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Li S, Qiu Y, Li Y, Wu J, Yin N, Ren J, Shao M, Yu J, Song Y, Sun X, Gao S, Cao W. Serum metabolite biomarkers for the early diagnosis and monitoring of age-related macular degeneration. J Adv Res 2024:S2090-1232(24)00434-X. [PMID: 39369956 DOI: 10.1016/j.jare.2024.10.001] [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: 05/22/2024] [Revised: 09/29/2024] [Accepted: 10/02/2024] [Indexed: 10/08/2024] Open
Abstract
INTRODUCTION Age-related macular degeneration (AMD) is a leading cause of irreversible blindness worldwide, with significant challenges for early diagnosis and treatment. OBJECTIVES To identify new biomarkers that are important for the early diagnosis and monitoring of the severity/progression of AMD. METHODS We investigated the diagnostic and monitoring potential of blood metabolites in a cohort of 547 individuals (167 healthy controls, 240 individuals with other eye diseases as eye disease controls, and 140 individuals with AMD) from 2 centers over three phases: discovery phase 1, discovery phase 2, and an external validation phase. The samples were analyzed via a mass spectrometry-based, widely targeted metabolomic workflow. In discovery phases 1 and 2, we built a machine learning algorithm to predict the probability of AMD. In the external validation phase, we further confirmed the performance of the biomarker panel identified by the algorithm. We subsequently evaluated the performance of the identified biomarker panel in monitoring the progression and severity of AMD. RESULTS We developed a clinically specific three-metabolite panel (hypoxanthine, 2-furoylglycine, and 1-hexadecyl-2-azelaoyl-sn-glycero-3-phosphocholine) via five machine learning models. The random forest model effectively discriminated patients with AMD from patents in the other two groups and showed acceptable calibration (area under the curve (AUC) = 1.0; accuracy = 1.0) in both discovery phases 1 and 2. An independent validation phase confirmed the diagnostic model's efficacy (AUC = 0.962; accuracy = 0.88). The three-biomarker panel model demonstrated an AUC of 1.0 in differentiating the severity of AMD via RF machine learning, which was consistent across both the discovery and external validation phases. Additionally, the biomarker concentrations remained stable under repeated freeze-thaw cycles (P > 0.05). CONCLUSIONS This study reveals distinct metabolite variations in the serum of AMD patients, paving the way for the development of the first routine laboratory test for AMD.
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Affiliation(s)
- Shengjie Li
- Department of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; NHC Key Laboratory of Myopia and Related Eye Diseases, Shanghai 200031, China; Key Laboratory of Myopia and Related Eye Diseases, Chinese Academy of Medical Sciences, Shanghai 200031, China; Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai 200031, China.
| | - Yichao Qiu
- Department of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China
| | - Yingzhu Li
- Department of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China
| | - Jianing Wu
- Department of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China
| | - Ning Yin
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China; National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai 200080, China
| | - Jun Ren
- Department of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China
| | - Mingxi Shao
- Department of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China
| | - Jian Yu
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; NHC Key Laboratory of Myopia and Related Eye Diseases, Shanghai 200031, China; Key Laboratory of Myopia and Related Eye Diseases, Chinese Academy of Medical Sciences, Shanghai 200031, China; Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai 200031, China
| | - Yunxiao Song
- Department of Clinical Laboratory, Shanghai Xuhui Central Hospital, Fudan University, Shanghai 200031, China
| | - Xinghuai Sun
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; NHC Key Laboratory of Myopia and Related Eye Diseases, Shanghai 200031, China; Key Laboratory of Myopia and Related Eye Diseases, Chinese Academy of Medical Sciences, Shanghai 200031, China; Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai 200031, China
| | - Shunxiang Gao
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China; National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai 200080, China.
| | - Wenjun Cao
- Department of Clinical Laboratory, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; NHC Key Laboratory of Myopia and Related Eye Diseases, Shanghai 200031, China; Key Laboratory of Myopia and Related Eye Diseases, Chinese Academy of Medical Sciences, Shanghai 200031, China; Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai 200031, China.
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Yan M, Yang J, Zhu H, Zou Q, Zhao H, Sun H. Volatile organic compound exposure in relation to lung cancer: Insights into mechanisms of action through metabolomics. JOURNAL OF HAZARDOUS MATERIALS 2024; 480:135856. [PMID: 39298956 DOI: 10.1016/j.jhazmat.2024.135856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 09/11/2024] [Accepted: 09/14/2024] [Indexed: 09/22/2024]
Abstract
Volatile organic compounds (VOCs) have proven to be hazardous to the human respiratory system. However, the underlying biological mechanisms remain poorly understood. Therefore, targeted determination of eleven VOC metabolites (mVOCs) along with the nontargeted metabolomic analysis was performed on urine samples collected from lung cancer patients and healthy individuals. Nine mVOCs mainly derived from aldehydes, alkenes, amides, and aromatics were detected in > 90 % of the urine samples, suggesting that the participants were ubiquitously exposed to these typical VOCs. A molecular gatekeeper discovery workflow was employed to link the exposure biomarkers with correlated clusters of endogenous metabolites. As a result, multiple metabolic pathways, including amino acid metabolism, steroid hormone biosynthesis and metabolism, and fatty acid β-oxidation were connected with VOC exposure. Furthermore, 16 of 73 molecular gatekeepers were associated with lung cancer and pointed to a few disrupted metabolic pathways related to hydroxysteroids and acylcarnitine. The shift in molecular profiles was validated in rat model post VOC administration. Thereinto, the up-regulation of enzymes involved in acylcarnitine synthesis and transport in rat lung tissues highlighted that the mitochondrial dysfunction may be a potential carcinogenic mechanism. Our findings provide new insights into the mechanisms underlying lung cancer induced by VOC exposure.
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Affiliation(s)
- Mengqi Yan
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Jintao Yang
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Hongkai Zhu
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
| | - Qiang Zou
- Department of Interventional Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China.
| | - Hongzhi Zhao
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Hongwen Sun
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
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Li XJ, Fang C, Zhao RH, Zou L, Miao H, Zhao YY. Bile acid metabolism in health and ageing-related diseases. Biochem Pharmacol 2024; 225:116313. [PMID: 38788963 DOI: 10.1016/j.bcp.2024.116313] [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: 02/18/2024] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 05/26/2024]
Abstract
Bile acids (BAs) have surpassed their traditional roles as lipid solubilizers and regulators of BA homeostasis to emerge as important signalling molecules. Recent research has revealed a connection between microbial dysbiosis and metabolism disruption of BAs, which in turn impacts ageing-related diseases. The human BAs pool is primarily composed of primary BAs and their conjugates, with a smaller proportion consisting of secondary BAs. These different BAs exert complex effects on health and ageing-related diseases through several key nuclear receptors, such as farnesoid X receptor and Takeda G protein-coupled receptor 5. However, the underlying molecular mechanisms of these effects are still debated. Therefore, the modulation of signalling pathways by regulating synthesis and composition of BAs represents an interesting and novel direction for potential therapies of ageing-related diseases. This review provides an overview of synthesis and transportion of BAs in the healthy body, emphasizing its dependence on microbial community metabolic capacity. Additionally, the review also explores how ageing and ageing-related diseases affect metabolism and composition of BAs. Understanding BA metabolism network and the impact of their nuclear receptors, such as farnesoid X receptor and G protein-coupled receptor 5 agonists, paves the way for developing therapeutic agents for targeting BA metabolism in various ageing-related diseases, such as metabolic disorder, hepatic injury, cardiovascular disease, renal damage and neurodegenerative disease.
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Affiliation(s)
- Xiao-Jun Li
- School of Pharmacy, Zhejiang Chinese Medical University, No. 548 Binwen Road, Hangzhou, Zhejiang 310053, China; Southern Medical University Hospital of Integrated Traditional Chinese and Western Medicine, Southern Medical University, No.13, Shi Liu Gang Road, Haizhu District, Guangzhou, Guangdong 510315, China
| | - Chu Fang
- School of Pharmacy, Zhejiang Chinese Medical University, No. 548 Binwen Road, Hangzhou, Zhejiang 310053, China
| | - Rui-Hua Zhao
- School of Pharmacy, Zhejiang Chinese Medical University, No. 548 Binwen Road, Hangzhou, Zhejiang 310053, China
| | - Liang Zou
- School of Food and Bioengineering, Chengdu University, No. 2025 Chengluo Avenue, Chengdu, Sichuan 610106, China
| | - Hua Miao
- School of Pharmacy, Zhejiang Chinese Medical University, No. 548 Binwen Road, Hangzhou, Zhejiang 310053, China.
| | - Ying-Yong Zhao
- School of Pharmacy, Zhejiang Chinese Medical University, No. 548 Binwen Road, Hangzhou, Zhejiang 310053, China; National Key Laboratory of Kidney Diseases, First Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing 100853, China.
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Ling K, Hong M, Jin L, Wang J. Blood metabolomic and postpartum depression: a mendelian randomization study. BMC Pregnancy Childbirth 2024; 24:429. [PMID: 38877415 PMCID: PMC11177545 DOI: 10.1186/s12884-024-06628-3] [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: 01/17/2024] [Accepted: 06/09/2024] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Postpartum depression is a complex mental health condition that often occurs after childbirth and is characterized by persistent sadness, anxiety, and fatigue. Recent research suggests a metabolic component to the disorder. This study aims to investigate the causal relationship between blood metabolites and postpartum depression using mendelian randomization (MR). METHODS This study used a bi-directional MR framework to investigate the causal relationship between 1,400 metabolic biomarkers and postpartum depression. We used two specific genome-wide association studies datasets: one with single nucleotide polymorphisms data from mothers diagnosed with postpartum depression and another with blood metabolite data, both of which focused on people of European ancestry. Genetic variants were chosen as instrumental variables from both datasets using strict criteria to improve the robustness of the MR analysis. The combination of these datasets enabled a thorough examination of genetic influences on metabolic profiles associated with postpartum depression. Statistical analyses were conducted using techniques such as inverse variance weighting, weighted median, and model-based estimation, which enabled rigorous causal inference from the observed associations. postpartum depression was defined using endpoint definitions approved by the FinnGen study's clinical expert groups, which included leading experts in their respective medical fields. RESULTS The MR analysis identified seven metabolites that could be linked to postpartum depression. Out of these, one metabolite was found to be protective, while six were associated with an increased risk of developing the condition. The results were consistent across multiple MR methods, indicating a significant correlation. CONCLUSIONS This study emphasizes the potential of metabolomics for understanding postpartum depression. The discovery of specific metabolites associated with the condition sheds new insights on its pathophysiology and opens up possibilities for future research into targeted treatment strategies.
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Affiliation(s)
- Keng Ling
- Jiaxing Women and Children's Hospital, Wenzhou Medical University, Jiaxing, China
| | - Minping Hong
- Jiaxing Hospital of Traditional Chinese Medical, Jiaxing, China
| | - Liqin Jin
- Jiaxing Women and Children's Hospital, Wenzhou Medical University, Jiaxing, China
| | - Jianguo Wang
- Jiaxing Women and Children's Hospital, Wenzhou Medical University, Jiaxing, China.
- Central Laboratory, Jiaxing Maternity and Child Health Care Hospital, Affiliated Women and Children Hospital, Jiaxing University, Jiaxing, 314000, China.
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Mitchell JM, Chi Y, Thapa M, Pang Z, Xia J, Li S. Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline. PLoS Comput Biol 2024; 20:e1011912. [PMID: 38843301 PMCID: PMC11185459 DOI: 10.1371/journal.pcbi.1011912] [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: 02/12/2024] [Revised: 06/18/2024] [Accepted: 05/20/2024] [Indexed: 06/18/2024] Open
Abstract
To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.
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Affiliation(s)
- Joshua M. Mitchell
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Yuanye Chi
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Maheshwor Thapa
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
- University of Connecticut School of Medicine, Farmington, Connecticut, United States of America
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Dalamaga M. Clinical metabolomics: Useful insights, perspectives and challenges. Metabol Open 2024; 22:100290. [PMID: 39011161 PMCID: PMC11247213 DOI: 10.1016/j.metop.2024.100290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024] Open
Abstract
Metabolomics, a cutting-edge omics technique, is a rapidly advancing field in biomedical research, concentrating on the elucidation of pathogenetic mechanisms and the discovery of novel metabolite signatures predictive of disease risk, aiding in earlier disease detection, prognosis and prediction of treatment response. The capacity of this omics approach to simultaneously quantify thousands of metabolites, i.e. small molecules less than 1500 Da in samples, positions it as a promising tool for research and clinical applications in personalized medicine. Clinical metabolomics studies have proven valuable in understanding cardiometabolic disorders, potentially uncovering diagnostic biomarkers predictive of disease risk. Liquid chromatography-mass spectrometry is the predominant analytical method used in metabolomics, particularly untargeted. Metabolomics combined with extensive genomic data, proteomics, clinical chemistry data, imaging, health records, and other pertinent health-related data may yield significant advances beneficial for both public health initiatives, clinical applications and precision medicine, particularly in rare disorders and multimorbidity. This special issue has gathered original research articles in topics related to clinical metabolomics as well as research articles, reviews, perspectives and highlights in the broader field of translational and clinical metabolic research. Additional research is necessary to identify which metabolites consistently enhance clinical risk prediction across various populations and are causally linked to disease progression.
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Affiliation(s)
- Maria Dalamaga
- Department of Biological Chemistry, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias, 11527, Athens, Greece
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Zanetti KA, Guo L, Husain D, Kelly RS, Lasky-Su J, Broadhurst D, Wheelock CE. Workshop report - interdisciplinary metabolomic epidemiology: the pathway to clinical translation. Metabolomics 2024; 20:60. [PMID: 38773013 PMCID: PMC11108898 DOI: 10.1007/s11306-024-02111-4] [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: 02/29/2024] [Accepted: 03/19/2024] [Indexed: 05/23/2024]
Abstract
Metabolomic epidemiology studies are complex and require a broad array of domain expertise. Although many metabolite-phenotype associations have been identified; to date, few findings have been translated to the clinic. Bridging this gap requires understanding of both the underlying biology of these associations and their potential clinical implications, necessitating an interdisciplinary team approach. To address this need in metabolomic epidemiology, a workshop was held at Metabolomics 2023 in Niagara Falls, Ontario, Canada that highlighted the domain expertise needed to effectively conduct these studies -- biochemistry, clinical science, epidemiology, and assay development for biomarker validation -- and emphasized the role of interdisciplinary teams to move findings towards clinical translation.
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Affiliation(s)
- Krista A Zanetti
- Office of Nutrition Research, Division of Program Coordination, Planning, and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, MD, USA.
| | | | - Deeba Husain
- Mass Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - David Broadhurst
- School of Science, Edith Cowan University, Joondalup, Perth, Western Australia
| | - Craig E Wheelock
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden
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Shojima N, Yamauchi T. Common metabolite for microangiopathy in Japanese and European populations. J Diabetes Investig 2024; 15:523-524. [PMID: 38517118 PMCID: PMC11060153 DOI: 10.1111/jdi.14189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/02/2024] [Accepted: 03/03/2024] [Indexed: 03/23/2024] Open
Affiliation(s)
- Nobuhiro Shojima
- Department of Diabetes and Metabolic Diseases, Graduate School of MedicineThe University of TokyoTokyoJapan
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of MedicineThe University of TokyoTokyoJapan
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Woodruff MC, Faliti CE, Sanz I. Systems biology of B cells in COVID-19. Semin Immunol 2024; 72:101875. [PMID: 38489999 DOI: 10.1016/j.smim.2024.101875] [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: 01/29/2024] [Revised: 03/04/2024] [Accepted: 03/04/2024] [Indexed: 03/17/2024]
Abstract
The integration of multi-'omic datasets into complex systems-wide assessments has become a mainstay in immunologic investigation. This focus on high-dimensional data collection and analysis was on full display in the investigation of COVID-19, the respiratory illness resulting from infection by the novel coronavirus SARS-CoV-2. Particularly in the area of B cell biology, tremendous efforts in both cellular and serologic investigation have resulted in an increasingly detailed mapping of the coordinated effector, memory, and antibody secreting cell responses that underpin the development of humoral immunity in response to primary viral infection. Further, the rapid development and deployment of effective vaccines has allowed for the assessment of developing memory responses across a wide variety of immune contexts, including in patients with compromised immune function. The result has been a period of rapid gains in the understanding of B cell biology unrestricted to the study of COVID-19. Here, we outline the systems-level technologies that have been routinely implemented in these investigations throughout the pandemic, and discuss how their use has led to clear and applicable gains in pursuance of the amelioration of human infectious disease and beyond.
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Affiliation(s)
- Matthew C Woodruff
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, Emory University, Atlanta, GA, USA; Emory Autoimmunity Center of Excellence, Emory University, Atlanta, GA, USA.
| | - Caterina E Faliti
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, Emory University, Atlanta, GA, USA; Emory Autoimmunity Center of Excellence, Emory University, Atlanta, GA, USA.
| | - Ignacio Sanz
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, Emory University, Atlanta, GA, USA; Emory Autoimmunity Center of Excellence, Emory University, Atlanta, GA, USA
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Mitchell JM, Chi Y, Thapa M, Pang Z, Xia J, Li S. Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580048. [PMID: 38405981 PMCID: PMC10888883 DOI: 10.1101/2024.02.13.580048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.
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Affiliation(s)
- Joshua M. Mitchell
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Yuanye Chi
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Maheshwor Thapa
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
- University of Connecticut School of Medicine, Farmington, CT 06032, USA
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