an antimicrobial ingredient that commonly incorporated in many household and personal care products, raising public concerns about its potential health risks. Previous research has showed that TCC could cross the blood-brain barrier, but to date our understanding of its potential neurotoxicity at human-relevant concentrations remains lacking. In this study, we observed anxiety-like behaviors in mice with continuous percutaneous exposure to TCC. Subsequently, we combined lipidomic, proteomic, and metabolic landscapes to investigate the underlying mechanisms of TCC-related neurotoxicity. The results showed that TCC exposure dysregulated the proteins involved in endocytosis and neurodegenerative disorders in mouse cerebrum. Brain energy homeostasis was also altered, as evidenced by the perturbation of pyruvate metabolism, TCA cycle, and oxidative phosphorylation, which in turn caused mitochondrial dysfunction. Meanwhile, the changing trends of sphingolipid signaling pathway and overproduction of mitochondrial reactive oxygen species (mROS) could enhance the neural apoptosis. The in vitro approach further demonstrated that TCC exposure promoted apoptosis, accompanied by the overproduction of mROS and alteration in the mitochondrial membrane potential in N2A cells. Together, dysregulated endocytosis, mROS-related mitochondrial dysfunction and neural cell apoptosis are considered to be crucial factors for TCC-induced neurotoxicity, which may contribute to the occurrence and development of neurodegenerative disorders. Our findings provide novel perspectives for the mechanisms of TCC-triggered neurotoxicity.
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Affiliation(s)
- Qian Song
- Department of Occupational and Environmental Health, School of Public Health, Qingdao University, Qingdao 266071, China
| | - Chengchen Hu
- Department of Occupational and Environmental Health, School of Public Health, Qingdao University, Qingdao 266071, China
| | - Xueying Zhang
- Department of Occupational and Environmental Health, School of Public Health, Qingdao University, Qingdao 266071, China
| | - Pengweilin Ji
- Department of Occupational and Environmental Health, School of Public Health, Qingdao University, Qingdao 266071, China
| | - Yansong Li
- Department of Occupational and Environmental Health, School of Public Health, Qingdao University, Qingdao 266071, China
| | - Hanyong Peng
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Yuxin Zheng
- Department of Occupational and Environmental Health, School of Public Health, Qingdao University, Qingdao 266071, China
| | - Hongna Zhang
- Department of Occupational and Environmental Health, School of Public Health, Qingdao University, Qingdao 266071, China.
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14
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Gan X, Li X, Cai Y, Yin B, Pan Q, Teng T, He Y, Tang H, Wang T, Li J, Zhu Z, Zhou X, Li J. Metabolic features of adolescent major depressive disorder: A comparative study between treatment-resistant depression and first-episode drug-naive depression. Psychoneuroendocrinology 2024; 167:107086. [PMID: 38824765 DOI: 10.1016/j.psyneuen.2024.107086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/12/2024] [Accepted: 05/21/2024] [Indexed: 06/04/2024]
Abstract
Major depressive disorder (MDD) is a psychiatric illness that can jeopardize the normal growth and development of adolescents. Approximately 40% of adolescent patients with MDD exhibit resistance to conventional antidepressants, leading to the development of Treatment-Resistant Depression (TRD). TRD is associated with severe impairments in social functioning and learning ability and an elevated risk of suicide, thereby imposing an additional societal burden. In this study, we conducted plasma metabolomic analysis on 53 adolescents diagnosed with first-episode drug-naïve MDD (FEDN-MDD), 53 adolescents with TRD, and 56 healthy controls (HCs) using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) and reversed-phase liquid chromatography-mass spectrometry (RPLC-MS). We established a diagnostic model by identifying differentially expressed metabolites and applying cluster analysis, metabolic pathway analysis, and multivariate linear support vector machine (SVM) algorithms. Our findings suggest that adolescent TRD shares similarities with FEDN-MDD in five amino acid metabolic pathways and exhibits distinct metabolic characteristics, particularly tyrosine and glycerophospholipid metabolism. Furthermore, through multivariate receiver operating characteristic (ROC) analysis, we optimized the area under the curve (AUC) and achieved the highest predictive accuracy, obtaining an AUC of 0.903 when comparing FEDN-MDD patients with HCs and an AUC of 0.968 when comparing TRD patients with HCs. This study provides new evidence for the identification of adolescent TRD and sheds light on different pathophysiologies by delineating the distinct plasma metabolic profiles of adolescent TRD and FEDN-MDD.
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Affiliation(s)
- Xieyu Gan
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuping Cai
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Bangmin Yin
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qiyuan Pan
- The First People's Hospital of Zaoyang City, Hubei, China
| | - Teng Teng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuqian He
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Han Tang
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ting Wang
- Department of Psychology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhengjiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China; Shanghai Key Laboratory of Aging Studies, Shanghai, China.
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Jinfang Li
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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15
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Lan Q, Li X, Fang J, Yu X, Wu ZE, Yang C, Jian H, Li F. Comprehensive biomarker analysis of metabolomics in different syndromes in traditional Chinese medical for prediabetes mellitus. Chin Med 2024; 19:114. [PMID: 39183283 PMCID: PMC11346218 DOI: 10.1186/s13020-024-00983-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 08/16/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Prediabetes mellitus (PreDM) is a high-risk state for developing type 2 diabetes mellitus (T2DM) and often goes undiagnosed, which is closely associated with obesity and characterized by insulin resistance that urgently needs to be treated. PURPOSE To obtain a better understanding of the biological processes associated with both "spleen-dampness" syndrome individuals and those with dysglycaemic control at its earliest stages, we performed a detailed metabolomic analysis of individuals with various early impairments in glycaemic control, the results can facilitate clinicians' decision making and benefit individuals at risk. METHODS According to the diagnostic criteria of TCM patterns and PreDM, patients were divided into 4 groups with 20 cases, patients with syndrome of spleen deficiency with dampness encumbrance and PreDM (PDMPXSK group), patients with syndrome of dampness-heat in the spleen and PreDM (PDMSRYP group), patients with syndrome of spleen deficiency with dampness encumbrance and normal blood glucose (NDMPXSK group), and patients with syndrome of dampness-heat in the spleen and normal blood glucose (NDMSRYP group). Plasma samples from patients were collected for clinical index assessment and untargeted metabolomics using liquid chromatography-mass spectrometry. RESULTS Among patients with the syndrome of spleen deficiency with dampness encumbrance (PXSK), those with PreDM (PDMPXSK group) had elevated levels of 2-hour post-load blood glucose (2-h PG), glycosylated hemoglobin (HbA1c), high-density lipoprotein cholesterol (HDL-C), and systolic blood pressure (SBP) than those in the normal blood glucose group (NDMPXSK group, P < 0.01). Among patients with the syndrome of dampness-heat in the spleen (SRYP), the levels of body mass index (BMI), fasting blood glucose (FBG), 2-h PG, HbA1c, and fasting insulin (FINS) were higher in the PreDM group (PDMSRYP group) than those in the normal blood glucose group (NDMSRYP group, P < 0.05). In both TCM syndromes, the plasma metabolomic profiles of PreDM patients were mainly discriminatory from the normal blood glucose controls of the same syndrome in the levels of lipid species, with the PXSK syndrome showing a more pronounced and broader spectrum of alterations than the SRYP syndrome. Changes associated with PreDM common to both syndromes included elevations in the levels of 27 metabolites which were mainly lipid species encompassing glycerophospholipids (GPs), diglycerides (DGs) and triglycerides (TGs), cholesterol and derivatives, and decreases in 5 metabolites consisting 1 DG, 1 TG, 2 N,N-dimethyl phosphatidylethanolamine (PE-NMe2) and iminoacetic acid. Correlation analysis identified significant positive correlations of 3α,7α,12α,25-Tetrahydroxy-5β-cholestane-24-one with more than one glycaemia-related indicators, whereas DG (20:4/20:5) and PC (20:3/14:0) were positively and PC (18:1/14:0) was inversely correlated with more than one lipid profile-related indicators. Based on the value of correlation coefficient, the top three correlative pairs were TG with PC (18:1/14:0) (r = - 0.528), TG with TG (14:0/22:4/22:5) (r = 0.521) and FINS with PE-NMe (15:0/22:4) (r = 0.52). CONCLUSION Our results revealed PreDM patients with different TCM syndromes were characterized by different clinical profiles. Common metabolite markers associated with PreDM shared by the two TCM syndromes were mainly lipid species encompassing GP, GL, cholesterol and derivatives. Our findings were in line with the current view that altered lipid metabolism is a conserved and early event of dysglycaemia. Our study also implied the possible involvement of perturbed bile acid homeostasis and dysregulated PE methylation during development of dysglycaemia.
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Affiliation(s)
- Qin Lan
- Jiangxi University of Traditional Chinese Medicine, Nanchang, 330004, China
- Outpatient Department, Hongdu Traditional Chinese Medicine Hospital Affiliated to Jiangxi University of Traditional Chinese Medicine, Nanchang, 330006, China
| | - Xue Li
- Department of Gastroenterology and Hepatology, Laboratory of Metabolomics and Drug-Induced Liver Injury, Frontiers Science Center for Disease-Related Molecular Network, and State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Jianhe Fang
- Medical Ancient Literature Teaching and Research Office, Jiangxi University of Traditional Chinese Medicine, Nanchang, 330004, China
| | - Xinyu Yu
- Discipline of Chinese and Western Integrative Medicine, Jiangxi University of Chinese Medicine, Nanchang, 330004, China
| | - Zhanxuan E Wu
- Department of Gastroenterology and Hepatology, Laboratory of Metabolomics and Drug-Induced Liver Injury, Frontiers Science Center for Disease-Related Molecular Network, and State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Caiyun Yang
- Endocrinology Department II, Hongdu Traditional Chinese Medicine Hospital Affiliated to Jiangxi University of Traditional Chinese Medicine, Nanchang, 330006, China
| | - Hui Jian
- Jiangxi University of Traditional Chinese Medicine, Nanchang, 330004, China.
| | - Fei Li
- Department of Gastroenterology and Hepatology, Laboratory of Metabolomics and Drug-Induced Liver Injury, Frontiers Science Center for Disease-Related Molecular Network, and State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
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16
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Deng Y, Yao Y, Wang Y, Yu T, Cai W, Zhou D, Yin F, Liu W, Liu Y, Xie C, Guan J, Hu Y, Huang P, Li W. An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles. Nat Commun 2024; 15:7136. [PMID: 39164279 PMCID: PMC11335749 DOI: 10.1038/s41467-024-51433-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 08/07/2024] [Indexed: 08/22/2024] Open
Abstract
Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present DeepMSProfiler, an explainable deep-learning-based method, enabling end-to-end analysis on raw metabolic signals with output of high accuracy and reliability. Using cross-hospital 859 human serum samples from lung adenocarcinoma, benign lung nodules, and healthy individuals, DeepMSProfiler successfully differentiates the metabolomic profiles of different groups (AUC 0.99) and detects early-stage lung adenocarcinoma (accuracy 0.961). Model flow and ablation experiments demonstrate that DeepMSProfiler overcomes inter-hospital variability and effects of unknown metabolites signals. Our ensemble strategy removes background-category phenomena in multi-classification deep-learning models, and the novel interpretability enables direct access to disease-related metabolite-protein networks. Further applying to lipid metabolomic data unveils correlations of important metabolites and proteins. Overall, DeepMSProfiler offers a straightforward and reliable method for disease diagnosis and mechanism discovery, enhancing its broad applicability.
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Affiliation(s)
- Yongjie Deng
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yao Yao
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
- Metabolic Innovation Platform, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yanni Wang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Tiantian Yu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
- Metabolic Innovation Platform, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Wenhao Cai
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Dingli Zhou
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Feng Yin
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wanli Liu
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yuying Liu
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chuanbo Xie
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jian Guan
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yumin Hu
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
- Metabolic Innovation Platform, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
| | - Peng Huang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
- Metabolic Innovation Platform, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
| | - Weizhong Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
- Sun Yat-Sen University School of Medicine, Sun Yat-Sen University, Shenzhen, China.
- Key Laboratory of Tropical Disease Control of Ministry of Education, Sun Yat-sen University, Guangzhou, China.
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Li X, Zhou Chen Y, Kalia A, Zhu H, Liu LP, Hassoun S. An Ensemble Spectral Prediction (ESP) model for metabolite annotation. Bioinformatics 2024; 40:btae490. [PMID: 39180771 PMCID: PMC11344591 DOI: 10.1093/bioinformatics/btae490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 06/25/2024] [Indexed: 08/26/2024] Open
Abstract
MOTIVATION A key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned identities. Two complementary computational approaches have emerged to address the annotation problem: mapping candidate molecules to spectra, and mapping query spectra to molecular candidates. In essence, the candidate molecule with the spectrum that best explains the query spectrum is recommended as the target molecule. Despite candidate ranking being fundamental in both approaches, limited prior works incorporated rank learning tasks in determining the target molecule. RESULTS We propose a novel machine learning model, Ensemble Spectral Prediction (ESP), for metabolite annotation. ESP takes advantage of prior neural network-based annotation models that utilize multilayer perceptron (MLP) networks and Graph Neural Networks (GNNs). Based on the ranking results of the MLP- and GNN-based models, ESP learns a weighting for the outputs of MLP and GNN spectral predictors to generate a spectral prediction for a query molecule. Importantly, training data is stratified by molecular formula to provide candidate sets during model training. Further, baseline MLP and GNN models are enhanced by considering peak dependencies through label mixing and multi-tasking on spectral topic distributions. When trained on the NIST 2020 dataset and evaluated on the relevant candidate sets from PubChem, ESP improves average rank by 23.7% and 37.2% over the MLP and GNN baselines, respectively, demonstrating performance gain over state-of-the-art neural network approaches. However, MLP approaches remain strong contenders when considering top five ranks. Importantly, we show that annotation performance is dependent on the training dataset, the number of molecules in the candidate set and candidate similarity to the target molecule. AVAILABILITY AND IMPLEMENTATION The ESP code, a trained model, and a Jupyter notebook that guide users on using the ESP tool is available at https://github.com/HassounLab/ESP.
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Affiliation(s)
- Xinmeng Li
- Department of Computer Science, Tufts University, Medford, MA, 02155, United States
| | - Yan Zhou Chen
- Department of Computer Science, Tufts University, Medford, MA, 02155, United States
| | - Apurva Kalia
- Department of Computer Science, Tufts University, Medford, MA, 02155, United States
| | - Hao Zhu
- Department of Computer Science, Tufts University, Medford, MA, 02155, United States
| | - Li-ping Liu
- Department of Computer Science, Tufts University, Medford, MA, 02155, United States
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA, 02155, United States
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA, 02155, United States
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Yang Y, Fang H, Xie Z, Ren F, Yan L, Zhang M, Xu G, Song Z, Chen Z, Sun W, Shan B, Zhu ZJ, Xu D. Yersinia infection induces glucose depletion and AMPK-dependent inhibition of pyroptosis in mice. Nat Microbiol 2024; 9:2144-2159. [PMID: 38844594 DOI: 10.1038/s41564-024-01734-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 04/04/2024] [Indexed: 08/09/2024]
Abstract
Nutritional status and pyroptosis are important for host defence against infections. However, the molecular link that integrates nutrient sensing into pyroptosis during microbial infection is unclear. Here, using metabolic profiling, we found that Yersinia pseudotuberculosis infection results in a significant decrease in intracellular glucose levels in macrophages. This leads to activation of the glucose and energy sensor AMPK, which phosphorylates the essential kinase RIPK1 at S321 during caspase-8-mediated pyroptosis. This phosphorylation inhibits RIPK1 activation and thereby restrains pyroptosis. Boosting the AMPK-RIPK1 cascade by glucose deprivation, AMPK agonists, or RIPK1-S321E knockin suppresses pyroptosis, leading to increased susceptibility to Y. pseudotuberculosis infection in mice. Ablation of AMPK in macrophages or glucose supplementation in mice is protective against infection. Thus, we reveal a molecular link between glucose sensing and pyroptosis, and unveil a mechanism by which Y. pseudotuberculosis reduces glucose levels to impact host AMPK activation and limit host pyroptosis to facilitate infection.
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Affiliation(s)
- Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hongwen Fang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhangdan Xie
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Fandong Ren
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Lingjie Yan
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Mengmeng Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Guifang Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Ziwen Song
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zezhao Chen
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Weimin Sun
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Bing Shan
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- Shanghai Key Laboratory of Aging Studies, Shanghai, China
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, China.
- State Key Laboratory of Chemical Biology, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
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Luo W, Chou L, Cui Q, Wei S, Zhang X, Guo J. High-efficiency effect-directed analysis (EDA) advancing toxicant identification in aquatic environments: Latest progress and application status. ENVIRONMENT INTERNATIONAL 2024; 190:108855. [PMID: 38945088 DOI: 10.1016/j.envint.2024.108855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 05/21/2024] [Accepted: 06/26/2024] [Indexed: 07/02/2024]
Abstract
Facing the great threats to ecosystems and human health posed by the continuous release of chemicals into aquatic environments, effect-directed analysis (EDA) has emerged as a powerful tool for identifying causative toxicants. However, traditional EDA shows problems of low-coverage, labor-intensive and low-efficiency. Currently, a number of high-efficiency techniques have been integrated into EDA to improve toxicant identification. In this review, the latest progress and current limitations of high-efficiency EDA, comprising high-coverage effect evaluation, high-resolution fractionation, high-coverage chemical analysis, high-automation causative peak extraction and high-efficiency structure elucidation, are summarized. Specifically, high-resolution fractionation, high-automation data processing algorithms and in silico structure elucidation techniques have been well developed to enhance EDA. While high-coverage effect evaluation and chemical analysis should be further emphasized, especially omics tools and data-independent mass acquisition. For the application status in aquatic environments, high-efficiency EDA is widely applied in surface water and wastewater. Estrogenic, androgenic and aryl hydrocarbon receptor-mediated activities are the most concerning, with causative toxicants showing the typical structural features of steroids and benzenoids. A better understanding of the latest progress and application status of EDA would be beneficial to further advance in the field and greatly support aquatic environment monitoring.
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Affiliation(s)
- Wenrui Luo
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Liben Chou
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Qinglan Cui
- Bluestar Lehigh Engineering Institute Co., Ltd., Lianyungang 222004, China
| | - Si Wei
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Jing Guo
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China; Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, China.
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20
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Jiang Y, Cai Y, Teng T, Wang X, Yin B, Li X, Yu Y, Liu X, Wang J, Wu H, He Y, Zhu ZJ, Zhou X. Dysregulations of amino acid metabolism and lipid metabolism in urine of children and adolescents with major depressive disorder: a case-control study. Psychopharmacology (Berl) 2024; 241:1691-1703. [PMID: 38605232 DOI: 10.1007/s00213-024-06590-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 04/08/2024] [Indexed: 04/13/2024]
Abstract
RATIONALE The mechanisms underlying major depressive disorder (MDD) in children and adolescents are unclear. Metabolomics has been utilized to capture metabolic signatures of various psychiatric disorders; however, urinary metabolic profile of MDD in children and adolescents has not been studied. OBJECTIVES We analyzed urinary metabolites in children and adolescents with MDD to identify potential biomarkers and metabolic signatures. METHODS Here, liquid chromatography-mass spectrometry was used to profile metabolites in urine samples from 192 subjects, comprising 80 individuals with antidepressant-naïve MDD (AN-MDD), 37 with antidepressant-treated MDD (AT-MDD) and 75 healthy controls (HC). We performed orthogonal partial least squares discriminant analysis to identify differential metabolites and employed logistic regression and receiver operating characteristic analysis to establish a diagnostic panel. RESULTS In total, 143 and 71 differential metabolites were identified in AN-MDD and AT-MDD, respectively. These were primarily linked to lipid metabolism, molecular transport, and small molecule biochemistry. AN-MDD additionally exhibited dysregulated amino acid metabolism. Compared to HC, a diagnostic panel of seven metabolites displayed area under the receiver operating characteristic curves of 0.792 for AN-MDD, 0.828 for AT-MDD, and 0.799 for all MDD. Furthermore, the urinary metabolic profiles of children and adolescents with MDD significantly differed from those of adult MDD. CONCLUSIONS Our research suggests dysregulated amino acid metabolism and lipid metabolism in the urine of children and adolescents with MDD, similar to results in plasma metabolomics studies. This contributes to the comprehension of mechanisms underlying children and adolescents with MDD.
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Affiliation(s)
- Yuanliang Jiang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuping Cai
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Teng Teng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaolin Wang
- Health Management Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bangmin Yin
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ying Yu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xueer Liu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Wang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongyan Wu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuqian He
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, China.
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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21
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Li X, Li J, Yu F, Feng X, Luo Y, Liu Z, Zhao T, Xia J. The Untargeted Metabolomics Reveals Differences in Energy Metabolism in Patients with Different Subtypes of Ischemic Stroke. Mol Neurobiol 2024; 61:5308-5319. [PMID: 38183570 DOI: 10.1007/s12035-023-03884-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 12/13/2023] [Indexed: 01/08/2024]
Abstract
AIMS Ischemic stroke (IS) is the most common subtype of stroke. The risk factors and pathogenesis of IS are complex and varied due to different subtypes. Therefore, we used metabolomics technology to investigate the biomarkers and potential pathophysiological mechanisms of different subtypes of IS. METHODS We included 126 IS patients and divided them into two groups based on the TOAST classification: large-artery atherosclerosis (LAA) group (n = 87) and small-vessel occlusion (SVO) group (n = 39). Plasma metabolomics analysis was performed using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) to identify metabolic profiles in LAA and SVO subtype IS patients and to determine metabolic differences between patients with the two subtypes of IS. RESULTS We identified 26 differential metabolites between LAA and SVO subtype IS. A multiple prediction model based on the plasm metabolites had good predictive ability for IS subtyping (AUC = 0.822, accuracy = 77.8%), with 12,13-DHOME being the most important differential metabolite in the model. The differential metabolic pathways between the two subtypes of IS patients included tricarboxylic acid (TCA) cycle, alanine, aspartate and glutamate metabolism, and pyruvate metabolism, mainly focused on energy metabolism. CONCLUSION 12,13-DHOME emerged as the primary discriminatory metabolite between LAA and SVO subtypes of IS. In LAA subtype IS patients, energy metabolism, encompassing pyruvate metabolism and the TCA cycle, exhibited lower activity levels when compared to patients with the SVO subtype IS. The utilization of targeted metabolomics holds the potential to improve diagnostic accuracy for distinguishing stroke subtypes.
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Affiliation(s)
- Xi Li
- Department of Neurology, Xiangya Hospital, Central South University, 87# Xiangya Road, Changsha, 410008, Hunan, China
| | - Jiaxin Li
- Department of Neurology, Xiangya Hospital, Central South University, 87# Xiangya Road, Changsha, 410008, Hunan, China
| | - Fang Yu
- Department of Neurology, Xiangya Hospital, Central South University, 87# Xiangya Road, Changsha, 410008, Hunan, China
| | - Xianjing Feng
- Department of Neurology, Xiangya Hospital, Central South University, 87# Xiangya Road, Changsha, 410008, Hunan, China
| | - Yunfang Luo
- Department of Neurology, Xiangya Hospital, Central South University, 87# Xiangya Road, Changsha, 410008, Hunan, China
| | - Zeyu Liu
- Department of Neurology, Xiangya Hospital, Central South University, 87# Xiangya Road, Changsha, 410008, Hunan, China
| | - Tingting Zhao
- Department of Neurology, Xiangya Hospital, Central South University, 87# Xiangya Road, Changsha, 410008, Hunan, China
| | - Jian Xia
- Department of Neurology, Xiangya Hospital, Central South University, 87# Xiangya Road, Changsha, 410008, Hunan, China.
- Clinical Research Center for Cerebrovascular Disease of Hunan Province, Central South University, Changsha, Hunan, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China.
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Zhuang X, Li M, Mi L, Zhang X, Pu J, He G, Zhang L, Yu H, Yao L, Chen H, Ji Y, Zuo C, Su Y, Gan Y, Hao X, Zhang Y, Chen X, Wen F. Molecular Responses of Anti-VEGF Therapy in Neovascular Age-Related Macular Degeneration: Integrative Insights From Multi-Omics and Clinical Imaging. Invest Ophthalmol Vis Sci 2024; 65:24. [PMID: 39140961 PMCID: PMC11328887 DOI: 10.1167/iovs.65.10.24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 06/05/2024] [Indexed: 08/15/2024] Open
Abstract
Purpose The purpose of this study was to investigate the molecular mechanisms underlying anti-vascular endothelial growth factor (anti-VEGF) efficacy and response variability in neovascular age-related macular degeneration (nAMD) using longitudinal proteomic and metabolomic analysis alongside three-dimensional lesion measurements. Methods In this prospective study, 54 treatment-naive patients with nAMD underwent "3+ pro re nata" (3+PRN) anti-VEGF regimens followed for at least 12 weeks. Aqueous humors were collected pre- and post-treatment for proteomic and metabolomic analysis. Three-dimensional optical coherence tomography (OCT) and OCT angiography assessed different types of nAMD lesion volumes and areas. Results There were 1350 proteins and 1268 metabolites that were identified in aqueous humors, with 301 proteins and 353 metabolites significantly altered during anti-VEGF treatment, enriched in pathways of angiogenesis, energy metabolism, signal transduction, and neurofunctional regulation. Sixty-seven changes of (Δ) molecules significantly correlated with at least one type of ΔnAMD lesion. Notably, proteins FGA, TALDO1, and ASPH significantly decreased during treatment, with their reductions correlating with greater lesion regression in at least two lesion types. Conversely, despite that YIPF3 also showed significant downregulation, its decrease was associated with poorer regression in total nAMD lesion and subretinal hyper-reflective material. Conclusions This study identifies FGA, TALDO1, and ASPH as potential key molecules in the efficacy of anti-VEGF therapy, whereas YIPF3 may be a key factor in poor response. The integration of longitudinal three-dimensional lesion analysis with multi-omics provides valuable insights into the mechanisms and response variability of anti-VEGF treatment in nAMD.
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Affiliation(s)
- Xuenan Zhuang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Miaoling Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Lan Mi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Xiongze Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Jiaxin Pu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Guiqin He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Liang Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Honghua Yu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Liwei Yao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Hui Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Yuying Ji
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Chengguo Zuo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Yongyue Su
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Yuhong Gan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Xinlei Hao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Yining Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Xuelin Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Feng Wen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
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Chen Y, Dan Z, Li S. GROWTH REGULATING FACTOR 7-mediated arbutin metabolism enhances rice salt tolerance. THE PLANT CELL 2024; 36:2834-2850. [PMID: 38701348 PMCID: PMC11289636 DOI: 10.1093/plcell/koae140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 05/05/2024]
Abstract
Salt stress is an environmental factor that limits plant growth and crop production. With the rapid expansion of salinized arable land worldwide, investigating the molecular mechanisms underlying the salt stress response in plants is urgently needed. Here, we report that GROWTH REGULATING FACTOR 7 (OsGRF7) promotes salt tolerance by regulating arbutin (hydroquinone-β-D-glucopyranoside) metabolism in rice (Oryza sativa). Overexpression of OsGRF7 increased arbutin content, and exogenous arbutin application rescued the salt-sensitive phenotype of OsGRF7 knockdown and knockout plants. OsGRF7 directly promoted the expression of the arbutin biosynthesis genes URIDINE DIPHOSPHATE GLYCOSYLTRANSFERASE 1 (OsUGT1) and OsUGT5, and knockout of OsUGT1 or OsUGT5 reduced rice arbutin content, salt tolerance, and grain size. Furthermore, OsGRF7 degradation through its interaction with F-BOX AND OTHER DOMAINS CONTAINING PROTEIN 13 reduced rice salinity tolerance and grain size. These findings highlight an underexplored role of OsGRF7 in modulating rice arbutin metabolism, salt stress response, and grain size, as well as its broad potential use in rice breeding.
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Affiliation(s)
- Yunping Chen
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice of Ministry of Agriculture, Engineering Research Center for Plant Biotechnology and Germplasm Utilization of Ministry of Education, College of Life Sciences, Wuhan University, Wuhan 430072, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Zhiwu Dan
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice of Ministry of Agriculture, Engineering Research Center for Plant Biotechnology and Germplasm Utilization of Ministry of Education, College of Life Sciences, Wuhan University, Wuhan 430072, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Shaoqing Li
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice of Ministry of Agriculture, Engineering Research Center for Plant Biotechnology and Germplasm Utilization of Ministry of Education, College of Life Sciences, Wuhan University, Wuhan 430072, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
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Chen J, Wang Y, Chen C, Song X, Shen X, Cao D, Zhao Z. Integrated network pharmacology and metabolomics reveal vascular protective effects of Ilex pubescens on thromboangiitis obliterans. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 130:155720. [PMID: 38763010 DOI: 10.1016/j.phymed.2024.155720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 04/09/2024] [Accepted: 05/05/2024] [Indexed: 05/21/2024]
Abstract
BACKGROUND Ilex pubescens Hook. et Arn (IP), traditionally known for its properties of promoting blood circulation, swelling and pain relief, heat clearing, and detoxification, has been used in the treatment of thromboangiitis obliterans (TAO). Despite its traditional applications, the specific mechanisms by which IP exerts its therapeutic effects on TAO remain unclear. AIM OF THE STUDY This study aims to uncover the underlying mechanisms in the therapeutic effects of IP on TAO, employing network pharmacology and metabolomic approaches. METHODS In this study, a rat TAO model was established by injecting sodium laurate through the femoral artery, followed by the oral administration of IP for 7 days. Plasma coagulation parameters were measured to assess the therapeutic effects of IP. The potential influence on the femoral artery and gastrocnemius muscle was histopathologically evaluated. Network pharmacology was employed to predict relevant targets and model pathways for TAO. Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS) was used for the metabolic profile analysis of rat plasma. Immunohistochemistry (IHC) was used to verify the mechanisms by which IP promotes blood circulation in TAO. RESULTS The study revealed that IP improved blood biochemical function in TAO and played a significant role in vascular protection and maintaining normal blood vessels and gastrocnemius morphologies. Network pharmacology showed that IP compounds play a therapeutic role in modulating lipids and atherosclerosis. Metabolomic analysis revealed that the pathways involved in sphingolipid metabolism and steroid biosynthesis were significantly disrupted. The joint analysis showed a strong correlation between lysophosphatidylcholine and IP components, including triterpenoid and iridoid components, which support the curative action of IP through the modulation of sphingolipid metabolism. Furthermore, decreased expression levels of SPHK1/S1PR1, TNF-α, IL-1β, and IL-6 were observed in the IP-treated group, suggesting that IP exerts a protective effect on the vasculature primarily by regulating of the SPHK1/S1PR1 signaling pathway. CONCLUSION In this study, we found that IP protects the vasculature against injury and treats TAO by regulating the steady-state disturbance of the sphingolipid pathway. These findings suggest that IP promotes vasculature by modulating sphingolipid metabolism and SPHK1/S1PR1 signaling pathway and reduce levels of inflammatory factors, offering new insights into its therapeutic potential.
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Affiliation(s)
- Jie Chen
- State Key Laboratory of Traditional Chinese Medicine Syndrome, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Yuanyuan Wang
- State Key Laboratory of Traditional Chinese Medicine Syndrome, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Caixin Chen
- State Key Laboratory of Traditional Chinese Medicine Syndrome, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Xianshu Song
- State Key Laboratory of Traditional Chinese Medicine Syndrome, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Xiuting Shen
- State Key Laboratory of Traditional Chinese Medicine Syndrome, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Di Cao
- Wannan Medical College, Wuhu 241002, China
| | - Zhongxiang Zhao
- State Key Laboratory of Traditional Chinese Medicine Syndrome, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510006, China.
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Lv J, Pan C, Cai Y, Han X, Wang C, Ma J, Pang J, Xu F, Wu S, Kou T, Ren F, Zhu ZJ, Zhang T, Wang J, Chen Y. Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease. Nat Commun 2024; 15:5729. [PMID: 38977723 PMCID: PMC11231153 DOI: 10.1038/s41467-024-50125-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 07/01/2024] [Indexed: 07/10/2024] Open
Abstract
Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.
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Affiliation(s)
- Jiali Lv
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chang Pan
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
| | - Yuping Cai
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
- Shanghai Key Laboratory of Aging Studies, Shanghai, China
| | - Xinyue Han
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Cheng Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science, Shandong University, Jinan, China
| | - Jingjing Ma
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
| | - Jiaojiao Pang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
| | - Feng Xu
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
| | - Shuo Wu
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
- Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China
| | - Tianzhang Kou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Fandong Ren
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, China.
| | - Tao Zhang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China.
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China.
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
| | - Jiali Wang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China.
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China.
- Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China.
- Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China.
| | - Yuguo Chen
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China.
- Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China.
- Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China.
- Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Qilu Hospital of Shandong University, Jinan, China.
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Yan J, Zhang X, Zhu K, Yu M, Liu Q, De Felici M, Zhang T, Wang J, Shen W. Sleep deprivation causes gut dysbiosis impacting on systemic metabolomics leading to premature ovarian insufficiency in adolescent mice. Theranostics 2024; 14:3760-3776. [PMID: 38948060 PMCID: PMC11209713 DOI: 10.7150/thno.95197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 06/06/2024] [Indexed: 07/02/2024] Open
Abstract
Rationale: Currently, there are occasional reports of health problems caused by sleep deprivation (SD). However, to date, there remains a lack of in-depth research regarding the effects of SD on the growth and development of oocytes in females. The present work aimed to investigate whether SD influences ovarian folliculogenesis in adolescent female mice. Methods: Using a dedicated device, SD conditions were established in 3-week old female mice (a critical stage of follicular development) for 6 weeks and gut microbiota and systemic metabolomics were analyzed. Analyses were related to parameters of folliculogenesis and reproductive performance of SD females. Results: We found that the gut microbiota and systemic metabolomics were severely altered in SD females and that these were associated with parameters of premature ovarian insufficiency (POI). These included increased granulosa cell apoptosis, reduced numbers of primordial follicles (PmFs), correlation with decreased AMH, E2, and increased LH in blood serum, and a parallel increased number of growing follicles and changes in protein expression compatible with PmF activation. SD also reduced oocyte maturation and reproductive performance. Notably, fecal microbial transplantation from SD females into normal females induced POI parameters in the latter while niacinamide (NAM) supplementation alleviated such symptoms in SD females. Conclusion: Gut microbiota and alterations in systemic metabolomics caused by SD induced POI features in juvenile females that could be counteracted with NAM supplementation.
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Affiliation(s)
- Jiamao Yan
- College of Life Sciences, Qingdao Agricultural University, Qingdao 266109, China
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010021, China
| | - Xiaoyuan Zhang
- College of Life Sciences, Qingdao Agricultural University, Qingdao 266109, China
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010021, China
| | - Kexin Zhu
- College of Life Sciences, Qingdao Agricultural University, Qingdao 266109, China
| | - Mubin Yu
- College of Life Sciences, Qingdao Agricultural University, Qingdao 266109, China
| | - Qingchun Liu
- College of Life Sciences, Qingdao Agricultural University, Qingdao 266109, China
| | - Massimo De Felici
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome 00133, Italy
| | - Teng Zhang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010021, China
| | - Junjie Wang
- College of Life Sciences, Qingdao Agricultural University, Qingdao 266109, China
| | - Wei Shen
- College of Life Sciences, Qingdao Agricultural University, Qingdao 266109, China
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27
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Wang S, He JN, Wang YJ, Zhao WY, Yang QX, Wang YN, Zhang Y, Zhang LP, Liu HW. Metabolome and Genome Analysis of a Novel Endophytic Fungus Aureobasidium pullulans KB3: Discovery of Polyketones and Polyketone Biosynthesis Pathway. Biochem Genet 2024:10.1007/s10528-024-10866-7. [PMID: 38877158 DOI: 10.1007/s10528-024-10866-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 06/09/2024] [Indexed: 06/16/2024]
Abstract
Endophytic fungi associated with plants may contain undiscovered bioactive compounds. Under standard laboratory conditions, most undiscovered compounds are inactive, whereas their production could be stimulated under different cultivation conditions. In this study, six endophytic fungi were isolated from the bark of Koelreuteria paniculata in Quancheng Park, Jinan City, Shandong Province, one of which was identified as a new subspecies of Aureobasidium pullulans, named A. pullulans KB3. Additionally, metabolomic tools were used to screen suitable media for A. pullulans KB3 fermentation, and the results showed that peptone dextrose medium (PDM) was more beneficial to culture A. pullulans KB3 for isolation of novel compounds. Sphaerolone, a polyketone compound, was initially isolated from A. pullulans KB3 via scaled up fermentation utilizing PDM. Additionally, the whole-genome DNA of A. pullulans KB3 was sequenced to facilitate compound isolation and identify the biosynthesis gene clusters (BGCs). This study reports the multi-omics (metabolome and genome) analysis of A. pullulans KB3, laying the foundation for discovering novel compounds of silent BGCs and identifying their biosynthesis pathway.
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Affiliation(s)
- Shuai Wang
- School of Pharmaceutical Sciences, Liaocheng University, Liaocheng, 252000, Shandong, People's Republic of China
| | - Jia-Nuo He
- Laboratory of Microbiology, Institute of Biology, Hebei Academy of Sciences, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
- Laboratory of Microbiology, Main Crops Disease of Microbial Control Engineering Technology Research Center in Hebei Province, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
| | - Ying-Jie Wang
- Laboratory of Microbiology, Institute of Biology, Hebei Academy of Sciences, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
- Laboratory of Microbiology, Main Crops Disease of Microbial Control Engineering Technology Research Center in Hebei Province, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
| | - Wen-Ya Zhao
- Laboratory of Microbiology, Institute of Biology, Hebei Academy of Sciences, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
- Laboratory of Microbiology, Main Crops Disease of Microbial Control Engineering Technology Research Center in Hebei Province, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
| | - Qing-Xia Yang
- Laboratory of Microbiology, Institute of Biology, Hebei Academy of Sciences, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
- Laboratory of Microbiology, Main Crops Disease of Microbial Control Engineering Technology Research Center in Hebei Province, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
| | - Ya-Na Wang
- Laboratory of Microbiology, Institute of Biology, Hebei Academy of Sciences, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
- Laboratory of Microbiology, Main Crops Disease of Microbial Control Engineering Technology Research Center in Hebei Province, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
| | - Yang Zhang
- Laboratory of Microbiology, Institute of Biology, Hebei Academy of Sciences, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
- Laboratory of Microbiology, Main Crops Disease of Microbial Control Engineering Technology Research Center in Hebei Province, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
| | - Li-Ping Zhang
- Laboratory of Microbiology, Institute of Biology, Hebei Academy of Sciences, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
- Laboratory of Microbiology, Main Crops Disease of Microbial Control Engineering Technology Research Center in Hebei Province, 15, Shijiazhuang, 050081, Hebei, People's Republic of China
| | - Hong-Wei Liu
- Laboratory of Microbiology, Institute of Biology, Hebei Academy of Sciences, 15, Shijiazhuang, 050081, Hebei, People's Republic of China.
- Laboratory of Microbiology, Main Crops Disease of Microbial Control Engineering Technology Research Center in Hebei Province, 15, Shijiazhuang, 050081, Hebei, People's Republic of China.
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Zhao S, Guo T, Yao Y, Dong B, Zhao G. Research advancements in the maintenance mechanism of Sporidiobolus pararoseus enhancing the quality of soy sauce during fermentation. Int J Food Microbiol 2024; 417:110690. [PMID: 38581832 DOI: 10.1016/j.ijfoodmicro.2024.110690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/21/2024] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
Soy sauce is a traditional condiment that undergoes microbial fermentation of various ingredients to achieve its desired color, scent, and flavor. Sporidiobolus pararoseus, which is a type of Rhodocerevisiae, shows promising potential as a source of lipids, carotenoids, and enzymes that can enrich the taste and color of soy sauce. However, there is currently a lack of systematic and comprehensive studies on the functions and mechanisms of action of S. pararoseus during soy sauce fermentation. In this review, it is well established that S. pararoseus produces lipids that are abundant in unsaturated fatty acids, particularly oleic acid, as well as various carotenoids, such as β-carotene, torulene, and torularhodin. These pigments are synthesized through the mevalonic acid pathway and possess remarkable antioxidant properties, acting as natural colorants. The synthesis of carotenoids is stimulated by high salt concentrations, which induces oxidative stress caused by NaCl. This stress further activates crucial enzymes involved in carotenoid production, ultimately leading to pigment formation. Moreover, S. pararoseus can produce high-quality enzymes that aid in the efficient utilization of soy sauce substrates during fermentation. Furthermore, this review focused on the impact of S. pararoseus on the color and quality of soy sauce and comprehensively analyzed its characteristics and ingredients. Thus, this review serves as a basis for screening high-quality oleaginous red yeast strains and improving the quality of industrial soy sauce production through the wide application of S. pararoseus.
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Affiliation(s)
- Shuoshuo Zhao
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Ting Guo
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Yunping Yao
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Bin Dong
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China.
| | - Guozhong Zhao
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China.
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29
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Song JJ, Cai J, Ma WJ, Lou Y, Bian J, Zhao B, She X, Liu XN. Untargeted metabolomics reveals potential plasma biomarkers for diagnosis of primary aldosteronism using liquid chromatography-mass spectrometry. Biomed Chromatogr 2024; 38:e5855. [PMID: 38442715 DOI: 10.1002/bmc.5855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 01/31/2024] [Accepted: 02/08/2024] [Indexed: 03/07/2024]
Abstract
Metabolite profiling has the potential to comprehensively bridge phenotypes and complex heterogeneous physiological and pathological states. We performed a metabolomics study using parallel liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis to screen for biomarkers of primary aldosteronism (PA) from a cohort of 111 PA patients and 218 primary hypertension (PH) patients. Hydrophilic interaction chromatography and reversed-phase liquid chromatography separations were employed to obtain a global plasma metabolome of endogenous metabolites. The satisfactory classification between PA and PH patients was obtained using the MVDA model. A total of 35 differential metabolites were screened out and identified. A diagnostic biomarker panel was established using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model and receiver operating characteristic analysis. Joint analysis with clinical indicators, including plasma supine aldosterone level, plasma orthostatic aldosterone level, body mass index, and blood potassium, revealed that the combination of metabolite biomarker panel and plasma supine aldosterone has the best clinical diagnostic efficacy.
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Affiliation(s)
- Jing-Jing Song
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jun Cai
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wen-Jun Ma
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ying Lou
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jin Bian
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Beibei Zhao
- Clinical Mass Spectrometry Center, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou International Bioisland, Guangzhou, China
| | - Xuhui She
- Clinical Mass Spectrometry Center, Guangzhou KingMed Center for Clinical Laboratory Co., Ltd., Guangzhou International Bioisland, Guangzhou, China
| | - Xiao-Ning Liu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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30
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He M, Liu A, Shi J, Xu YJ, Liu Y. Multi-Omics Reveals the Effects of Cannabidiol on Gut Microbiota and Metabolic Phenotypes. Cannabis Cannabinoid Res 2024; 9:714-727. [PMID: 37098174 DOI: 10.1089/can.2022.0331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/27/2023] Open
Abstract
Introduction: Cannabidiol (CBD) has important pharmacological activity, which includes antispasmodic, antioxidant, antithrombotic, and antianxiety properties. CBD has been applied as a health supplement to atherosclerosis. However, CBDs effect on gut microbiota and metabolic phenotype is unclear. Materials and Methods: We constructed a high production of cardiovascular risk factors, such as trimethylamine-N-oxide (TMAO) and phenylacetylglutamine (PAGln), in a mouse model using Clostridium sporogenes colonization. We used 16S ribosomal RNA (rRNA) gene sequencing and ultra-high performance liquid chromatography-quadrupole time-of flight mass spectrometry-based metabolomics to evaluate the effect of CBD on gut microbiota and plasma metabolites. Results: CBD decreased the levels of creatine kinase (CK), alanine transaminase (ALT), and low-density lipoprotein cholesterol and markedly increased high-density lipoprotein cholesterol. Furthermore, CBD treatment increased the abundance of beneficial bacteria, which include Lachnospiraceae_NK4A136 and Blautia in the gut, but it decreased the levels of TMAO and PAGln in the plasma. Conclusion: CBD might have beneficial effects for cardiovascular protection.
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Affiliation(s)
- Mengxue He
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China
| | - Aiyang Liu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China
| | - Jiachen Shi
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China
| | - Yong-Jiang Xu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China
| | - Yuanfa Liu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi, China
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31
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Wang X, Yu N, Jiao Z, Li L, Yu H, Wei S. Machine learning-enhanced molecular network reveals global exposure to hundreds of unknown PFAS. SCIENCE ADVANCES 2024; 10:eadn1039. [PMID: 38781329 PMCID: PMC11114235 DOI: 10.1126/sciadv.adn1039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 04/17/2024] [Indexed: 05/25/2024]
Abstract
Unknown forever chemicals like per- and polyfluoroalkyl substances (PFASs) are difficult to identify. Current platforms designed for metabolites and natural products cannot capture the diverse structural characteristics of PFAS. Here, we report an automatic PFAS identification platform (APP-ID) that screens for PFAS in environmental samples using an enhanced molecular network and identifies unknown PFAS structures using machine learning. Our networking algorithm, which enhances characteristic fragment matches, has lower false-positive rate (0.7%) than current algorithms (2.4 to 46%). Our support vector machine model identified unknown PFAS in test set with 58.3% accuracy, surpassing current software. Further, APP-ID detected 733 PFASs in real fluorochemical wastewater, 39 of which are previously unreported in environmental media. Retrospective screening of 126 PFASs against public data repository from 20 countries show PFAS substitutes are prevalent worldwide.
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Affiliation(s)
| | | | - Zhaoyu Jiao
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, People’s Republic of China
| | - Laihui Li
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, People’s Republic of China
| | - Hongxia Yu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, People’s Republic of China
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Wu S, Zhou H, Chen D, Lu Y, Li Y, Qiao J. Multi-omic analysis tools for microbial metabolites prediction. Brief Bioinform 2024; 25:bbae264. [PMID: 38859767 PMCID: PMC11165163 DOI: 10.1093/bib/bbae264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 05/08/2024] [Indexed: 06/12/2024] Open
Abstract
How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.
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Affiliation(s)
- Shengbo Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Haonan Zhou
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Danlei Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Yutong Lu
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Yanni Li
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
| | - Jianjun Qiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
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Pang Z, Xu L, Viau C, Lu Y, Salavati R, Basu N, Xia J. MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics. Nat Commun 2024; 15:3675. [PMID: 38693118 PMCID: PMC11063062 DOI: 10.1038/s41467-024-48009-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 04/18/2024] [Indexed: 05/03/2024] Open
Abstract
The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.
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Affiliation(s)
- Zhiqiang Pang
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lei Xu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Charles Viau
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Yao Lu
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada
| | - Reza Salavati
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Niladri Basu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Jianguo Xia
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada.
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada.
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Perez de Souza L, Fernie AR. Computational methods for processing and interpreting mass spectrometry-based metabolomics. Essays Biochem 2024; 68:5-13. [PMID: 37999335 PMCID: PMC11065554 DOI: 10.1042/ebc20230019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/10/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Abstract
Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.
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Affiliation(s)
- Leonardo Perez de Souza
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
- Center for Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
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Zhu Y, Wang H, Li J, Wang Z, Wang Y. Metabolic Profiles and Microbial Synergy Mechanism of Anammox Biomass Enrichment and Membrane Fouling Alleviation in the Anammox Dynamic Membrane Bioreactor. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6284-6295. [PMID: 38488464 DOI: 10.1021/acs.est.3c10030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
The anammox dynamic membrane bioreactor (DMBR) is promising in applications with enhanced anammox biomass enrichment and fouling alleviation. However, the metabolic mechanism underlying the functional features of anammox sludge and the biofilm membrane is still obscure. We investigated the metabolic networks of anammox sludge and membrane biofilm in the DMBR. The cooperation between anammox and dissimilatory nitrate reduction to ammonium processes favored the robust anammox process in the DMBR. The rapid bacterial growth occurred in the DMBR sludge with 1.33 times higher biomass yield compared to the MBR sludge, linked to the higher activities of lipid metabolism, nucleotide metabolism, and B vitamin-related metabolism of the DMBR sludge. The metabolism of the DMBR biofilm microbial community benefited the fouling alleviation that the abundant fermentative bacteria and their cooperation with the anammox sludge microbial community promoted organics degradation. The intensified degradation of foulants by the DMBR biofilm community was further evidenced by the active carbohydrate metabolism and the upregulated vitamin B intermediates in the biofilms of the DMBR. Our findings provide insights into key metabolic mechanisms for enhanced biomass enrichment and fouling control of the anammox DMBR, guiding manipulations and applications for overcoming anammox biomass loss in the treatment of wastewater under detrimental environmental conditions.
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Affiliation(s)
- Yijing Zhu
- State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, P. R. China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, P. R. China
| | - Han Wang
- State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, P. R. China
| | - Jia Li
- State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, P. R. China
| | - Zhiwei Wang
- State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, P. R. China
| | - Yayi Wang
- State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, P. R. China
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Qiu H, Zhang X, Zhang Y, Jiang X, Ren Y, Gao D, Zhu X, Usadel B, Fernie AR, Wen W. Depicting the genetic and metabolic panorama of chemical diversity in the tea plant. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:1001-1016. [PMID: 38048231 PMCID: PMC10955498 DOI: 10.1111/pbi.14241] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/11/2023] [Accepted: 11/12/2023] [Indexed: 12/06/2023]
Abstract
As a frequently consumed beverage worldwide, tea is rich in naturally important bioactive metabolites. Combining genetic, metabolomic and biochemical methodologies, here, we present a comprehensive study to dissect the chemical diversity in tea plant. A total of 2837 metabolites were identified at high-resolution with 1098 of them being structurally annotated and 63 of them were structurally identified. Metabolite-based genome-wide association mapping identified 6199 and 7823 metabolic quantitative trait loci (mQTL) for 971 and 1254 compounds in young leaves (YL) and the third leaves (TL), respectively. The major mQTL (i.e., P < 1.05 × 10-5, and phenotypic variation explained (PVE) > 25%) were further interrogated. Through extensive annotation of the tea metabolome as well as network-based analysis, this study broadens the understanding of tea metabolism and lays a solid foundation for revealing the natural variations in the chemical composition of the tea plant. Interestingly, we found that galloylations, rather than hydroxylations or glycosylations, were the largest class of conversions within the tea metabolome. The prevalence of galloylations in tea is unusual, as hydroxylations and glycosylations are typically the most prominent conversions of plant specialized metabolism. The biosynthetic pathway of flavonoids, which are one of the most featured metabolites in tea plant, was further refined with the identified metabolites. And we demonstrated the further mining and interpretation of our GWAS results by verifying two identified mQTL (including functional candidate genes CsUGTa, CsUGTb, and CsCCoAOMT) and completing the flavonoid biosynthetic pathway of the tea plant.
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Affiliation(s)
- Haiji Qiu
- National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops, Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry SciencesHuazhong Agricultural UniversityWuhanChina
- Shenzhen Institute of Nutrition and HealthHuazhong Agricultural UniversityWuhanChina
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenChina
| | - Xiaoliang Zhang
- National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops, Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry SciencesHuazhong Agricultural UniversityWuhanChina
| | - Youjun Zhang
- Max‐Planck‐Institute of Molecular Plant PhysiologyPotsdam‐GolmGermany
- Center of Plant Systems Biology and BiotechnologyPlovdivBulgaria
| | - Xiaohui Jiang
- National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops, Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry SciencesHuazhong Agricultural UniversityWuhanChina
| | - Yujia Ren
- National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops, Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry SciencesHuazhong Agricultural UniversityWuhanChina
| | - Dawei Gao
- National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops, Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry SciencesHuazhong Agricultural UniversityWuhanChina
| | - Xiang Zhu
- Thermo Fisher ScientificShanghaiChina
| | - Björn Usadel
- Institute of Bio‐ and Geosciences, IBG‐4: Bioinformatics, CEPLAS, Forschungszentrum JülichJülichGermany
- Institute for Biological Data ScienceHeinrich Heine UniversityDüsseldorfGermany
| | - Alisdair R. Fernie
- Max‐Planck‐Institute of Molecular Plant PhysiologyPotsdam‐GolmGermany
- Center of Plant Systems Biology and BiotechnologyPlovdivBulgaria
| | - Weiwei Wen
- National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops, Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry SciencesHuazhong Agricultural UniversityWuhanChina
- Shenzhen Institute of Nutrition and HealthHuazhong Agricultural UniversityWuhanChina
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenChina
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37
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Ma G, Kang J, Yu T. Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes. Brief Bioinform 2024; 25:bbae141. [PMID: 38581417 PMCID: PMC10998539 DOI: 10.1093/bib/bbae141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/28/2024] [Accepted: 03/13/2024] [Indexed: 04/08/2024] Open
Abstract
Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.
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Affiliation(s)
- Guoxuan Ma
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Tianwei Yu
- Shenzhen Research Institute of Big Data, School of Data Science, The Chinese University of Hong Kong - Shenzhen (CUHK-Shenzhen), Shenzhen, Guangdong 518172, China
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38
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Yin B, Cai Y, Teng T, Wang X, Liu X, Li X, Wang J, Wu H, He Y, Ren F, Kou T, Zhu ZJ, Zhou X. Identifying plasma metabolic characteristics of major depressive disorder, bipolar disorder, and schizophrenia in adolescents. Transl Psychiatry 2024; 14:163. [PMID: 38531835 DOI: 10.1038/s41398-024-02886-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 03/14/2024] [Accepted: 03/19/2024] [Indexed: 03/28/2024] Open
Abstract
Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.
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Affiliation(s)
- Bangmin Yin
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuping Cai
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Teng Teng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaolin Wang
- Health Management Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xueer Liu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Wang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongyan Wu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuqian He
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fandong Ren
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Tianzhang Kou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, China.
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Cooper B, Yang R. An assessment of AcquireX and Compound Discoverer software 3.3 for non-targeted metabolomics. Sci Rep 2024; 14:4841. [PMID: 38418855 PMCID: PMC10902394 DOI: 10.1038/s41598-024-55356-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/22/2024] [Indexed: 03/02/2024] Open
Abstract
We used the Exploris 240 mass spectrometer for non-targeted metabolomics on Saccharomyces cerevisiae strain BY4741 and tested AcquireX software for increasing the number of detectable compounds and Compound Discoverer 3.3 software for identifying compounds by MS2 spectral library matching. AcquireX increased the number of potentially identifiable compounds by 50% through six iterations of MS2 acquisition. On the basis of high-scoring MS2 matches made by Compound Discoverer, there were 483 compounds putatively identified from nearly 8000 candidate spectra. Comparisons to 20 amino acid standards, however, revealed instances whereby compound matches could be incorrect despite strong scores. Situations included the candidate with the top score not being the correct compound, matching the same compound at two different chromatographic peaks, assigning the highest score to a library compound much heavier than the mass for the parent ion, and grouping MS2 isomers to a single parent ion. Because the software does not calculate false positive and false discovery rates at these multiple levels where such errors can propagate, we conclude that manual examination of findings will be required post software analysis. These results will interest scientists who may use this platform for metabolomics research in diverse disciplines including medical science, environmental science, and agriculture.
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Affiliation(s)
- Bret Cooper
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, 20705, USA.
| | - Ronghui Yang
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, 20705, USA
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40
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Zhu A, Liu M, Tian Z, Liu W, Hu X, Ao M, Jia J, Shi T, Liu H, Li D, Mao H, Su H, Yan W, Li Q, Lan C, Fernie AR, Chen W. Chemical-tag-based semi-annotated metabolomics facilitates gene identification and specialized metabolic pathway elucidation in wheat. THE PLANT CELL 2024; 36:540-558. [PMID: 37956052 PMCID: PMC10896294 DOI: 10.1093/plcell/koad286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/19/2023] [Accepted: 10/19/2023] [Indexed: 11/15/2023]
Abstract
The importance of metabolite modification and species-specific metabolic pathways has long been recognized. However, linking the chemical structure of metabolites to gene function in order to explore the genetic and biochemical basis of metabolism has not yet been reported in wheat (Triticum aestivum). Here, we profiled metabolic fragment enrichment in wheat leaves and consequently applied chemical-tag-based semi-annotated metabolomics in a genome-wide association study in accessions of wheat. The studies revealed that all 1,483 quantified metabolites have at least one known functional group whose modification is tailored in an enzyme-catalyzed manner and eventually allows efficient candidate gene mining. A Triticeae crop-specific flavonoid pathway and its underlying metabolic gene cluster were elucidated in further functional studies. Additionally, upon overexpressing the major effect gene of the cluster TraesCS2B01G460000 (TaOMT24), the pathway was reconstructed in rice (Oryza sativa), which lacks this pathway. The reported workflow represents an efficient and unbiased approach for gene mining using forward genetics in hexaploid wheat. The resultant candidate gene list contains vast molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets and will ultimately aid in achieving wheat crop improvement.
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Affiliation(s)
- Anting Zhu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Mengmeng Liu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Zhitao Tian
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Wei Liu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Xin Hu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Min Ao
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Jingqi Jia
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Taotao Shi
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Hongbo Liu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
| | - Dongqin Li
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
| | - Hailiang Mao
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
| | - Handong Su
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Wenhao Yan
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Qiang Li
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
| | - Caixia Lan
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Department of Root Biology and Symbiosis, Potsdam-Golm 14476, Germany
| | - Wei Chen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
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41
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Long Y, Han X, Meng X, Xu P, Tao F. A robust yeast chassis: comprehensive characterization of a fast-growing Saccharomyces cerevisiae. mBio 2024; 15:e0319623. [PMID: 38214535 PMCID: PMC10865977 DOI: 10.1128/mbio.03196-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/07/2023] [Indexed: 01/13/2024] Open
Abstract
Robust chassis are critical to facilitate advances in synthetic biology. This study describes a comprehensive characterization of a new yeast isolate Saccharomyces cerevisiae XP that grows faster than commonly used research and industrial S. cerevisiae strains. The genomic, transcriptomic, and metabolomic analyses suggest that the fast growth rate is, in part, due to the efficient electron transport chain and key growth factor synthesis. A toolbox for genetic manipulation of the yeast was developed; we used it to construct l-lactic acid producers for high lactate production. The development of genetically malleable yeast strains that grow faster than currently used strains may significantly enhance the uses of S. cerevisiae in biotechnology.IMPORTANCEYeast is known as an outstanding starting strain for constructing microbial cell factories. However, its growth rate restricts its application. A yeast strain XP, which grows fast in high concentrations of sugar and acidic environments, is revealed to demonstrate the potential in industrial applications. A toolbox was also built for its genetic manipulation including gene insertion, deletion, and ploidy transformation. The knowledge of its metabolism, which could guide the designing of genetic experiments, was generated with multi-omics analyses. This novel strain along with its toolbox was then tested by constructing an l-lactic acid efficient producer, which is conducive to the development of degradable plastics. This study highlights the remarkable competence of nonconventional yeast for applications in biotechnology.
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Affiliation(s)
- Yangdanyu Long
- State Key Laboratory of Microbial Metabolism and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Xiao Han
- State Key Laboratory of Microbial Metabolism and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Xuanlin Meng
- State Key Laboratory of Microbial Metabolism and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Ping Xu
- State Key Laboratory of Microbial Metabolism and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Fei Tao
- State Key Laboratory of Microbial Metabolism and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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42
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Martin MR, Bittremieux W, Hassoun S. Molecular structure discovery for untargeted metabolomics using biotransformation rules and global molecular networking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.04.578795. [PMID: 38370723 PMCID: PMC10871291 DOI: 10.1101/2024.02.04.578795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Although untargeted mass spectrometry-based metabolomics is crucial for understanding life's molecular underpinnings, its effectiveness is hampered by low annotation rates of the generated tandem mass spectra. To address this issue, we introduce a novel data-driven approach, Biotransformation-based Annotation Method (BAM), that leverages molecular structural similarities inherent in biochemical reactions. BAM operates by applying biotransformation rules to known 'anchor' molecules, which exhibit high spectral similarity to unknown spectra, thereby hypothesizing and ranking potential structures for the corresponding 'suspect' molecule. BAM's effectiveness is demonstrated by its success in annotating suspect spectra in a global molecular network comprising hundreds of millions of spectra. BAM was able to assign correct molecular structures to 24.2 % of examined anchor-suspect cases, thereby demonstrating remarkable advancement in metabolite annotation.
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43
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Wang X, Sun X, Wang F, Wei C, Zheng F, Zhang X, Zhao X, Zhao C, Lu X, Xu G. Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking. Anal Chem 2024; 96:1444-1453. [PMID: 38240194 DOI: 10.1021/acs.analchem.3c03443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases. Molecular networking (MN) shows great promise in large-scale metabolome annotation, but enhancing the correlation between spectral and structural similarity is essential to fully exploring the benefits of MN annotation. In this study, a novel approach was proposed to enhance metabolite annotation in untargeted metabolomics using EIEIO and MN. MS/MS spectra were acquired in EIEIO and collision-induced dissociation (CID) modes for over 400 reference metabolites. The study revealed a stronger correlation between the EIEIO spectra and metabolite structure. Moreover, the EIEIO spectral network outperformed the CID spectral network in capturing structural analogues. The annotation performance of the structural similarity network for untargeted LC-MS/MS was evaluated. For the spiked NIST SRM 1950 human plasma, the annotation coverage and accuracy were 72.94 and 74.19%, respectively. A total of 2337 metabolite features were successfully annotated in NIST SRM 1950 human plasma, which was twice that of LC-CID MS/MS. Finally, the developed method was applied to investigate prostate cancer. A total of 87 significantly differential metabolites were annotated. This study combining EIEIO and MN makes a valuable contribution to improving metabolome annotation.
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Affiliation(s)
- Xinxin Wang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P. R. China
| | - Xiaoshan Sun
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P. R. China
| | - Fubo Wang
- Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, P. R. China
- Department of Urology, Institute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, P. R. China
| | - Chunmeng Wei
- Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, P. R. China
- Department of Urology, Institute of Urology and Nephrology, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, P. R. China
| | - Fujian Zheng
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P. R. China
| | - Xiuqiong Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P. R. China
| | - Xinjie Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P. R. China
| | - Chunxia Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P. R. China
| | - Xin Lu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P. R. China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, P. R. China
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Han B, Wu J, Wei Q, Liu F, Cui L, Rueppell O, Xu S. Life-history stage determines the diet of ectoparasitic mites on their honey bee hosts. Nat Commun 2024; 15:725. [PMID: 38272866 PMCID: PMC10811344 DOI: 10.1038/s41467-024-44915-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 01/04/2024] [Indexed: 01/27/2024] Open
Abstract
Ectoparasitic mites of the genera Varroa and Tropilaelaps have evolved to exclusively exploit honey bees as food sources during alternating dispersal and reproductive life history stages. Here we show that the primary food source utilized by Varroa destructor depends on the host life history stage. While feeding on adult bees, dispersing V. destructor feed on the abdominal membranes to access to the fat body as reported previously. However, when V. destructor feed on honey bee pupae during their reproductive stage, they primarily consume hemolymph, indicated by wound analysis, preferential transfer of biostains, and a proteomic comparison between parasite and host tissues. Biostaining and proteomic results were paralleled by corresponding findings in Tropilaelaps mercedesae, a mite that only feeds on brood and has a strongly reduced dispersal stage. Metabolomic profiling of V. destructor corroborates differences between the diet of the dispersing adults and reproductive foundresses. The proteome and metabolome differences between reproductive and dispersing V. destructor suggest that the hemolymph diet coincides with amino acid metabolism and protein synthesis in the foundresses while the metabolism of non-reproductive adults is tuned to lipid metabolism. Thus, we demonstrate within-host dietary specialization of ectoparasitic mites that coincides with life history of hosts and parasites.
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Affiliation(s)
- Bin Han
- State Key Laboratory of Resource Insects, Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jiangli Wu
- State Key Laboratory of Resource Insects, Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Qiaohong Wei
- State Key Laboratory of Resource Insects, Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Fengying Liu
- State Key Laboratory of Resource Insects, Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lihong Cui
- Cell Biology Facility, Center of Biomedical Analysis, Tsinghua University, Beijing, 100084, China
| | - Olav Rueppell
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, T6G2L3, Canada.
| | - Shufa Xu
- State Key Laboratory of Resource Insects, Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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45
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Rigel N, Li DW, Brüschweiler R. COLMARppm: A Web Server Tool for the Accurate and Rapid Prediction of 1H and 13C NMR Chemical Shifts of Organic Molecules and Metabolites. Anal Chem 2024; 96:701-709. [PMID: 38157361 PMCID: PMC10794995 DOI: 10.1021/acs.analchem.3c03677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/15/2023] [Accepted: 11/20/2023] [Indexed: 01/03/2024]
Abstract
Despite rapid progress in metabolomics research, a major bottleneck is the large number of metabolites whose chemical structures are unknown or whose spectra have not been deposited in metabolomics databases. Nuclear magnetic resonance (NMR) spectroscopy has a long history of elucidating chemical structures from experimentally measured 1H and 13C chemical shifts. One approach to characterizing the chemical structures of an unknown metabolite is to predict the 1H and 13C chemical shifts of candidate compounds (e.g., metabolites from the Human Metabolome Database (HMDB)) and compare them with chemical shifts of the unknown. However, accurate prediction of NMR chemical shifts in aqueous solution is challenging due to limitations of experimental chemical shift libraries and the high computational cost of quantum chemical methods. To improve NMR prediction accuracy and applicability, an empirical prediction strategy is introduced here to provide an accurately predicted chemical shift for organic molecules and metabolites within seconds. Unique features of COLMARppm include (i) the training library exclusively consisting of high quality NMR spectra measured under standard conditions in aqueous solution, (ii) utilization of NMR motif information, and (iii) leveraging of the improved prediction accuracy for the automated assignment of experimental chemical shifts for candidate structures. COLMARppm is demonstrated in terms of accuracy and speed for a set of 20 compounds taken from the HMDB for chemical shift prediction and resonance assignment. COLMARppm is applicable to a wide range of small molecules and can be directly incorporated into metabolomics workflows.
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Affiliation(s)
- Nick Rigel
- Department
of Chemistry and Biochemistry, The Ohio
State University, Columbus, Ohio 43210, United States
| | - Da-Wei Li
- Campus
Chemical Instrument Center,The Ohio State
University, Columbus, Ohio 43210, United States
| | - Rafael Brüschweiler
- Department
of Chemistry and Biochemistry, The Ohio
State University, Columbus, Ohio 43210, United States
- Campus
Chemical Instrument Center,The Ohio State
University, Columbus, Ohio 43210, United States
- Department
of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210, United States
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46
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Tan Y, Li Y, Ren L, Fu H, Li Q, Liu S. Integrative proteome and metabolome analyses reveal molecular basis underlying growth and nutrient composition in the Pacific oyster, Crassostrea gigas. J Proteomics 2024; 290:105021. [PMID: 37838097 DOI: 10.1016/j.jprot.2023.105021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 09/28/2023] [Accepted: 10/03/2023] [Indexed: 10/16/2023]
Abstract
In order to comprehend the molecular basis of growth, nutrient composition, and color pigmentation in oysters, comparative proteome and metabolome analyses of two selectively bred oyster strains with contrasting growth rate and shell color were used in this study. A total of 289 proteins and 224 metabolites were identified differentially expressed between the two strains. We identified a series of specifically enriched functional clusters implicated in protein biosynthesis (RPL4, MRPS7, and CARS), fatty acid metabolism (ACSL5, PEX3, ACOXI, CPTIA, FABP6, and HSD17B12), energy metabolism (FH, PPP1R7, CLAM2, and RGN), cell proliferation (MYB, NFYC, DOHH, TOP2a, SMARCA5, and SMARCC2), material transport (ABCB1, ABCB8, VPS16, and VPS33a), and pigmentation (RDH7, RDH13, Retsat, COX15, and Cyp3a9). Integrated proteome and metabolome analyses indicate that fast-growing strain utilize energy-efficient mechanisms of ATP generation while promoting protein and polyunsaturated fatty acid synthesis, activating the cell cycle to increase cell proliferation and thus promoting their biomass increase. These results uncovered molecular mechanisms underlying growth regulation, nutrition quality, and pigmentation and provided candidate biomarkers for molecular breeding in oysters. SIGNIFICANCE: Rapid growth has always been the primary breeding objective to increase the production profits of Pacific oyster (Crassostrea gigas), while favorable nutritional quality and beautiful color add commercial value. In recent years, proteomic and metabolomic techniques have been widely used in marine organisms, although these techniques are seldom utilized to study oyster growth and development. In this study, two C. gigas strains with contrasted phenotypes in growth and shell color provided an ideal model for unraveling the molecular basis of growth and nutrient composition through a comparison of the proteome and metabolome. Since proteins and metabolites are the critical undertakers and the end products of cellular regulatory processes, identifying the differentially expressed proteins and metabolites would allow for discovering biomarkers and pathways that were implicated in cell growth, proliferation, and other critical functions. This work provides valuable resources in assistance with molecular breeding of oyster strains with superior production traits of fast-growth and high-quality nutrient value.
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Affiliation(s)
- Ying Tan
- Key Laboratory of Mariculture (Ocean University of China), Ministry of Education, and College of Fisheries, Ocean University of China, Qingdao 266003, China
| | - Yongjing Li
- Key Laboratory of Mariculture (Ocean University of China), Ministry of Education, and College of Fisheries, Ocean University of China, Qingdao 266003, China
| | - Liting Ren
- Key Laboratory of Mariculture (Ocean University of China), Ministry of Education, and College of Fisheries, Ocean University of China, Qingdao 266003, China
| | - Huiru Fu
- Key Laboratory of Mariculture (Ocean University of China), Ministry of Education, and College of Fisheries, Ocean University of China, Qingdao 266003, China
| | - Qi Li
- Key Laboratory of Mariculture (Ocean University of China), Ministry of Education, and College of Fisheries, Ocean University of China, Qingdao 266003, China
| | - Shikai Liu
- Key Laboratory of Mariculture (Ocean University of China), Ministry of Education, and College of Fisheries, Ocean University of China, Qingdao 266003, China.
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47
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Zhao D, Zhuang W, Wang Y, Xu X, Qiao L. In-depth mass spectrometry analysis of rhGH administration altered energy metabolism and steroidogenesis. Talanta 2024; 266:125069. [PMID: 37574608 DOI: 10.1016/j.talanta.2023.125069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/05/2023] [Accepted: 08/09/2023] [Indexed: 08/15/2023]
Abstract
Growth hormone, as a proteohormone, is primarily known of its dramatic effect on longitudinal growth. Recombinant DNA technology has provided a safe, abundant and comparatively cheap supply of human GH for growth hormone-deficient individuals. However, many healthy subjects, especially athletics, administrate GH for enhanced athletic performance or strength. A better and more comprehensive understanding of rhGH effect in healthy individuals is urgent and essential. In this study, we recruited 14 healthy young male and injected rhGH once. Untargeted LC-MS metabolomics profiling of serum and urine was performed before and after the rhGH injection. The GH-induced dysregulation of energy related pathways, such as amino acid metabolism, nucleotide metabolism, glycolysis and TCA cycle, was revealed. Moreover, individuals supplemented with micro-doses of rhGH exhibited significantly changed urinary steroidal profiles, suggesting a role of rhGH in both energy metabolism and steroidogenesis. We expect that our results will be helpful to provide new evidence on the effects of rhGH injection and provide potential biomarkers for rhGH administration.
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Affiliation(s)
- Dan Zhao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, 200000, China
| | - Wenqian Zhuang
- Research Institute for Doping Control, Shanghai University of Sport, Shanghai, 200000, China
| | - Yang Wang
- Research Institute for Doping Control, Shanghai University of Sport, Shanghai, 200000, China
| | - Xin Xu
- Research Institute for Doping Control, Shanghai University of Sport, Shanghai, 200000, China.
| | - Liang Qiao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, 200000, China.
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48
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Zhang L, Zheng Z, Huang H, Fu Y, Chen T, Liu C, Yi Q, Lin C, Zeng Y, Ou Q, Zeng Y. Multi-omics reveals deoxycholic acid modulates bile acid metabolism via the gut microbiota to antagonize carbon tetrachloride-induced chronic liver injury. Gut Microbes 2024; 16:2323236. [PMID: 38416424 PMCID: PMC10903553 DOI: 10.1080/19490976.2024.2323236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/21/2024] [Indexed: 02/29/2024] Open
Abstract
Deoxycholic acid (DCA) serves essential functions in both physiological and pathological liver processes; nevertheless, the relationship among DCA, gut microbiota, and metabolism in chronic liver injury remain insufficiently understood. The primary objective of this study is to elucidate the potential of DCA in ameliorating chronic liver injury and evaluate its regulatory effect on gut microbiota and metabolism via a comprehensive multi-omics approach. Our study found that DCA supplementation caused significant changes in the composition of gut microbiota, which were essential for its antagonistic effect against CCl4-induced chronic liver injury. When gut microbiota was depleted with antibiotics, the observed protective efficacy of DCA against chronic liver injury became noticeably attenuated. Mechanistically, we discovered that DCA regulates the metabolism of bile acids (BAs), including 3-epi DCA, Apo-CA, and its isomers 12-KLCA and 7-KLCA, IHDCA, and DCA, by promoting the growth of A.muciniphila in gut microbiota. This might lead to the inhibition of the IL-17 and TNF inflammatory signaling pathway, thereby effectively countering CCl4-induced chronic liver injury. This study illustrates that the enrichment of A. muciniphila in the gut microbiota, mediated by DCA, enhances the production of secondary bile acids, thereby mitigating chronic liver injury induced by CCl4. The underlying mechanism may involve the inhibition of hepatic IL-17 and TNF signaling pathways. These findings propose a promising approach to alleviate chronic liver injury by modulating both the gut microbiota and bile acids metabolism.
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Affiliation(s)
- Li Zhang
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Zhiyi Zheng
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Huanhuan Huang
- Department of Pediatrics, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Ya Fu
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Tianbin Chen
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Can Liu
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Qiang Yi
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Caorui Lin
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Yongjun Zeng
- Department of Cardiology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Qishui Ou
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Yongbin Zeng
- Department of Laboratory Medicine, Gene Diagnosis Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
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49
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Peng HR, Qiu JQ, Zhou QM, Zhang YK, Chen QY, Yin YQ, Su W, Yu S, Wang YT, Cai Y, Gu MN, Zhang HH, Sun QQ, Hu G, Wu YW, Liu J, Chen S, Zhu ZJ, Song XY, Zhou JW. Intestinal epithelial dopamine receptor signaling drives sex-specific disease exacerbation in a mouse model of multiple sclerosis. Immunity 2023; 56:2773-2789.e8. [PMID: 37992711 DOI: 10.1016/j.immuni.2023.10.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 08/22/2023] [Accepted: 10/27/2023] [Indexed: 11/24/2023]
Abstract
Although the gut microbiota can influence central nervous system (CNS) autoimmune diseases, the contribution of the intestinal epithelium to CNS autoimmunity is less clear. Here, we showed that intestinal epithelial dopamine D2 receptors (IEC DRD2) promoted sex-specific disease progression in an animal model of multiple sclerosis. Female mice lacking Drd2 selectively in intestinal epithelial cells showed a blunted inflammatory response in the CNS and reduced disease progression. In contrast, overexpression or activation of IEC DRD2 by phenylethylamine administration exacerbated disease severity. This was accompanied by altered lysozyme expression and gut microbiota composition, including reduced abundance of Lactobacillus species. Furthermore, treatment with N2-acetyl-L-lysine, a metabolite derived from Lactobacillus, suppressed microglial activation and neurodegeneration. Taken together, our study indicates that IEC DRD2 hyperactivity impacts gut microbial abundances and increases susceptibility to CNS autoimmune diseases in a female-biased manner, opening up future avenues for sex-specific interventions of CNS autoimmune diseases.
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Affiliation(s)
- Hai-Rong Peng
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jia-Qian Qiu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China; Shanghai Key Laboratory of Aging Studies, Shanghai 201210, China
| | - Qin-Ming Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yu-Kai Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qiao-Yu Chen
- Department of Pharmacology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
| | - Yan-Qing Yin
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Wen Su
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shui Yu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ya-Ting Wang
- State Key Laboratory of Cell Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuping Cai
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China; Shanghai Key Laboratory of Aging Studies, Shanghai 201210, China
| | - Ming-Na Gu
- Department of Pharmacology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
| | - Hao-Hao Zhang
- State Key Laboratory of Cell Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Qing-Qing Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Gang Hu
- Department of Pharmacology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
| | - Yi-Wen Wu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jun Liu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Sheng Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China; Shanghai Key Laboratory of Aging Studies, Shanghai 201210, China.
| | - Xin-Yang Song
- State Key Laboratory of Cell Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China.
| | - Jia-Wei Zhou
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China; Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai 201210, China; Innovation Center of Neurodegeneration, School of Medicine, Nantong University, Nantong, Jiangsu 226001, China.
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Rao H, Liu C, Wang A, Ma C, Xu Y, Ye T, Su W, Zhou P, Gao WQ, Li L, Ding X. SETD2 deficiency accelerates sphingomyelin accumulation and promotes the development of renal cancer. Nat Commun 2023; 14:7572. [PMID: 37989747 PMCID: PMC10663509 DOI: 10.1038/s41467-023-43378-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 11/07/2023] [Indexed: 11/23/2023] Open
Abstract
Patients with polycystic kidney disease (PKD) encounter a high risk of clear cell renal cell carcinoma (ccRCC), a malignant tumor with dysregulated lipid metabolism. SET domain-containing 2 (SETD2) has been identified as an important tumor suppressor and an immunosuppressor in ccRCC. However, the role of SETD2 in ccRCC generation in PKD remains largely unexplored. Herein, we perform metabolomics, lipidomics, transcriptomics and proteomics within SETD2 loss induced PKD-ccRCC transition mouse model. Our analyses show that SETD2 loss causes extensive metabolic reprogramming events that eventually results in enhanced sphingomyelin biosynthesis and tumorigenesis. Clinical ccRCC patient specimens further confirm the abnormal metabolic reprogramming and sphingomyelin accumulation. Tumor symptom caused by Setd2 knockout is relieved by myriocin, a selective inhibitor of serine-palmitoyl-transferase and sphingomyelin biosynthesis. Our results reveal that SETD2 deficiency promotes large-scale metabolic reprogramming and sphingomyelin biosynthesis during PKD-ccRCC transition. This study introduces high-quality multi-omics resources and uncovers a regulatory mechanism of SETD2 on lipid metabolism during tumorigenesis.
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Affiliation(s)
- Hanyu Rao
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Changwei Liu
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Aiting Wang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Chunxiao Ma
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Yue Xu
- State Key Laboratory of Systems Medicine for Cancer, Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Tianbao Ye
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenqiong Su
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Peijun Zhou
- Division of Kidney Transplant, Department of Urology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wei-Qiang Gao
- State Key Laboratory of Systems Medicine for Cancer, Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Li Li
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
- State Key Laboratory of Systems Medicine for Cancer, Renji-Med X Clinical Stem Cell Research Center, Ren Ji Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200127, China.
| | - Xianting Ding
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- State Key Laboratory of Systems Medicine for Cancer, Institute for Personalized Medicine and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
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