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Song B, Liu D, Lu J, Tao X, Peng X, Wu T, Hou YM, Wang J, Regenstein JM, Zhou P. Lipidomic Comparisons of Whole Cream Buttermilk Whey and Cheese Whey Cream Buttermilk of Caprine Milk. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:11268-11277. [PMID: 38695399 DOI: 10.1021/acs.jafc.4c00792] [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: 05/16/2024]
Abstract
Buttermilk is a potential material for the production of a milk fat globule membrane (MFGM) and can be mainly classified into two types: whole cream buttermilk and cheese whey cream buttermilk (WCB). Due to the high casein micelle content of whole cream buttermilk, the removal of casein micelles to improve the purity of MFGM materials is always required. This study investigated the effects of rennet and acid coagulation on the lipid profile of buttermilk rennet-coagulated whey (BRW) and buttermilk acid-coagulated whey (BAW) and compared them with WCB. BRW has significantly higher phospholipids (PLs) and ganglioside contents than BAW and WCB. The abundance of arachidonic acid (ARA)- and eicosapentaenoic acid (EPA)-structured PLs was higher in WCB, while docosahexaenoic acid (DHA)-structured PLs were higher in BRW, indicating that BRW and WCB intake might have a greater effect on improving cardiovascular conditions and neurodevelopment. WCB and BRW had a higher abundance of plasmanyl PL and plasmalogen PL, respectively. Phosphatidylcholine (PC) (28:1), LPE (20:5), and PC (26:0) are characteristic lipids among BRW, BAW, and WCB, and they can be used to distinguish MFGM-enriched whey from different sources.
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Affiliation(s)
- Bo Song
- State Key Laboratory of Food Science and Resources, School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Dasong Liu
- State Key Laboratory of Food Science and Resources, School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Jing Lu
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Key Laboratory of Flavor Chemistry, School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
| | - Xiumei Tao
- State Key Laboratory of Food Science and Resources, School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China
- Analysis and Testing Center, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xiaoyu Peng
- Ausnutria Dairy (China) Co. Ltd., Changsha, Hunan 410200, China
| | - Tong Wu
- Hyproca Nutrition Co., Ltd., Changsha, Hunan 410200, China
| | - Yan-Mei Hou
- Hyproca Nutrition Co., Ltd., Changsha, Hunan 410200, China
| | - Jiaqi Wang
- Ausnutria Dairy (China) Co. Ltd., Changsha, Hunan 410200, China
| | - Joe M Regenstein
- Department of Food Science, Cornell University, Ithaca, New York 14853-7201, United States
| | - Peng Zhou
- State Key Laboratory of Food Science and Resources, School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, 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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Shields PG. Role of untargeted omics biomarkers of exposure and effect for tobacco research. ADDICTION NEUROSCIENCE 2023; 7:100098. [PMID: 37396411 PMCID: PMC10310069 DOI: 10.1016/j.addicn.2023.100098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.
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Affiliation(s)
- Peter G. Shields
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH
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Hoffmann N, Mayer G, Has C, Kopczynski D, Al Machot F, Schwudke D, Ahrends R, Marcus K, Eisenacher M, Turewicz M. A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics. Metabolites 2022; 12:584. [PMID: 35888710 PMCID: PMC9319858 DOI: 10.3390/metabo12070584] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/17/2022] [Accepted: 06/19/2022] [Indexed: 12/13/2022] Open
Abstract
Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography-mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.
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Affiliation(s)
- Nils Hoffmann
- Forschungszentrum Jülich GmbH, Institute for Bio- and Geosciences (IBG-5), 52425 Jülich, Germany
| | - Gerhard Mayer
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany;
| | - Canan Has
- Biological Mass Spectrometry, Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany;
- University Hospital Carl Gustav Carus, 01307 Dresden, Germany
- CENTOGENE GmbH, 18055 Rostock, Germany
| | - Dominik Kopczynski
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (D.K.); (R.A.)
| | - Fadi Al Machot
- Faculty of Science and Technology, Norwegian University for Life Science (NMBU), 1433 Ås, Norway;
| | - Dominik Schwudke
- Bioanalytical Chemistry, Forschungszentrum Borstel, Leibniz Lung Center, 23845 Borstel, Germany;
- Airway Research Center North, German Center for Lung Research (DZL), 23845 Borstel, Germany
- German Center for Infection Research (DZIF), TTU Tuberculosis, 23845 Borstel, Germany
| | - Robert Ahrends
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (D.K.); (R.A.)
| | - Katrin Marcus
- Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Ruhr University Bochum, 44801 Bochum, Germany; (K.M.); (M.E.)
| | - Martin Eisenacher
- Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Ruhr University Bochum, 44801 Bochum, Germany; (K.M.); (M.E.)
- Faculty of Medicine, Medizinisches Proteom-Center, Ruhr University Bochum, 44801 Bochum, Germany
| | - Michael Turewicz
- Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, 85764 Neuherberg, Germany
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5
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Bilbao A, Gibbons BC, Stow SM, Kyle JE, Bloodsworth KJ, Payne SH, Smith RD, Ibrahim YM, Baker ES, Fjeldsted JC. A Preprocessing Tool for Enhanced Ion Mobility-Mass Spectrometry-Based Omics Workflows. J Proteome Res 2022; 21:798-807. [PMID: 34382401 PMCID: PMC8837709 DOI: 10.1021/acs.jproteome.1c00425] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The ability to improve the data quality of ion mobility-mass spectrometry (IM-MS) measurements is of great importance for enabling modular and efficient computational workflows and gaining better qualitative and quantitative insights from complex biological and environmental samples. We developed the PNNL PreProcessor, a standalone and user-friendly software housing various algorithmic implementations to generate new MS-files with enhanced signal quality and in the same instrument format. Different experimental approaches are supported for IM-MS based on Drift-Tube (DT) and Structures for Lossless Ion Manipulations (SLIM), including liquid chromatography (LC) and infusion analyses. The algorithms extend the dynamic range of the detection system, while reducing file sizes for faster and memory-efficient downstream processing. Specifically, multidimensional smoothing improves peak shapes of poorly defined low-abundance signals, and saturation repair reconstructs the intensity profile of high-abundance peaks from various analyte types. Other functionalities are data compression and interpolation, IM demultiplexing, noise filtering by low intensity threshold and spike removal, and exporting of acquisition metadata. Several advantages of the tool are illustrated, including an increase of 19.4% in lipid annotations and a two-times faster processing of LC-DT IM-MS data-independent acquisition spectra from a complex lipid extract of a standard human plasma sample. The software is freely available at https://omics.pnl.gov/software/pnnl-preprocessor.
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Affiliation(s)
- Aivett Bilbao
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Bryson C Gibbons
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Sarah M Stow
- Agilent Technologies, Santa Clara, California 95051, United States
| | - Jennifer E Kyle
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Kent J Bloodsworth
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, Utah 84602, United States
| | - Richard D Smith
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Yehia M Ibrahim
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - John C Fjeldsted
- Agilent Technologies, Santa Clara, California 95051, United States
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