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Lee AJ, Gangi LR, Zandkarimi F, Stockwell BR, Hung CT. Red blood cell exposure increases chondrocyte susceptibility to oxidative stress following hemarthrosis. Osteoarthritis Cartilage 2023; 31:1365-1376. [PMID: 37364817 PMCID: PMC10529126 DOI: 10.1016/j.joca.2023.06.007] [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: 12/13/2022] [Revised: 05/11/2023] [Accepted: 06/13/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVE The detrimental effects of blood exposure on articular tissues are well characterized, but the individual contributions of specific whole blood components are yet to be fully elucidated. Better understanding of mechanisms that drive cell and tissue damage in hemophilic arthropathy will inform novel therapeutic strategies. The studies here aimed to identify the specific contributions of intact and lysed red blood cells (RBCs) on cartilage and the therapeutic potential of Ferrostatin-1 in the context of lipid changes, oxidative stress, and ferroptosis. METHODS Changes to biochemical and mechanical properties following intact RBC treatment were assessed in human chondrocyte-based tissue-engineered cartilage constructs and validated against human cartilage explants. Chondrocyte monolayers were assayed for changes to intracellular lipid profiles and the presence of oxidative and ferroptotic mechanisms. RESULTS Markers of tissue breakdown were observed in cartilage constructs without parallel losses in DNA (control: 786.3 (102.2) ng/mg; RBCINT: 751 (126.4) ng/mg; P = 0.6279), implicating nonlethal chondrocyte responses to intact RBCs. Dose-dependent loss of viability in response to intact and lysed RBCs was observed in chondrocyte monolayers, with greater toxicity observed with lysates. Intact RBCs induced changes to chondrocyte lipid profiles, upregulating highly oxidizable fatty acids (e.g., FA 18:2) and matrix disrupting ceramides. RBC lysates induced cell death via oxidative mechanisms that resemble ferroptosis. CONCLUSIONS Intact RBCs induce intracellular phenotypic changes to chondrocytes that increase vulnerability to tissue damage while lysed RBCs have a more direct influence on chondrocyte death by mechanisms that are representative of ferroptosis.
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Affiliation(s)
- Andy J Lee
- Department of Biomedical Engineering, Columbia University, 351 Engineering Terrace, Mail Code 8904, 1210 Amsterdam Avenue, New York, NY, USA.
| | - Lianna R Gangi
- Department of Biomedical Engineering, Columbia University, 351 Engineering Terrace, Mail Code 8904, 1210 Amsterdam Avenue, New York, NY, USA.
| | - Fereshteh Zandkarimi
- Department of Chemistry, Columbia University, 216 Havemeyer Hall, 3000 Broadway, Mail Code 3183, New York, NY, USA.
| | - Brent R Stockwell
- Department of Chemistry, Columbia University, 216 Havemeyer Hall, 3000 Broadway, Mail Code 3183, New York, NY, USA; Department of Biological Sciences, Columbia University, 1208 NWC Building, 550 West 120th St. M.C. 4846, New York, NY, USA.
| | - Clark T Hung
- Department of Biomedical Engineering, Columbia University, 351 Engineering Terrace, Mail Code 8904, 1210 Amsterdam Avenue, New York, NY, USA; Department of Orthopaedic Surgery, Columbia University, New York, NY, USA.
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Bunyat-Zada AR, Ross AC. Highlights of bioinformatic tools and methods for validating bioinformatics derived hypotheses for microbial natural products research. Curr Opin Chem Biol 2023; 76:102367. [PMID: 37453164 DOI: 10.1016/j.cbpa.2023.102367] [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: 12/20/2022] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 07/18/2023]
Abstract
Historically, bacterial natural products have served as an excellent source of drug leads, however, in recent decades the rate of discovery has slowed due to multiple challenges. Rapid advances in genome sequencing science in recent years have revealed the vast untapped encoded potential of bacteria to make natural products. To access these molecules, researchers can employ the ever-growing array of bioinformatic tools at their disposal and leverage newly developed experimental approaches to validate these bioinformatic-driven hypotheses. When used together effectively, bioinformatic and experimental tools enable researchers to deeply examine the full diversity of bacterial natural products. This review briefly outlines recent bioinformatic tools that can facilitate natural product research in bacteria including the use of CRISPR, co-occurrence network analysis, and combinatorial generation of microbial natural products to test bioinformatic hypotheses in the lab.
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Affiliation(s)
- Amir R Bunyat-Zada
- Department of Chemistry, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Avena C Ross
- Department of Chemistry, Queen's University, Kingston, ON, K7L 3N6, Canada.
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Jena A, Montoya CA, Fraser K, Giezenaar C, Young W, Mullaney JA, Dilger RN, Roy D, McNabb WC, Roy NC. Metabolite profiling of peripheral blood plasma in pigs in early postnatal life fed whole bovine, caprine or ovine milk. Front Nutr 2023; 10:1242301. [PMID: 37823089 PMCID: PMC10564076 DOI: 10.3389/fnut.2023.1242301] [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: 06/19/2023] [Accepted: 09/13/2023] [Indexed: 10/13/2023] Open
Abstract
Ruminants' milk is commonly used for supplying nutrients to infants when breast milk is unavailable or limited. Previous studies have highlighted the differences between ruminants' milk composition, digestion, absorption, and fermentation. However, whether consuming different ruminants' milk impact the appearance of the circulatory blood metabolites in the early postnatal life is not well understood. The analysis conducted here aimed to determine the effect of feeding exclusively whole milk from bovine, caprine or ovine species to pigs, approximately 7 days-old for 15 days, on circulatory blood plasma metabolites. Relative intensities of plasma metabolites were detected using a liquid chromatography-mass spectrometry based metabolomic approach. Seven polar and 83 non-polar (lipids) metabolites in plasma were significantly different (false discovery rate < 0.05) between milk treatments. These included polar metabolites involved in amino acid metabolism and lipids belonging to phosphatidylcholine, lysophosphatidylcholine, sphingomyelin, and triglycerides. Compared to the caprine or bovine milk group, the relative intensities of polar metabolites and unsaturated triglycerides were higher in the peripheral circulation of the ovine milk group. In contrast, relative intensities of saturated triglycerides and phosphatidylcholine were higher in the bovine milk group compared to the ovine or caprine milk group. In addition, correlations were identified between amino acid and lipid intake and their appearance in peripheral blood circulation. The results highlighted that consuming different ruminants' milk influences the plasma appearance of metabolites, especially lipids, that may contribute to early postnatal life development in pigs.
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Affiliation(s)
- Ankita Jena
- Riddet Institute, Massey University, Palmerston North, New Zealand
- School of Food and Advanced Technology, College of Sciences, Massey University, Palmerston North, New Zealand
- AgResearch, Palmerston North, New Zealand
| | - Carlos A. Montoya
- Riddet Institute, Massey University, Palmerston North, New Zealand
- AgResearch, Palmerston North, New Zealand
| | - Karl Fraser
- Riddet Institute, Massey University, Palmerston North, New Zealand
- AgResearch, Palmerston North, New Zealand
- High-Value Nutrition National Science Challenge, Auckland, New Zealand
| | - Caroline Giezenaar
- Riddet Institute, Massey University, Palmerston North, New Zealand
- Food Experience and Sensory Testing (FEAST) Laboratory, School of Food and Advanced Technology, Massey University, Palmerston North, New Zealand
| | - Wayne Young
- Riddet Institute, Massey University, Palmerston North, New Zealand
- AgResearch, Palmerston North, New Zealand
- High-Value Nutrition National Science Challenge, Auckland, New Zealand
| | - Jane A. Mullaney
- Riddet Institute, Massey University, Palmerston North, New Zealand
- AgResearch, Palmerston North, New Zealand
- High-Value Nutrition National Science Challenge, Auckland, New Zealand
| | - Ryan N. Dilger
- Department of Animal Sciences, University of Illinois, Urbana, IL, United States
| | - Debashree Roy
- Riddet Institute, Massey University, Palmerston North, New Zealand
| | - Warren C. McNabb
- Riddet Institute, Massey University, Palmerston North, New Zealand
- High-Value Nutrition National Science Challenge, Auckland, New Zealand
| | - Nicole C. Roy
- Riddet Institute, Massey University, Palmerston North, New Zealand
- High-Value Nutrition National Science Challenge, Auckland, New Zealand
- Department of Human Nutrition, University of Otago, Dunedin, New Zealand
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Spangenberg SH, Palermo A, Gazaniga NR, Martínez-Peña F, Guijas C, Chin EN, Rinschen MM, Sander PN, Webb B, Pereira LE, Jia Y, Meitz L, Siuzdak G, Lairson LL. Hydroxyproline metabolism enhances IFN-γ-induced PD-L1 expression and inhibits autophagic flux. Cell Chem Biol 2023; 30:1115-1134.e10. [PMID: 37467751 DOI: 10.1016/j.chembiol.2023.06.016] [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: 09/21/2022] [Revised: 04/20/2023] [Accepted: 06/16/2023] [Indexed: 07/21/2023]
Abstract
The immune checkpoint protein PD-L1 plays critical roles in both immune system homeostasis and tumor progression. Impaired PD-1/PD-L1 function promotes autoimmunity and PD-L1 expression within tumors promotes immune evasion. If and how changes in metabolism or defined metabolites regulate PD-L1 expression is not fully understood. Here, using a metabolomics activity screening-based approach, we have determined that hydroxyproline (Hyp) significantly and directly enhances adaptive (i.e., IFN-γ-induced) PD-L1 expression in multiple relevant myeloid and cancer cell types. Mechanistic studies reveal that Hyp acts as an inhibitor of autophagic flux, which allows it to regulate this negative feedback mechanism, thereby contributing to its overall effect on PD-L1 expression. Due to its prevalence in fibrotic tumors, these findings suggest that hydroxyproline could contribute to the establishment of an immunosuppressive tumor microenvironment and that Hyp metabolism could be targeted to pharmacologically control PD-L1 expression for the treatment of cancer or autoimmune diseases.
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Affiliation(s)
| | - Amelia Palermo
- Scripps Center for Metabolomics, the Scripps Research Institute, La Jolla, CA 92037, USA; Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nathalia R Gazaniga
- Department of Chemistry, the Scripps Research Institute, La Jolla, CA 92037, USA
| | | | - Carlos Guijas
- Scripps Center for Metabolomics, the Scripps Research Institute, La Jolla, CA 92037, USA
| | - Emily N Chin
- Department of Chemistry, the Scripps Research Institute, La Jolla, CA 92037, USA
| | - Markus M Rinschen
- Scripps Center for Metabolomics, the Scripps Research Institute, La Jolla, CA 92037, USA
| | - Philipp N Sander
- Department of Chemistry, the Scripps Research Institute, La Jolla, CA 92037, USA
| | - Bill Webb
- Scripps Center for Metabolomics, the Scripps Research Institute, La Jolla, CA 92037, USA
| | - Laura E Pereira
- Department of Chemistry, the Scripps Research Institute, La Jolla, CA 92037, USA
| | - Ying Jia
- Department of Chemistry, the Scripps Research Institute, La Jolla, CA 92037, USA
| | - Lance Meitz
- Department of Chemistry, the Scripps Research Institute, La Jolla, CA 92037, USA
| | - Gary Siuzdak
- Scripps Center for Metabolomics, the Scripps Research Institute, La Jolla, CA 92037, USA; Department of Integrative Structural and Computational Biology, La Jolla, CA 92037, USA.
| | - Luke L Lairson
- Department of Chemistry, the Scripps Research Institute, La Jolla, CA 92037, USA.
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Li T, Zhang K, Niu X, Chen W, Yang X, Gong X, Tu P, Wang Y, Liu W, Song Y. MS/MS fingerprint comparison between adjacent generations enables substructure identification: Flavonoid glycosides as cases. J Pharm Biomed Anal 2023; 234:115559. [PMID: 37393693 DOI: 10.1016/j.jpba.2023.115559] [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: 03/26/2023] [Revised: 06/20/2023] [Accepted: 06/27/2023] [Indexed: 07/04/2023]
Abstract
MS/MS spectrum matching currently serves as a favored means to identify the concerned metabolites attributing to the accessibility of several famous databases. However, the rule that takes the entire structure into account frequently leads to "0 hit" when inquiring MS/MS (usually MS2) spectrum in the databases. Conjugation plays an important role for the high-level structural diversity of metabolites in all organisms, and a given conjugate usually consists of two or more substructures. If MS3 spectra participate in database retrieval, the structural annotation potential of those databases should be dramatically expanded via identifying substructures. Attributing to the ubiquitous distribution pattern, flavonoid glycosides were deployed as the representative family to justify whether the primary fragment ion termed as Y0+, resulted from neutral loss of glycosyl residue(s), generated identical MS3 spectrum with MS2 spectrum of the aglycone cation namely [A+H]+. Because of owning unique ability to measure MS/MS spectrum with the exactly desired exciting energy, linear ion trap chamber of Qtrap-MS was responsible for generating the desired MS3 and MS2 spectra. When taking both m/z and ion intensity features into consideration, the findings included: 1) glycosides sharing identical aglycones produced the same MS3 spectra for Y0+; 2) different MS3 spectra for Y0+ occurred amongst glycosides bearing distinct, even isomeric, aglycones; 3) isomeric aglycones generated different MS2 spectra; and 4) MS3 spectra for Y0+ agreed with MS2 spectra of [A+H]+ when comparing paired glycoside and aglycone. Together, fingerprint comparison between MS3 and MS2 spectra could structurally annotate the substructures and further advance MS/MS spectrum matching towards the identification of, but not limited to, aglycones for flavonoid glycosides.
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Affiliation(s)
- Ting Li
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Ke Zhang
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Xiaoya Niu
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Wei Chen
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Xiangfen Yang
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Xingcheng Gong
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Pengfei Tu
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yitao Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa 999078, Macao
| | - Wenjing Liu
- School of Pharmacy, Henan University of Chinese Medicine, Jinshui East Road, Zhengdong New District, Zhengzhou 450046, China.
| | - Yuelin Song
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China.
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Xue X, Sun H, Yang M, Liu X, Hu HY, Deng Y, Wang X. Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective. Anal Chem 2023; 95:13733-13745. [PMID: 37688541 DOI: 10.1021/acs.analchem.3c02540] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2023]
Abstract
The interpretation of spectral data, including mass, nuclear magnetic resonance, infrared, and ultraviolet-visible spectra, is critical for obtaining molecular structural information. The development of advanced sensing technology has multiplied the amount of available spectral data. Chemical experts must use basic principles corresponding to the spectral information generated by molecular fragments and functional groups. This is a time-consuming process that requires a solid professional knowledge base. In recent years, the rapid development of computer science and its applications in cheminformatics and the emergence of computer-aided expert systems have greatly reduced the difficulty in analyzing large quantities of data. For expert systems, however, the problem-solving strategy must be known in advance or extracted by human experts and translated into algorithms. Gratifyingly, the development of artificial intelligence (AI) methods has shown great promise for solving such problems. Traditional algorithms, including the latest neural network algorithms, have shown great potential for both extracting useful information and processing massive quantities of data. This Perspective highlights recent innovations covering all of the emerging AI-based spectral interpretation techniques. In addition, the main limitations and current obstacles are presented, and the corresponding directions for further research are proposed. Moreover, this Perspective gives the authors' personal outlook on the development and future applications of spectral interpretation.
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Affiliation(s)
- Xi Xue
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
- Beijing Key Laboratory of Active Substances Discovery and Drugability Evaluation, Department of Medicinal Chemistry, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, P. R. China
| | - Hanyu Sun
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
- Beijing Key Laboratory of Active Substances Discovery and Drugability Evaluation, Department of Medicinal Chemistry, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, P. R. China
| | - Minjian Yang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
- Beijing Key Laboratory of Active Substances Discovery and Drugability Evaluation, Department of Medicinal Chemistry, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, P. R. China
| | - Xue Liu
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Hai-Yu Hu
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd. Beijing 100080, China
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaojian Wang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China
- CarbonSilicon AI Technology Co., Ltd. Beijing 100080, China
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57
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Zhao T, Xing S, Yu H, Huan T. De Novo Cleaning of Chimeric MS/MS Spectra for LC-MS/MS-Based Metabolomics. Anal Chem 2023; 95:13018-13028. [PMID: 37603462 DOI: 10.1021/acs.analchem.3c00736] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
The purity of tandem mass spectrometry (MS/MS) is essential to MS/MS-based metabolite annotation and unknown exploration. This work presents a de novo approach to cleaning chimeric MS/MS spectra generated in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics. The assumption is that true fragments and their precursors are well correlated across the samples in a study, while false or contamination fragments are rather independent. Using data simulation, this work starts with an investigation of the negative effects of chimeric MS/MS spectra on spectral similarity analysis and molecular networking. Next, the characteristics of true and false fragments in chimeric MS/MS spectra were investigated using MS/MS of chemical standards. We recognized three fragment peak attributes indicative of whether a peak is a false fragment, including (1) intensity ratio fluctuation, (2) appearance rate, and (3) relative intensity. Using these attributes, we tested three machine learning models and identified XGBoost as the best model to achieve an area under the precision-recall curve of 0.98 for a clear separation between true and false fragments. Based on the trained model, we constructed an automated bioinformatic platform, DNMS2Purifier (short for de novo MS2Purifier), for metabolic features from metabolomics studies. DNMS2Purifier recognizes and processes chimeric MS/MS spectra without additional sample analysis or library confirmation. DNMS2Purifer was evaluated on a metabolomics data set generated with different MS/MS precursor isolation windows. It successfully captured the increase in the number of false fragments from the increased isolation window. DNMS2Purifier was also compared to MS2Purifier, an existing MS/MS spectral cleaning tool based on the addition of data-independent acquisition (DIA) analysis. Results indicated that DNMS2Purifier uniquely recognizes false fragments, which complements the previous DIA-based approach. Finally, DNMS2Purifier was demonstrated using a real experimental metabolomics study, showing improved MS/MS spectral quality and leading to an improved spectral match ratio and molecular networking outcome.
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Affiliation(s)
- Tingting Zhao
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
| | - Shipei Xing
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
| | - Huaxu Yu
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
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Rosen Vollmar AK, Rattray NJW, Cai Y, Jain A, Yan H, Deziel NC, Calafat AM, Wilcox AJ, Jukic AMZ, Johnson CH. Urinary Paraben Concentrations and Associations with the Periconceptional Urinary Metabolome: Untargeted and Targeted Metabolomics Analyses of Participants from the Early Pregnancy Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:97006. [PMID: 37702489 PMCID: PMC10498870 DOI: 10.1289/ehp12125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 08/05/2023] [Accepted: 08/10/2023] [Indexed: 09/14/2023]
Abstract
BACKGROUND Parabens, found in everyday items from personal care products to foods, are chemicals with endocrine-disrupting activity, which has been shown to influence reproductive function. OBJECTIVES This study investigated whether urinary concentrations of methylparaben, propylparaben, or butylparaben were associated with the urinary metabolome during the periconceptional period, a critical window for female reproductive function. Changes to the periconceptional urinary metabolome could provide insights into the mechanisms by which parabens could impact fertility. METHODS Urinary paraben concentrations were measured in paired pre- and postconception urine samples from 42 participants in the Early Pregnancy Study, a prospective cohort of 221 women attempting to conceive. We performed untargeted and targeted metabolomics analyses using ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry. We used principal component analysis, orthogonal partial least-squares discriminant analysis, and permutation testing, coupled with univariate statistical analyses, to find metabolites associated with paraben concentration at the two time points. Potential confounders were identified with a directed acyclic graph and used to adjust results with multivariable linear regression. Metabolites were identified using fragmentation data. RESULTS Seven metabolites were associated with paraben concentration (variable importance to projection score > 1 , false discovery rate-corrected q -value < 0.1 ). We identified four diet-related metabolites to the Metabolomics Standards Initiative (MSI) certainty of identification level 2, including metabolites from smoke flavoring, grapes, and olive oil. One metabolite was identified to the class level only (MSI level 3). Two metabolites were unidentified (MSI level 4). After adjustment, three metabolites remained associated with methylparaben and propylparaben, two of which were diet-related. No metabolomic markers of endocrine disruption were associated with paraben concentrations. DISCUSSION This study identified novel relationships between urinary paraben concentrations and diet-related metabolites but not with metabolites on endocrine-disrupting pathways, as hypothesized. It demonstrates the feasibility of integrating untargeted metabolomics data with environmental exposure information and epidemiological adjustment for confounders. The findings underscore a potentially important connection between diet and paraben exposure, with applications to nutritional epidemiology and dietary exposure assessment. https://doi.org/10.1289/EHP12125.
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Affiliation(s)
- Ana K Rosen Vollmar
- Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Nicholas J W Rattray
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Yuping Cai
- Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Abhishek Jain
- Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Hong Yan
- Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Nicole C Deziel
- Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Antonia M Calafat
- Organic Analytical Toxicology Branch, National Center for Environmental Health, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Allen J Wilcox
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Anne Marie Z Jukic
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Caroline H Johnson
- Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
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Lan J, Greter G, Streckenbach B, Wanner B, Arnoldini M, Zenobi R, Slack E. Non-invasive monitoring of microbiota and host metabolism using secondary electrospray ionization-mass spectrometry. CELL REPORTS METHODS 2023; 3:100539. [PMID: 37671025 PMCID: PMC10475793 DOI: 10.1016/j.crmeth.2023.100539] [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: 07/21/2022] [Revised: 05/08/2023] [Accepted: 06/28/2023] [Indexed: 09/07/2023]
Abstract
The metabolic "handshake" between the microbiota and its mammalian host is a complex, dynamic process with major influences on health. Dissecting the interaction between microbial species and metabolites found in host tissues has been a challenge due to the requirement for invasive sampling. Here, we demonstrate that secondary electrospray ionization-mass spectrometry (SESI-MS) can be used to non-invasively monitor metabolic activity of the intestinal microbiome of a live, awake mouse. By comparing the headspace metabolome of individual gut bacterial culture with the "volatilome" (metabolites released to the atmosphere) of gnotobiotic mice, we demonstrate that the volatilome is characteristic of the dominant colonizing bacteria. Combining SESI-MS with feeding heavy-isotope-labeled microbiota-accessible sugars reveals the presence of microbial cross-feeding within the animal intestine. The microbiota is, therefore, a major contributor to the volatilome of a living animal, and it is possible to capture inter-species interaction within the gut microbiota using volatilome monitoring.
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Affiliation(s)
- Jiayi Lan
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | - Giorgia Greter
- Department of Health Sciences and Technology, ETH Zurich, 8093 Zurich, Switzerland
| | - Bettina Streckenbach
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | | | - Markus Arnoldini
- Department of Health Sciences and Technology, ETH Zurich, 8093 Zurich, Switzerland
| | - Renato Zenobi
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | - Emma Slack
- Department of Health Sciences and Technology, ETH Zurich, 8093 Zurich, Switzerland
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Koussiouris J, Looby N, Kulasingam V, Chandran V. A Solid-Phase Microextraction-Liquid Chromatography-Mass Spectrometry Method for Analyzing Serum Lipids in Psoriatic Disease. Metabolites 2023; 13:963. [PMID: 37623906 PMCID: PMC10456752 DOI: 10.3390/metabo13080963] [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: 07/13/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
Approximately 25% of psoriasis patients have an inflammatory arthritis termed psoriatic arthritis (PsA). There is strong interest in identifying and validating biomarkers that can accurately and reliably predict conversion from psoriasis to PsA using novel technologies such as metabolomics. Lipids, in particular, are of key interest in psoriatic disease. We sought to develop a liquid chromatography-mass spectrometry (LC-MS) method to be used in conjunction with solid-phase microextraction (SPME) for analyzing fatty acids and similar molecules. A total of 25 chromatographic methods based on published lipid studies were tested on two LC columns. As a proof of concept, serum samples from psoriatic disease patients (n = 27 psoriasis and n = 26 PsA) were processed using SPME and run on the selected LC-MS method. The method that was best for analyzing fatty acids and fatty acid-like molecules was optimized and applied to serum samples. A total of 18 tentatively annotated features classified as fatty acids and other lipid compounds were statistically significant between psoriasis and PsA groups using both multivariate and univariate approaches. The SPME-LC-MS method developed and optimized was capable of detecting fatty acids and similar lipids that may aid in differentiating psoriasis and PsA patients.
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Affiliation(s)
- John Koussiouris
- Schroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada; (J.K.); (N.L.)
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada;
| | - Nikita Looby
- Schroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada; (J.K.); (N.L.)
| | - Vathany Kulasingam
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada;
- Division of Clinical Biochemistry, Laboratory Medicine Program, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Vinod Chandran
- Schroeder Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada; (J.K.); (N.L.)
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada;
- Division of Rheumatology, Department of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
- Department of Medicine, Memorial University, St. John’s, NL A1B 3V6, Canada
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61
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Harwood TV, Treen DGC, Wang M, de Jong W, Northen TR, Bowen BP. BLINK enables ultrafast tandem mass spectrometry cosine similarity scoring. Sci Rep 2023; 13:13462. [PMID: 37596301 PMCID: PMC10439109 DOI: 10.1038/s41598-023-40496-9] [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: 03/28/2023] [Accepted: 08/11/2023] [Indexed: 08/20/2023] Open
Abstract
Metabolomics has a long history of using cosine similarity to match experimental tandem mass spectra to databases for compound identification. Here we introduce the Blur-and-Link (BLINK) approach for scoring cosine similarity. By bypassing fragment alignment and simultaneously scoring all pairs of spectra using sparse matrix operations, BLINK is over 3000 times faster than MatchMS, a widely used loop-based alignment and scoring implementation. Using a similarity cutoff of 0.7, BLINK and MatchMS had practically equivalent identification agreement, and greater than 99% of their scores and matching ion counts were identical. This performance improvement can enable calculations to be performed that would typically be limited by time and available computational resources.
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Affiliation(s)
- Thomas V Harwood
- Environmental Genomics and Systems Biology Division, The DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA, 94720, USA
| | - Daniel G C Treen
- Environmental Genomics and Systems Biology Division, The DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA, 94720, USA
| | - Mingxun Wang
- Department of Computer Science and Engineering, University of California Riverside, 900 University Avenue, Riverside, CA, 92521, USA
| | - Wibe de Jong
- Computational Chemistry, Materials and Climate Group, Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA, 94720, USA
| | - Trent R Northen
- Environmental Genomics and Systems Biology Division, The DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA, 94720, USA
| | - Benjamin P Bowen
- Environmental Genomics and Systems Biology Division, The DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA, 94720, USA.
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62
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Karunaratne E, Hill DW, Dührkop K, Böcker S, Grant DF. Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification. Anal Chem 2023; 95:11901-11907. [PMID: 37540774 DOI: 10.1021/acs.analchem.3c00937] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2023]
Abstract
The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability. Here, we present results of combining experimental and computational mass and IR spectral data for high-throughput nontargeted chemical structure identification. Experimental MS/MS and gas-phase IR data for 148 test compounds were obtained from NIST. Candidate structures for each of the test compounds were obtained from PubChem (mean = 4444 candidate structures per test compound). Our workflow used CSI:FingerID to initially score and rank the candidate structures. The top 1000 ranked candidates were subsequently used for IR spectra prediction, scoring, and ranking using density functional theory (DFT-IR). Final ranking of the candidates was based on a composite score calculated as the average of the CSI:FingerID and DFT-IR rankings. This approach resulted in the correct identification of 88 of the 148 test compounds (59%). 129 of the 148 test compounds (87%) were ranked within the top 20 candidates. These identification rates are the highest yet reported when candidate structures are used from PubChem. Combining experimental and computational MS/MS and IR spectral data is a potentially powerful option for prioritizing candidates for final structure verification.
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Affiliation(s)
- Erandika Karunaratne
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Dennis W Hill
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Kai Dührkop
- Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena 07743, Germany
| | - Sebastian Böcker
- Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena 07743, Germany
| | - David F Grant
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut 06269, United States
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63
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Wang X, Li C, Li Z, Qi Y, Zhang X, Zhao X, Zhao C, Lin X, Lu X, Xu G. A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation. Anal Chem 2023; 95:11603-11612. [PMID: 37493263 DOI: 10.1021/acs.analchem.3c00849] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.
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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
| | - Chao Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P.R. China
- School of Computer Science & Technology, Dalian University of Technology, Dalian 116024, 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
| | - Zaifang Li
- 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
| | - Yanpeng Qi
- School of Computer Science & Technology, Dalian University of Technology, Dalian 116024, 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
| | - Xiaohui Lin
- School of Computer Science & Technology, Dalian University of Technology, Dalian 116024, 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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64
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Bartmanski BJ, Rocha M, Zimmermann-Kogadeeva M. Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism. Curr Opin Chem Biol 2023; 75:102324. [PMID: 37207402 PMCID: PMC10410306 DOI: 10.1016/j.cbpa.2023.102324] [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: 12/28/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023]
Abstract
With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.
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Affiliation(s)
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Campus of Gualtar, Braga, Portugal
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65
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Ryback B, Vorholt JA. Coenzyme biosynthesis in response to precursor availability reveals incorporation of β-alanine from pantothenate in prototrophic bacteria. J Biol Chem 2023; 299:104919. [PMID: 37315792 PMCID: PMC10393543 DOI: 10.1016/j.jbc.2023.104919] [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: 03/27/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/16/2023] Open
Abstract
Coenzymes are important for all classes of enzymatic reactions and essential for cellular metabolism. Most coenzymes are synthesized from dedicated precursors, also referred to as vitamins, which prototrophic bacteria can either produce themselves from simpler substrates or take up from the environment. The extent to which prototrophs use supplied vitamins and whether externally available vitamins affect the size of intracellular coenzyme pools and control endogenous vitamin synthesis is currently largely unknown. Here, we studied coenzyme pool sizes and vitamin incorporation into coenzymes during growth on different carbon sources and vitamin supplementation regimes using metabolomics approaches. We found that the model bacterium Escherichia coli incorporated pyridoxal, niacin, and pantothenate into pyridoxal 5'-phosphate, NAD, and coenzyme A (CoA), respectively. In contrast, riboflavin was not taken up and was produced exclusively endogenously. Coenzyme pools were mostly homeostatic and not affected by externally supplied precursors. Remarkably, we found that pantothenate is not incorporated into CoA as such but is first degraded to pantoate and β-alanine and then rebuilt. This pattern was conserved in various bacterial isolates, suggesting a preference for β-alanine over pantothenate utilization in CoA synthesis. Finally, we found that the endogenous synthesis of coenzyme precursors remains active when vitamins are supplied, which is consistent with described expression data of genes for enzymes involved in coenzyme biosynthesis under these conditions. Continued production of endogenous coenzymes may ensure rapid synthesis of the mature coenzyme under changing environmental conditions, protect against coenzyme limitation, and explain vitamin availability in naturally oligotrophic environments.
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66
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Vieira JPP, Ottosson F, Jujic A, Denisov V, Magnusson M, Melander O, Duarte JMN. Metabolite Profiling in a Diet-Induced Obesity Mouse Model and Individuals with Diabetes: A Combined Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy Study. Metabolites 2023; 13:874. [PMID: 37512581 PMCID: PMC10385288 DOI: 10.3390/metabo13070874] [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: 07/03/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies are sparse. In this study we test the hypothesis that combining the metabolic information obtained using liquid chromatography (LC) MS and 1H NMR spectroscopy improves the discrimination of metabolic disease development. We induced metabolic syndrome in male mice using a high-fat diet (HFD) exposure and performed LC-MS and NMR spectroscopy on plasma samples collected after 1 and 8 weeks of dietary intervention. In an orthogonal projection to latent structures (OPLS) analysis, we observed that combining MS and NMR was stronger than each analytical method alone at determining effects of both HFD feeding and time-on-diet. We then tested our metabolomics approach on plasma from 56 individuals from the Malmö Diet and Cancer Study (MDCS) cohort. All metabolic pathways impacted by HFD feeding in mice were confirmed to be affected by diabetes in the MDCS cohort, and most prominent HFD-induced metabolite concentration changes in mice were also associated with metabolic syndrome parameters in humans. The main drivers of metabolic disease discrimination emanating from the present study included plasma levels of xanthine, hippurate, 2-hydroxyisovalerate, S-adenosylhomocysteine and dimethylguanidino valeric acid. In conclusion, our combined NMR-MS approach provided a snapshot of metabolic imbalances in humans and a mouse model, which was improved over employment of each analytical method alone.
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Affiliation(s)
- João P P Vieira
- Department of Experimental Medical Science, Faculty of Medicine, Lund University, 22184 Lund, Sweden
- Wallenberg Centre for Molecular Medicine, Lund University, 22100 Lund, Sweden
| | - Filip Ottosson
- Department of Clinical Sciences-Malmö, Faculty of Medicine, Lund University, 20502 Malmö, Sweden
| | - Amra Jujic
- Wallenberg Centre for Molecular Medicine, Lund University, 22100 Lund, Sweden
- Department of Clinical Sciences-Malmö, Faculty of Medicine, Lund University, 20502 Malmö, Sweden
- Department of Cardiology, Skåne University Hospital, 21428 Malmö, Sweden
| | - Vladimir Denisov
- Biomedical Engineering Division, Department of Clinical Sciences-Lund, Faculty of Medicine, Lund University, 22100 Lund, Sweden
| | - Martin Magnusson
- Wallenberg Centre for Molecular Medicine, Lund University, 22100 Lund, Sweden
- Department of Clinical Sciences-Malmö, Faculty of Medicine, Lund University, 20502 Malmö, Sweden
- Department of Cardiology, Skåne University Hospital, 21428 Malmö, Sweden
- Hypertension in Africa Research Team, North-West University, Potchefstroom 2520, South Africa
| | - Olle Melander
- Department of Clinical Sciences-Malmö, Faculty of Medicine, Lund University, 20502 Malmö, Sweden
| | - João M N Duarte
- Department of Experimental Medical Science, Faculty of Medicine, Lund University, 22184 Lund, Sweden
- Wallenberg Centre for Molecular Medicine, Lund University, 22100 Lund, Sweden
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67
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Oesterle I, Pristner M, Berger S, Wang M, Verri Hernandes V, Rompel A, Warth B. Exposomic Biomonitoring of Polyphenols by Non-Targeted Analysis and Suspect Screening. Anal Chem 2023; 95:10686-10694. [PMID: 37409760 PMCID: PMC10357401 DOI: 10.1021/acs.analchem.3c01393] [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: 03/31/2023] [Accepted: 06/16/2023] [Indexed: 07/07/2023]
Abstract
Polyphenols, prevalent in plants and fungi, are investigated intensively in nutritional and clinical settings because of their beneficial bioactive properties. Due to their complexity, analysis with untargeted approaches is favorable, which typically use high-resolution mass spectrometry (HRMS) rather than low-resolution mass spectrometry (LRMS). Here, the advantages of HRMS were evaluated by thoroughly testing untargeted techniques and available online resources. By applying data-dependent acquisition on real-life urine samples, 27 features were annotated with spectral libraries, 88 with in silico fragmentation, and 113 by MS1 matching with PhytoHub, an online database containing >2000 polyphenols. Moreover, other exogenous and endogenous molecules were screened to measure chemical exposure and potential metabolic effects using the Exposome-Explorer database, further annotating 144 features. Additional polyphenol-related features were explored using various non-targeted analysis techniques including MassQL for glucuronide and sulfate neutral losses, and MetaboAnalyst for statistical analysis. As HRMS typically suffers a sensitivity loss compared to state-of-the-art LRMS used in targeted workflows, the gap between the two instrumental approaches was quantified in three spiked human matrices (urine, serum, plasma) as well as real-life urine samples. Both instruments showed feasible sensitivity, with median limits of detection in the spiked samples being 10-18 ng/mL for HRMS and 4.8-5.8 ng/mL for LRMS. The results demonstrate that, despite its intrinsic limitations, HRMS can readily be used for comprehensively investigating human polyphenol exposure. In the future, this work is expected to allow for linking human health effects with exposure patterns and toxicological mixture effects with other xenobiotics.
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Affiliation(s)
- Ian Oesterle
- Department
of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria
- Fakultät
für Chemie, Institut für Biophysikalische Chemie, Universität Wien, Wien 1090, Austria
- Doctoral
School of Chemistry, University of Vienna, Vienna 1090, Austria
| | - Manuel Pristner
- Department
of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria
- Doctoral
School of Chemistry, University of Vienna, Vienna 1090, Austria
| | - Sabrina Berger
- Department
of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria
| | - Mingxun Wang
- Department
of Computer Science, University of California
Riverside, Riverside, California 92521, United States
| | - Vinicius Verri Hernandes
- Department
of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria
- Exposome
Austria, Research Infrastructure and National EIRENE Hub, Vienna 1090, Austria
| | - Annette Rompel
- Fakultät
für Chemie, Institut für Biophysikalische Chemie, Universität Wien, Wien 1090, Austria
| | - Benedikt Warth
- Department
of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria
- Exposome
Austria, Research Infrastructure and National EIRENE Hub, Vienna 1090, Austria
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68
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Fecke A, Saw NMMT, Kale D, Kasarla SS, Sickmann A, Phapale P. Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics. Metabolites 2023; 13:844. [PMID: 37512551 PMCID: PMC10383057 DOI: 10.3390/metabo13070844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabolite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound's individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a "quantitative chromatogram library" with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications.
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Affiliation(s)
- Antonia Fecke
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
- Department Hamm 2, Hochschule Hamm-Lippstadt, Marker-Allee 76-78, 59063 Hamm, Germany
| | - Nay Min Min Thaw Saw
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
| | - Dipali Kale
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
| | - Siva Swapna Kasarla
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
| | - Prasad Phapale
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
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69
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De Simone G, Soldani C, Morabito A, Franceschini B, Ferlan F, Costa G, Pastorelli R, Donadon M, Brunelli L. Implication of metabolism in the polarization of tumor-associated-macrophages: the mass spectrometry-based point of view. Front Immunol 2023; 14:1193235. [PMID: 37503340 PMCID: PMC10368868 DOI: 10.3389/fimmu.2023.1193235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 06/22/2023] [Indexed: 07/29/2023] Open
Abstract
Tumor-associated macrophages (TAMs) represent one of the main tumor-infiltrating immune cell types and are generally categorized into either of two functionally contrasting subtypes, namely classical activated M1 macrophages and alternatively activated M2 macrophages. TAMs showed different activation states that can be represent by the two extremes of the complex profile of macrophages biology, the M1-like phenotype (pro-inflammatory activity) and the M2-like phenotype (anti-inflammatory activity). Based on the tumor type, and grades, TAMs can acquire different functions and properties; usually, the M1-like phenotype is typical of early tumor stages and is associated to an anti-tumor activity, while M2-like phenotype has a pro-inflammatory activity and is related to a poor patients' prognosis. The classification of macrophages into M1/M2 groups based on well-defined stimuli does not model the infinitely more complex tissue milieu where macrophages (potentially of different origin) would be exposed to multiple signals in different sequential order. This review aims to summarize the recent mass spectrometry-based (MS-based) metabolomics findings about the modifications of metabolism in TAMs polarization in different tumors. The published data shows that MS-based metabolomics is a promising tool to help better understanding TAMs metabolic phenotypes, although it is still poorly applied for TAMs metabolism. The knowledge of key metabolic alterations in TAMs is an essential step for discovering TAMs polarization novel biomarkers and developing novel therapeutic approaches targeting TAM metabolism to repolarize TAMs towards their anti-tumor phenotype.
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Affiliation(s)
- Giulia De Simone
- Laboratory of Metabolites and Proteins in Translational Research, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
- Department of Biotechnologies and Biosciences, Università degli Studi Milano Bicocca, Milan, Italy
| | - Cristiana Soldani
- Hepatobiliary Immunopathology Laboratory, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Aurelia Morabito
- Laboratory of Metabolites and Proteins in Translational Research, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Barbara Franceschini
- Hepatobiliary Immunopathology Laboratory, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Fabrizio Ferlan
- Hepatobiliary Immunopathology Laboratory, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Guido Costa
- Hepatobiliary Immunopathology Laboratory, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Roberta Pastorelli
- Laboratory of Metabolites and Proteins in Translational Research, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Matteo Donadon
- Hepatobiliary Immunopathology Laboratory, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Health Sciences, Università del Piemonte Orientale, Novara, Italy
- Department of General Surgery, University Maggiore Hospital, Novara, Italy
| | - Laura Brunelli
- Laboratory of Metabolites and Proteins in Translational Research, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
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70
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Wei W, Riley NM, Lyu X, Shen X, Guo J, Raun SH, Zhao M, Moya-Garzon MD, Basu H, Sheng-Hwa Tung A, Li VL, Huang W, Wiggenhorn AL, Svensson KJ, Snyder MP, Bertozzi CR, Long JZ. Organism-wide, cell-type-specific secretome mapping of exercise training in mice. Cell Metab 2023; 35:1261-1279.e11. [PMID: 37141889 PMCID: PMC10524249 DOI: 10.1016/j.cmet.2023.04.011] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 02/21/2023] [Accepted: 04/05/2023] [Indexed: 05/06/2023]
Abstract
There is a significant interest in identifying blood-borne factors that mediate tissue crosstalk and function as molecular effectors of physical activity. Although past studies have focused on an individual molecule or cell type, the organism-wide secretome response to physical activity has not been evaluated. Here, we use a cell-type-specific proteomic approach to generate a 21-cell-type, 10-tissue map of exercise training-regulated secretomes in mice. Our dataset identifies >200 exercise training-regulated cell-type-secreted protein pairs, the majority of which have not been previously reported. Pdgfra-cre-labeled secretomes were the most responsive to exercise training. Finally, we show anti-obesity, anti-diabetic, and exercise performance-enhancing activities for proteoforms of intracellular carboxylesterases whose secretion from the liver is induced by exercise training.
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Affiliation(s)
- Wei Wei
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biology, Stanford University, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Nicholas M Riley
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Department of Chemistry, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Xuchao Lyu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Wu Tsai Human Performance Alliance, Stanford University, Stanford, CA 94305, USA
| | - Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94035, USA
| | - Jing Guo
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Steffen H Raun
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Meng Zhao
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Maria Dolores Moya-Garzon
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Himanish Basu
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | - Alan Sheng-Hwa Tung
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Veronica L Li
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Wentao Huang
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Amanda L Wiggenhorn
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Katrin J Svensson
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94035, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Carolyn R Bertozzi
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Department of Chemistry, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Jonathan Z Long
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA; Wu Tsai Human Performance Alliance, Stanford University, Stanford, CA 94305, USA.
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71
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Pannkuk EL, Laiakis EC, Garty G, Ponnaiya B, Wu X, Shuryak I, Ghandhi SA, Amundson SA, Brenner DJ, Fornace AJ. Variable Dose Rates in Realistic Radiation Exposures: Effects on Small Molecule Markers of Ionizing Radiation in the Murine Model. Radiat Res 2023; 200:1-12. [PMID: 37212727 PMCID: PMC10410530 DOI: 10.1667/rade-22-00211.1] [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: 12/01/2022] [Accepted: 04/27/2023] [Indexed: 05/23/2023]
Abstract
Novel biodosimetry assays for use in preparedness and response to potential malicious attacks or nuclear accidents would ideally provide accurate dose reconstruction independent of the idiosyncrasies of a complex exposure to ionizing radiation. Complex exposures will consist of dose rates spanning the low dose rates (LDR) to very high-dose rates (VHDR) that need to be tested for assay validation. Here, we investigate how a range of relevant dose rates affect metabolomic dose reconstruction at potentially lethal radiation exposures (8 Gy in mice) from an initial blast or subsequent fallout exposures compared to zero or sublethal exposures (0 or 3 Gy in mice) in the first 2 days, which corresponds to an integral time individuals will reach medical facilities after a radiological emergency. Biofluids (urine and serum) were collected from both male and female 9-10-week-old C57BL/6 mice at 1 and 2 days postirradiation (total doses of 0, 3 or 8 Gy) after a VHDR of 7 Gy/s. Additionally, samples were collected after a 2-day exposure consisting of a declining dose rate (1 to 0.004 Gy/min) recapitulating the 7:10 rule-of-thumb time dependency of nuclear fallout. Overall similar perturbations were observed in both urine and serum metabolite concentrations irrespective of sex or dose rate, with the exception of xanthurenic acid in urine (female specific) and taurine in serum (VHDR specific). In urine, we developed identical multiplex metabolite panels (N6, N6,N6-trimethyllysine, carnitine, propionylcarnitine, hexosamine-valine-isoleucine, and taurine) that could identify individuals receiving potentially lethal levels of radiation from the zero or sublethal cohorts with excellent sensitivity and specificity, with creatine increasing model performance at day 1. In serum, individuals receiving a 3 or 8 Gy exposure could be identified from their pre-irradiation samples with excellent sensitivity and specificity, however, due to a lower dose response the 3 vs. 8 Gy groups could not be distinguished from each other. Together with previous results, these data indicate that dose-rate-independent small molecule fingerprints have potential in novel biodosimetry assays.
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Affiliation(s)
- Evan L. Pannkuk
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC
- Center for Metabolomic Studies, Georgetown University, Washington, DC
| | - Evagelia C. Laiakis
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC
- Center for Metabolomic Studies, Georgetown University, Washington, DC
| | - Guy Garty
- Radiological Research Accelerator Facility, Columbia University, Irvington, New York
- Center for Radiological Research, Columbia University Irving Medical Center, New York, New York
| | - Brian Ponnaiya
- Center for Radiological Research, Columbia University Irving Medical Center, New York, New York
| | - Xuefeng Wu
- Center for Radiological Research, Columbia University Irving Medical Center, New York, New York
| | - Igor Shuryak
- Center for Radiological Research, Columbia University Irving Medical Center, New York, New York
| | - Shanaz A. Ghandhi
- Center for Radiological Research, Columbia University Irving Medical Center, New York, New York
| | - Sally A. Amundson
- Center for Radiological Research, Columbia University Irving Medical Center, New York, New York
| | - David J. Brenner
- Center for Radiological Research, Columbia University Irving Medical Center, New York, New York
| | - Albert J. Fornace
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC
- Center for Metabolomic Studies, Georgetown University, Washington, DC
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72
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Baygi SF, Kumar Y, Barupal DK. IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets. Anal Chem 2023; 95:9480-9487. [PMID: 37311059 PMCID: PMC11080491 DOI: 10.1021/acs.analchem.3c00376] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Poor chemical annotation of high-resolution mass spectrometry data limits applications of untargeted metabolomics datasets. Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics─Composite Spectra Analysis (IDSL.CSA) R package, generates composite mass spectra libraries from MS1-only data, enabling the chemical annotation of high-resolution mass spectrometry coupled with liquid chromatography peaks regardless of the availability of MS2 fragmentation spectra. We demonstrate comparable annotation rates for commonly detected endogenous metabolites in human blood samples using IDSL.CSA libraries versus MS/MS libraries in validation tests. IDSL.CSA can create and search composite spectra libraries from any untargeted metabolomics dataset generated using high-resolution mass spectrometry coupled to liquid or gas chromatography instruments. The cross-applicability of these libraries across independent studies may provide access to new biological insights that may be missed due to the lack of MS2 fragmentation data. The IDSL.CSA package is available in the R-CRAN repository at https://cran.r-project.org/package=IDSL.CSA. Detailed documentation and tutorials are provided at https://github.com/idslme/IDSL.CSA.
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Affiliation(s)
- Sadjad Fakouri Baygi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Yashwant Kumar
- Non-communicable Diseases Division, Translational Health Science and Technology Institute, Faridabad, Haryana, 121001, India
| | - Dinesh Kumar Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
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73
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Thomson TM. On the importance for drug discovery of a transnational Latin American database of natural compound structures. Front Pharmacol 2023; 14:1207559. [PMID: 37426821 PMCID: PMC10324963 DOI: 10.3389/fphar.2023.1207559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/15/2023] [Indexed: 07/11/2023] Open
Affiliation(s)
- Timothy M. Thomson
- Institute for Molecular Biology (IBMB-CSIC), Barcelona, Spain
- CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
- Universidad Peruana Cayetano Heredia, Lima, Peru
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74
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Anwardeen NR, Diboun I, Mokrab Y, Althani AA, Elrayess MA. Statistical methods and resources for biomarker discovery using metabolomics. BMC Bioinformatics 2023; 24:250. [PMID: 37322419 DOI: 10.1186/s12859-023-05383-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 06/09/2023] [Indexed: 06/17/2023] Open
Abstract
Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.
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Affiliation(s)
- Najeha R Anwardeen
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Ilhame Diboun
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
| | - Younes Mokrab
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
| | - Asma A Althani
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar
- QU Health, Qatar University, Doha, Qatar
| | - Mohamed A Elrayess
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar.
- QU Health, Qatar University, Doha, Qatar.
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75
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Huang Z, Li X, Wei B, Yu Y. Global metabolomics study on the pathogenesis of pediatric medulloblastoma via UPLC- Q/E-MS/MS. PLoS One 2023; 18:e0287121. [PMID: 37319142 PMCID: PMC10270352 DOI: 10.1371/journal.pone.0287121] [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: 08/28/2022] [Accepted: 05/30/2023] [Indexed: 06/17/2023] Open
Abstract
Medulloblastoma is one of the most frequent malignant brain tumors in infancy and childhood. Early diagnosis and treatment are quite crucial for the prognosis. However, the pathogenesis of medulloblastoma is still not completely clarified. High-resolution mass spectrometry has enabled a comprehensive investigation on the mechanism of disease from the perspective of metabolism. Herein, we compared the difference of metabolic profiles of serum between medulloblastoma (n = 33) and healthy control (HC, n = 16) by using UPLC-Q/E-MS/MS. Principal component analysis and orthogonal projections to latent structures discriminant analysis (OPLS-DA) intuitively revealed the significantly distinct metabolic profiles between medulloblastoma and HC (p < 0.01 for permutation test on OPLS-DA model). Total of 25 significantly changed metabolites were identified. ROC analysis reported that six of them (Phosphatidic acid (8:0/15:0), 3'-Sialyllactose, Isocoproporphyrin, Acetylspermidine, Fructoseglycine and 3-Hydroxydodecanedioate) showed high specificity and precision to be potential diagnosis biomarkers (AUC > 0.98). Functional analysis discovered that there are four pathways notably perturbed for medulloblastoma. These pathways are related with the dysfunction of arachidonic acid metabolism, steroid hormone biosynthesis, and folate-related metabolism. The target intervention on these pathways may reduce the mortality of medulloblastoma.
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Affiliation(s)
- Zhehao Huang
- Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China
| | - Xianglan Li
- Department of Dermatology, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China
| | - Bo Wei
- Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China
| | - Yin Yu
- Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China
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76
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Sumara A, Stachniuk A, Trzpil A, Bartoszek A, Montowska M, Fornal E. LC-MS Metabolomic Profiling of Five Types of Unrefined, Cold-Pressed Seed Oils to Identify Markers to Determine Oil Authenticity and to Test for Oil Adulteration. Molecules 2023; 28:4754. [PMID: 37375308 DOI: 10.3390/molecules28124754] [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: 05/11/2023] [Revised: 06/08/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
The authenticity of food products marketed as health-promoting foods-especially unrefined, cold-pressed seed oils-should be controlled to ensure their quality and safeguard consumers and patients. Metabolomic profiling using liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (LC-QTOF) was employed to identify authenticity markers for five types of unrefined, cold-pressed seed oils: black seed oil (Nigella sativa L.), pumpkin seed oil (Cucurbita pepo L.), evening primrose oil (Oenothera biennis L.), hemp oil (Cannabis sativa L.) and milk thistle oil (Silybum marianum). Of the 36 oil-specific markers detected, 10 were established for black seed oil, 8 for evening primrose seed oil, 7 for hemp seed oil, 4 for milk thistle seed oil and 7 for pumpkin seed oil. In addition, the influence of matrix variability on the oil-specific metabolic markers was examined by studying binary oil mixtures containing varying volume percentages of each tested oil and each of three potential adulterants: sunflower, rapeseed and sesame oil. The presence of oil-specific markers was confirmed in 7 commercial oil mix products. The identified 36 oil-specific metabolic markers proved useful for confirming the authenticity of the five target seed oils. The ability to detect adulterations of these oils with sunflower, rapeseed and sesame oil was demonstrated.
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Affiliation(s)
- Agata Sumara
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
| | - Anna Stachniuk
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
| | - Alicja Trzpil
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
| | - Adrian Bartoszek
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
| | - Magdalena Montowska
- Department of Meat Technology, Poznan University of Life Sciences, ul. Wojska Polskiego 31, 60-624 Poznan, Poland
| | - Emilia Fornal
- Department of Bioanalytics, Medical University of Lublin, ul. Jaczewskiego 8b, 20-090 Lublin, Poland
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77
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Liang W, Yang Y, Gong S, Wei M, Ma Y, Feng R, Gao J, Liu X, Tu F, Ma W, Yi X, Liang Z, Wang F, Wang L, Chen D, Shu W, Miller BE, Tal-Singer R, Donaldson GC, Wedzicha JA, Singh D, Wilkinson TMA, Brightling CE, Chen R, Zhong N, Wang Z. Airway dysbiosis accelerates lung function decline in chronic obstructive pulmonary disease. Cell Host Microbe 2023; 31:1054-1070.e9. [PMID: 37207649 DOI: 10.1016/j.chom.2023.04.018] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 03/23/2023] [Accepted: 04/17/2023] [Indexed: 05/21/2023]
Abstract
Progressive lung function decline is a hallmark of chronic obstructive pulmonary disease (COPD). Airway dysbiosis occurs in COPD, but whether it contributes to disease progression remains unknown. Here, we show, through a longitudinal analysis of two cohorts involving four UK centers, that baseline airway dysbiosis in COPD patients, characterized by the enrichment of opportunistic pathogenic taxa, associates with a rapid forced expiratory volume in 1 s (FEV1) decline over 2 years. Dysbiosis associates with exacerbation-related FEV1 fall and sudden FEV1 fall at stability, contributing to long-term FEV1 decline. A third cohort in China further validates the microbiota-FEV1-decline association. Human multi-omics and murine studies show that airway Staphylococcus aureus colonization promotes lung function decline through homocysteine, which elicits a neutrophil apoptosis-to-NETosis shift via the AKT1-S100A8/A9 axis. S. aureus depletion via bacteriophages restores lung function in emphysema mice, providing a fresh approach to slow COPD progression by targeting the airway microbiome.
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Affiliation(s)
- Weijie Liang
- Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, Guangdong Province, China
| | - Yuqiong Yang
- First Affiliated Hospital of Guangzhou Medical University, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, Guangzhou, Guangdong Province, China
| | - Shenhai Gong
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Mingyuan Wei
- Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, Guangdong Province, China
| | - Yingfei Ma
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, China
| | - Ruipei Feng
- Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, Guangdong Province, China
| | - Jingyuan Gao
- Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, Guangdong Province, China
| | - Xiaomin Liu
- Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, Guangdong Province, China
| | - Fuyi Tu
- Institute of Statistics and Big Data, Renmin University of China, Beijing, China
| | - Wei Ma
- Institute of Statistics and Big Data, Renmin University of China, Beijing, China
| | - Xinzhu Yi
- Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, Guangdong Province, China
| | - Zhenyu Liang
- First Affiliated Hospital of Guangzhou Medical University, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, Guangzhou, Guangdong Province, China
| | - Fengyan Wang
- First Affiliated Hospital of Guangzhou Medical University, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, Guangzhou, Guangdong Province, China
| | - Lingwei Wang
- Pulmonary and Critical Care Department, Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong Province, China
| | - Dandan Chen
- Pulmonary and Critical Care Department, Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong Province, China
| | - Wensheng Shu
- Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, Guangdong Province, China
| | | | | | - Gavin C Donaldson
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Dave Singh
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester University NHS Foundation Trust, Manchester, UK
| | - Tom M A Wilkinson
- NIHR Southampton Respiratory Biomedical Research Unit, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Christopher E Brightling
- Institute for Lung Health, Leicester NIHR Biomedical Research Centre, Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Rongchang Chen
- First Affiliated Hospital of Guangzhou Medical University, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, Guangzhou, Guangdong Province, China; Pulmonary and Critical Care Department, Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong Province, China
| | - Nanshan Zhong
- First Affiliated Hospital of Guangzhou Medical University, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, Guangzhou, Guangdong Province, China
| | - Zhang Wang
- Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, Guangdong Province, China.
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78
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Houthuijs KJ, Berden G, Engelke UFH, Gautam V, Wishart DS, Wevers RA, Martens J, Oomens J. An In Silico Infrared Spectral Library of Molecular Ions for Metabolite Identification. Anal Chem 2023. [PMID: 37262385 DOI: 10.1021/acs.analchem.3c01078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Infrared ion spectroscopy (IRIS) continues to see increasing use as an analytical tool for small-molecule identification in conjunction with mass spectrometry (MS). The IR spectrum of an m/z selected population of ions constitutes a unique fingerprint that is specific to the molecular structure. However, direct translation of an IR spectrum to a molecular structure remains challenging, as reference libraries of IR spectra of molecular ions largely do not exist. Quantum-chemically computed spectra can reliably be used as reference, but the challenge of selecting the candidate structures remains. Here, we introduce an in silico library of vibrational spectra of common MS adducts of over 4500 compounds found in the human metabolome database. In total, the library currently contains more than 75,000 spectra computed at the DFT level that can be queried with an experimental IR spectrum. Moreover, we introduce a database of 189 experimental IRIS spectra, which is employed to validate the automated spectral matching routines. This demonstrates that 75% of the metabolites in the experimental data set are correctly identified, based solely on their exact m/z and IRIS spectrum. Additionally, we demonstrate an approach for specifically identifying substructures by performing a search without m/z constraints to find structural analogues. Such an unsupervised search paves the way toward the de novo identification of unknowns that are absent in spectral libraries. We apply the in silico spectral library to identify an unknown in a plasma sample as 3-hydroxyhexanoic acid, highlighting the potential of the method.
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Affiliation(s)
- Kas J Houthuijs
- Institute for Molecules and Materials, FELIX Laboratory, Radboud University, Nijmegen 6525 ED, The Netherlands
| | - Giel Berden
- Institute for Molecules and Materials, FELIX Laboratory, Radboud University, Nijmegen 6525 ED, The Netherlands
| | - Udo F H Engelke
- Department of Genetics, Translational Metabolic Laboratory, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton AB T6G 2E9, Canada
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton AB T6G 2E9, Canada
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2B7, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2H7, Canada
| | - Ron A Wevers
- Department of Genetics, Translational Metabolic Laboratory, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Jonathan Martens
- Institute for Molecules and Materials, FELIX Laboratory, Radboud University, Nijmegen 6525 ED, The Netherlands
| | - Jos Oomens
- Institute for Molecules and Materials, FELIX Laboratory, Radboud University, Nijmegen 6525 ED, The Netherlands
- van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam 1098 XH, The Netherlands
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79
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Baygi SF, Kumar Y, Barupal DK. IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.09.527886. [PMID: 36798308 PMCID: PMC9934657 DOI: 10.1101/2023.02.09.527886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Poor chemical annotation of high-resolution mass spectrometry data limit applications of untargeted metabolomics datasets. Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics - Composite Spectra Analysis (IDSL.CSA) R package, generates composite mass spectra libraries from MS1-only data, enabling the chemical annotation of LC/HRMS peaks regardless of the availability of MS2 fragmentation spectra. We demonstrate comparable annotation rates for commonly detected endogenous metabolites in human blood samples using IDSL.CSA libraries versus MS/MS libraries in validation tests. IDSL.CSA can create and search composite spectra libraries from any untargeted metabolomics dataset generated using high-resolution mass spectrometry coupled to liquid or gas chromatography instruments. The cross-applicability of these libraries across independent studies may provide access to new biological insights that may be missed due to the lack of MS2 fragmentation data. The IDSL.CSA package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.CSA . Detailed documentation and tutorials are provided at https://github.com/idslme/IDSL.CSA . For Table of Contents Only
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Affiliation(s)
- Sadjad Fakouri Baygi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Yashwant Kumar
- Non-communicable Diseases Division, Translational Health Science and Technology Institute, Faridabad, Haryana, 121001, India
| | - Dinesh Kumar Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
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Javed A, Alam MB, Naznin M, Shafique I, Kim S, Lee SH. Tyrosinase inhibitory activity of Sargassum fusiforme and characterisation of bioactive compounds. PHYTOCHEMICAL ANALYSIS : PCA 2023. [PMID: 37183174 DOI: 10.1002/pca.3233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/16/2023]
Abstract
INTRODUCTION Sargassum fusiforme (Harvey) Setchell, also known as Tot (in Korean) and Hijiki (in Japanese), is widely consumed in Korea, Japan, and China due to its health promoting properties. However, the bioactive component behind the biological activity is still unknown. OBJECTIVES We aimed to optimise the extraction conditions for achieving maximum tyrosinase inhibition activity by using two sophisticated statistical tools, that is, response surface methodology (RSM) and artificial neural network (ANN). Moreover, high-resolution mass spectrometry (HRMS) was used to tentatively identify the components, which are then further studied for molecular docking study using 2Y9X protein. METHODOLOGY RSM central composite design was used to conduct extraction using microwave equipment, which was then compared to ANN. Electrospray ionisation tandem mass spectrometry (ESI-MS/MS) was used to tentatively identify bioactive components, which were then docked to the 2Y9X protein using AutoDock Vina and MolDock software. RESULTS Maximum tyrosinase inhibition activity of 79.530% was achieved under optimised conditions of time: 3.27 min, temperature: 128.885°C, ethanol concentration: 42.13%, and microwave intensity: 577.84 W. Furthermore, 48 bioactive compounds were tentatively identified in optimised Sargassum fusiforme (OSF) extract, and among them, seven phenolics, five flavonoids, five lignans, six terpenes, and five sulfolipids and phospholipids were putatively reported for the first time in Sargassum fusiforme. Among 48 bioactive components, trifuhalol-A, diphlorethohydroxycarmalol, glycyrrhizin, and arctigenin exhibited higher binding energies for 2Y9X. CONCLUSION Taken together, these findings suggest that OSF extract can be used as an effective skin-whitening source on a commercial level and could be used in topical formulations by replacing conventional drugs.
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Affiliation(s)
- Ahsan Javed
- Department of Food Science and Biotechnology, Graduate School, Kyungpook National University, Daegu, Korea
| | - Md Badrul Alam
- Department of Food Science and Biotechnology, Graduate School, Kyungpook National University, Daegu, Korea
- Food and Bio-Industry Research Institute, Inner Beauty/Antiaging Centre, Kyungpook National University, Daegu, Korea
| | - Marufa Naznin
- Department of Chemistry, Kyungpook National University, Daegu, Korea
| | - Imran Shafique
- Department of Chemistry, Kyungpook National University, Daegu, Korea
| | - Sunghwan Kim
- Department of Chemistry, Kyungpook National University, Daegu, Korea
- Mass Spectroscopy Converging Research Centre, Green Nano Materials Research Centre, Kyungpook National University, Daegu, Korea
| | - Sang-Han Lee
- Department of Food Science and Biotechnology, Graduate School, Kyungpook National University, Daegu, Korea
- Food and Bio-Industry Research Institute, Inner Beauty/Antiaging Centre, Kyungpook National University, Daegu, Korea
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81
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Shi C, Zi Y, Huang S, Chen J, Wang X, Zhong J. Development and application of lipidomics for food research. ADVANCES IN FOOD AND NUTRITION RESEARCH 2023; 104:1-42. [PMID: 37236729 DOI: 10.1016/bs.afnr.2022.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Lipidomics is an emerging and promising omics derived from metabolomics to comprehensively analyze all of lipid molecules in biological matrices. The purpose of this chapter is to introduce the development and application of lipidomics for food research. First, three aspects of sample preparation are introduced: food sampling, lipid extraction, and transportation and storage. Second, five types of instruments for data acquisition are summarized: direct infusion-mass spectrometry (MS), chromatographic separation-MS, ion mobility-MS, MS imaging, and nuclear magnetic resonance spectroscopy. Third, data acquisition and analysis software are described for the lipidomics software development. Fourth, the application of lipidomics for food research is discussed such as food origin and adulteration analysis, food processing research, food preservation research, and food nutrition and health research. All the contents suggest that lipidomics is a powerful tool for food research based on its ability of lipid component profile analysis.
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Affiliation(s)
- Cuiping Shi
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ye Zi
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Shudan Huang
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Jiahui Chen
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Xichang Wang
- National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Jian Zhong
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China.
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82
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Zhang T, Noll SE, Peng JT, Klair A, Tripka A, Stutzman N, Cheng C, Zare RN, Dickinson AJ. Chemical imaging reveals diverse functions of tricarboxylic acid metabolites in root growth and development. Nat Commun 2023; 14:2567. [PMID: 37142569 PMCID: PMC10160030 DOI: 10.1038/s41467-023-38150-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 04/18/2023] [Indexed: 05/06/2023] Open
Abstract
Understanding how plants grow is critical for agriculture and fundamental for illuminating principles of multicellular development. Here, we apply desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to the chemical mapping of the developing maize root. This technique reveals a range of small molecule distribution patterns across the gradient of stem cell differentiation in the root. To understand the developmental logic of these patterns, we examine tricarboxylic acid (TCA) cycle metabolites. In both Arabidopsis and maize, we find evidence that elements of the TCA cycle are enriched in developmentally opposing regions. We find that these metabolites, particularly succinate, aconitate, citrate, and α-ketoglutarate, control root development in diverse and distinct ways. Critically, the developmental effects of certain TCA metabolites on stem cell behavior do not correlate with changes in ATP production. These results present insights into development and suggest practical means for controlling plant growth.
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Affiliation(s)
- Tao Zhang
- Cell and Developmental Biology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Sarah E Noll
- Department of Chemistry, Stanford University, Stanford, CA, 94305, USA
- Department of Chemistry, Pomona College, Claremont, CA, 91711, USA
| | - Jesus T Peng
- Cell and Developmental Biology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Amman Klair
- Cell and Developmental Biology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Abigail Tripka
- Cell and Developmental Biology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Nathan Stutzman
- Cell and Developmental Biology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Casey Cheng
- Cell and Developmental Biology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Richard N Zare
- Department of Chemistry, Stanford University, Stanford, CA, 94305, USA.
| | - Alexandra J Dickinson
- Cell and Developmental Biology, University of California San Diego, La Jolla, CA, 92093, USA.
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83
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Lana M, Simón O, Velasco P, Rodríguez VM, Caballero P, Poveda J. First study on the root endophytic fungus Trichoderma hamatum as an entomopathogen: Development of a fungal bioinsecticide against cotton leafworm (Spodoptera littoralis). Microbiol Res 2023; 270:127334. [PMID: 36804128 DOI: 10.1016/j.micres.2023.127334] [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: 12/07/2022] [Revised: 02/01/2023] [Accepted: 02/12/2023] [Indexed: 02/16/2023]
Abstract
Cotton leaf worm (Spodoptera littoralis) is a pest that produces important losses in horticultural and ornamental crops in greenhouse, being classified as quarantine pest A2 by EPPO. One of the strategies proposed to control agricultural pests in a health and environmentally friendly way is biological control with entomopathogenic fungi. The genus of filamentous fungi Trichoderma includes different species with direct (infection, antibiosis, anti-feeding, etc.) and indirect (systemic activation of plant defenses) insecticidal capacity, however, the species T. hamatum has never been described previously as entomopathogenic. In this work, the entomopathogenic capacity of T. hamatum on S. littoralis L3 larvae was analyzed by applying spores and fungal filtrates (topically and orally). Infection by spores was compared with the commercial entomopathogenic fungus Beauveria bassiana, obtaining similar results with respect to the production of larval mortality. Oral application of spores reported high mortality and fungal colonization of larvae, however, T. hamatum did not show chitinase activity when grown in the presence of S. littoralis tissues. Therefore, infection of S. littoralis larvae by T. hamatum is through natural openings such as mouth, anus or spiracles. With respect to the application of filtrates, only those obtained from the liquid culture of T. hamatum in contact with S. littoralis tissues reported a significant reduction in larval growth. Metabolomic analysis of the filtrates determined that the filtrate with insecticidal capacity presented the siderophore rhizoferrin in large quantities, which could be responsible for this activity. However, the production of this siderophore had never been previously described in Trichoderma and its insecticidal capacity was unknown. In conclusion, T. hamatum presents entomopathogenic capacity against S. littoralis larvae through the application of spores and filtrates, and both ways could be the basis for the development of efficient bioinsecticides against the pest.
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Affiliation(s)
- Maite Lana
- Institute for Multidisciplinary Research in Applied Biology (IMAB), Universidad Pública de Navarra, Campus Arrosadía, 31006 Pamplona, Spain
| | - Oihane Simón
- Institute for Multidisciplinary Research in Applied Biology (IMAB), Universidad Pública de Navarra, Campus Arrosadía, 31006 Pamplona, Spain
| | - Pablo Velasco
- Group of Genetics, Breeding and Biochemistry of Brassicas, Mision Biologica de Galicia (MBG-CSIC), 36143 Pontevedra, Spain
| | - Víctor M Rodríguez
- Group of Genetics, Breeding and Biochemistry of Brassicas, Mision Biologica de Galicia (MBG-CSIC), 36143 Pontevedra, Spain
| | - Primitivo Caballero
- Institute for Multidisciplinary Research in Applied Biology (IMAB), Universidad Pública de Navarra, Campus Arrosadía, 31006 Pamplona, Spain
| | - Jorge Poveda
- Institute for Multidisciplinary Research in Applied Biology (IMAB), Universidad Pública de Navarra, Campus Arrosadía, 31006 Pamplona, Spain; Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, Bragança 5300-253, Portugal; Recognised Research Group AGROBIOTECH, Department of Plant Production and Forest Resources, Higher Technical School of Agricultural Engineering of Palencia, University Institute for Research in Sustainable Forest Management (iuFOR), University of Valladolid, Avda. Madrid 57, 34004 Palencia, Spain.
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84
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Mahood EH, Bennett AA, Komatsu K, Kruse LH, Lau V, Rahmati Ishka M, Jiang Y, Bravo A, Louie K, Bowen BP, Harrison MJ, Provart NJ, Vatamaniuk OK, Moghe GD. Information theory and machine learning illuminate large-scale metabolomic responses of Brachypodium distachyon to environmental change. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 114:463-481. [PMID: 36880270 DOI: 10.1111/tpj.16160] [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: 05/20/2022] [Revised: 02/06/2023] [Accepted: 02/19/2023] [Indexed: 05/10/2023]
Abstract
Plant responses to environmental change are mediated via changes in cellular metabolomes. However, <5% of signals obtained from liquid chromatography tandem mass spectrometry (LC-MS/MS) can be identified, limiting our understanding of how metabolomes change under biotic/abiotic stress. To address this challenge, we performed untargeted LC-MS/MS of leaves, roots, and other organs of Brachypodium distachyon (Poaceae) under 17 organ-condition combinations, including copper deficiency, heat stress, low phosphate, and arbuscular mycorrhizal symbiosis. We found that both leaf and root metabolomes were significantly affected by the growth medium. Leaf metabolomes were more diverse than root metabolomes, but the latter were more specialized and more responsive to environmental change. We found that 1 week of copper deficiency shielded the root, but not the leaf metabolome, from perturbation due to heat stress. Machine learning (ML)-based analysis annotated approximately 81% of the fragmented peaks versus approximately 6% using spectral matches alone. We performed one of the most extensive validations of ML-based peak annotations in plants using thousands of authentic standards, and analyzed approximately 37% of the annotated peaks based on these assessments. Analyzing responsiveness of each predicted metabolite class to environmental change revealed significant perturbations of glycerophospholipids, sphingolipids, and flavonoids. Co-accumulation analysis further identified condition-specific biomarkers. To make these results accessible, we developed a visualization platform on the Bio-Analytic Resource for Plant Biology website (https://bar.utoronto.ca/efp_brachypodium_metabolites/cgi-bin/efpWeb.cgi), where perturbed metabolite classes can be readily visualized. Overall, our study illustrates how emerging chemoinformatic methods can be applied to reveal novel insights into the dynamic plant metabolome and stress adaptation.
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Affiliation(s)
- Elizabeth H Mahood
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Alexandra A Bennett
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Karyn Komatsu
- Department of Cell and Systems Biology, University of Toronto, Toronto, Canada
| | - Lars H Kruse
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Vincent Lau
- Department of Cell and Systems Biology, University of Toronto, Toronto, Canada
| | - Maryam Rahmati Ishka
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
- Boyce Thompson Institute, Ithaca, NY, USA
| | - Yulin Jiang
- Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | | | - Katherine Louie
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Lawrence Berkeley National Laboratory, Department of Energy Joint Genome Institute, Berkeley, CA, USA
| | - Benjamin P Bowen
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Lawrence Berkeley National Laboratory, Department of Energy Joint Genome Institute, Berkeley, CA, USA
| | | | - Nicholas J Provart
- Department of Cell and Systems Biology, University of Toronto, Toronto, Canada
| | - Olena K Vatamaniuk
- Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Gaurav D Moghe
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
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85
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Jia S, Marques Dos Santos M, Li C, Fang M, Sureshkumar M, Snyder SA. Analogy or fallacy, unsafe chemical alternatives: Mechanistic insights into energy metabolism dysfunction induced by Bisphenol analogs in HepG2 cells. ENVIRONMENT INTERNATIONAL 2023; 175:107942. [PMID: 37094511 DOI: 10.1016/j.envint.2023.107942] [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: 03/12/2023] [Revised: 04/09/2023] [Accepted: 04/17/2023] [Indexed: 05/03/2023]
Abstract
Bisphenol analogs (BPs) are widely used as industrial alternatives for Bisphenol A (BPA). Their toxicity assessment in humans has mainly focused on estrogenic activity, while other toxicity effects and mechanisms resulting from BPs exposure remain unclear. In this study, we investigated the effects of three BPs (Bisphenol AF (BPAF), Bisphenol G (BPG) and Bisphenol PH (BPPH)) on metabolic pathways of HepG2 cells. Results from comprehensive cellular bioenergetics analysis and nontarget metabolomics indicated that the most important process affected by BPs exposure was energy metabolism, as evidenced by reduced mitochondrial function and enhanced glycolysis. Compared to the control group, BPG and BPPH exhibited a consistent pattern of metabolic dysregulation, while BPAF differed from both, such as an increased ATP: ADP ratio (1.29-fold, p < 0.05) observed in BPAF and significantly decreased ATP: ADP ratio for BPG (0.28-fold, p < 0.001) and BPPH (0.45-fold, p < 0.001). Bioassay endpoint analysis revealed BPG/BPPH induced alterations in mitochondrial membrane potential and overproductions of reactive oxygen species. Taken together these data suggested that BPG/BPPH induced oxidative stress and mitochondrial damage in cells results in energy metabolism dysregulation. By contrast, BPAF had no effect on mitochondrial health, but induced a proliferation promoting effect on cells, which might contribute to the energy metabolism dysfunction. Interestingly, BPPH induced the greatest mitochondrial damage among the three BPs but did not exhibit Estrogen receptor alpha (ERα) activating effects. This study characterized the distinct metabolic mechanisms underlying energy metabolism dysregulation induced by different BPs in target human cells, providing new insight into the evaluation of the emerging BPA substitutes.
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Affiliation(s)
- Shenglan Jia
- Nanyang Environment and Water Research Institute (NEWRI), Nanyang Technological University, 1 Cleantech Loop, CleanTech One, Singapore 637141, Singapore
| | - Mauricius Marques Dos Santos
- Nanyang Environment and Water Research Institute (NEWRI), Nanyang Technological University, 1 Cleantech Loop, CleanTech One, Singapore 637141, Singapore
| | - Caixia Li
- Nanyang Environment and Water Research Institute (NEWRI), Nanyang Technological University, 1 Cleantech Loop, CleanTech One, Singapore 637141, Singapore
| | - Mingliang Fang
- Nanyang Environment and Water Research Institute (NEWRI), Nanyang Technological University, 1 Cleantech Loop, CleanTech One, Singapore 637141, Singapore; Department of Environmental Science and Engineering, Fudan University, 220 Handan Rd., Shanghai 200433, PR China
| | - Mithusha Sureshkumar
- Nanyang Environment and Water Research Institute (NEWRI), Nanyang Technological University, 1 Cleantech Loop, CleanTech One, Singapore 637141, Singapore
| | - Shane A Snyder
- Nanyang Environment and Water Research Institute (NEWRI), Nanyang Technological University, 1 Cleantech Loop, CleanTech One, Singapore 637141, Singapore.
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Liang D, Li Z, Vlaanderen J, Tang Z, Jones DP, Vermeulen R, Sarnat JA. A State-of-the-Science Review on High-Resolution Metabolomics Application in Air Pollution Health Research: Current Progress, Analytical Challenges, and Recommendations for Future Direction. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:56002. [PMID: 37192319 PMCID: PMC10187974 DOI: 10.1289/ehp11851] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 03/22/2023] [Accepted: 03/30/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND Understanding the mechanistic basis of air pollution toxicity is dependent on accurately characterizing both exposure and biological responses. Untargeted metabolomics, an analysis of small-molecule metabolic phenotypes, may offer improved estimation of exposures and corresponding health responses to complex environmental mixtures such as air pollution. The field remains nascent, however, with questions concerning the coherence and generalizability of findings across studies, study designs and analytical platforms. OBJECTIVES We aimed to review the state of air pollution research from studies using untargeted high-resolution metabolomics (HRM), highlight the areas of concordance and dissimilarity in methodological approaches and reported findings, and discuss a path forward for future use of this analytical platform in air pollution research. METHODS We conducted a state-of-the-science review to a) summarize recent research of air pollution studies using untargeted metabolomics and b) identify gaps in the peer-reviewed literature and opportunities for addressing these gaps in future designs. We screened articles published within Pubmed and Web of Science between 1 January 2005 and 31 March 2022. Two reviewers independently screened 2,065 abstracts, with discrepancies resolved by a third reviewer. RESULTS We identified 47 articles that applied untargeted metabolomics on serum, plasma, whole blood, urine, saliva, or other biospecimens to investigate the impact of air pollution exposures on the human metabolome. Eight hundred sixteen unique features confirmed with level-1 or -2 evidence were reported to be associated with at least one or more air pollutants. Hypoxanthine, histidine, serine, aspartate, and glutamate were among the 35 metabolites consistently exhibiting associations with multiple air pollutants in at least 5 independent studies. Oxidative stress and inflammation-related pathways-including glycerophospholipid metabolism, pyrimidine metabolism, methionine and cysteine metabolism, tyrosine metabolism, and tryptophan metabolism-were the most commonly perturbed pathways reported in > 70 % of studies. More than 80% of the reported features were not chemically annotated, limiting the interpretability and generalizability of the findings. CONCLUSIONS Numerous investigations have demonstrated the feasibility of using untargeted metabolomics as a platform linking exposure to internal dose and biological response. Our review of the 47 existing untargeted HRM-air pollution studies points to an underlying coherence and consistency across a range of sample analytical quantitation methods, extraction algorithms, and statistical modeling approaches. Future directions should focus on validation of these findings via hypothesis-driven protocols and technical advances in metabolic annotation and quantification. https://doi.org/10.1289/EHP11851.
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Affiliation(s)
- Donghai Liang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Zhenjiang Li
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Jelle Vlaanderen
- Department Population Health Sciences, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Ziyin Tang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Dean P. Jones
- Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Roel Vermeulen
- Department Population Health Sciences, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Jeremy A. Sarnat
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
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Wang XC, Ma XL, Liu JN, Zhang Y, Zhang JN, Ma MH, Ma FL, Yu YJ, She Y. A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics. Anal Chim Acta 2023; 1254:341127. [PMID: 37005031 DOI: 10.1016/j.aca.2023.341127] [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: 11/23/2022] [Revised: 03/15/2023] [Accepted: 03/20/2023] [Indexed: 03/29/2023]
Abstract
Data analysis of ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is an essential and time-consuming step in plant metabolomics and feature extraction is the fundamental step for current tools. Various methods lead to different feature extraction results in practical applications, which may puzzle users for selecting adequate data analysis tools to deal with collected data. In this work, we provide a comprehensive method evaluation for some advanced UHPLC-HRMS data analysis tools in plant metabolomics, including MS-DIAL, XCMS, MZmine, AntDAS, Progenesis QI, and Compound Discoverer. Both mixtures of standards and various complex plant matrices were specifically designed for evaluating the performances of the involved method in analyzing both targeted and untargeted metabolomics. Results indicated that AntDAS provide the most acceptable feature extraction, compound identification, and quantification results in targeted compound analysis. Concerning the complex plant dataset, both MS-DIAL and AntDAS can provide more reliable results than the others. The method comparison is maybe useful for the selection of suitable data analysis tools for users.
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Affiliation(s)
- Xing-Cai Wang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310032, China
| | - Xing-Ling Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Jia-Nan Liu
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Yang Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Jia-Ni Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Meng-Han Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Feng-Lian Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Yong-Jie Yu
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China.
| | - Yuanbin She
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310032, China.
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Feng Y, Zhang M, Liu Y, Yang X, Wei F, Jin X, Liu D, Guo Y, Hu Y. Quantitative microbiome profiling reveals the developmental trajectory of the chicken gut microbiota and its connection to host metabolism. IMETA 2023; 2:e105. [PMID: 38868437 PMCID: PMC10989779 DOI: 10.1002/imt2.105] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/06/2023] [Accepted: 03/15/2023] [Indexed: 06/14/2024]
Abstract
Revealing the assembly and succession of the chicken gut microbiota is critical for a better understanding of its role in chicken physiology and metabolism. However, few studies have examined dynamic changes of absolute chicken gut microbes using the quantitative microbiome profiling (QMP) method. Here, we revealed the developmental trajectory of the broiler chicken gut bacteriome and mycobiome by combining high-throughput sequencing with a microbial load quantification assay. We showed that chicken gut microbiota abundance and diversity reached a plateau at 7 days posthatch (DPH), forming segment-specific community types after 1 DPH. The bacteriome was more impacted by deterministic processes, and the mycobiome was more affected by stochastic processes. We also observed stage-specific microbes in different gut segments, and three microbial occurrence patterns including "colonization," "disappearance," and "core" were defined. The microbial co-occurrence networks were very different among gut segments, with more positive associations than negative associations. Furthermore, we provided links between the absolute changes in chicken gut microbiota and their serum metabolite variations. Time-course untargeted metabolomics revealed six metabolite clusters with different changing patterns of abundance. The foregut microbiota had more connections with chicken serum metabolites, and the gut microbes were closely related to chicken lipid and amino acid metabolism. The present study provided a full landscape of chicken gut microbiota development in a quantitative manner, and the associations between gut microbes and chicken serum metabolites further highlight the impact of gut microbiota in chicken growth and development.
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Affiliation(s)
- Yuqing Feng
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Meihong Zhang
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Yan Liu
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Xinyue Yang
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Fuxiao Wei
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Xiaolu Jin
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Dan Liu
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Yuming Guo
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Yongfei Hu
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
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89
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Tanzo JT, Li VL, Wiggenhorn AL, Moya-Garzon MD, Wei W, Lyu X, Dong W, Tahir UA, Chen ZZ, Cruz DE, Deng S, Shi X, Zheng S, Guo Y, Sims M, Abu-Remaileh M, Wilson JG, Gerszten RE, Long JZ, Benson MD. CYP4F2 is a human-specific determinant of circulating N-acyl amino acid levels. J Biol Chem 2023:104764. [PMID: 37121548 DOI: 10.1016/j.jbc.2023.104764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/21/2023] [Accepted: 04/23/2023] [Indexed: 05/02/2023] Open
Abstract
N-acyl amino acids are a large family of circulating lipid metabolites that modulate energy expenditure and fat mass in rodents. However, little is known about the regulation and potential cardiometabolic functions of N-acyl amino acids in humans. Here, we analyze the cardiometabolic phenotype associations and genomic associations of four plasma N-acyl amino acids (N-oleoyl-leucine, N-oleoyl-phenylalanine, N-oleoyl-serine, and N-oleoyl-glycine) in 2,351 individuals from the Jackson Heart Study. We find that plasma levels of specific N-acyl amino acids are associated with cardiometabolic disease endpoints independent of free amino acid plasma levels and in patterns according to the amino acid head group. By integrating whole genome sequencing data with N-acyl amino acid levels, we identify that the genetic determinants of N-acyl amino acid levels also cluster according to amino acid head group. Furthermore, we identify the CYP4F2 locus as a genetic determinant of plasma N-oleoyl-leucine and N-oleoyl-phenylalanine levels in human plasma. In experimental studies, we demonstrate that CYP4F2-mediated hydroxylation of N-oleoyl-leucine and N-oleoyl-phenylalanine results in metabolic diversification and production of many previously unknown lipid metabolites with varying characteristics of the fatty acid tail group, including several that structurally resemble fatty acid hydroxy fatty acids (FAHFAs). These studies provide a structural framework for understanding the regulation and disease-associations of N-acyl amino acids in humans and identify that the diversity of this lipid signaling family can be significantly expanded through CYP4F-mediated ω-hydroxylation.
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Affiliation(s)
- Julia T Tanzo
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Veronica L Li
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA; Department of Chemistry, Stanford University, Stanford, CA, USA; Wu Tsai Human Performance Alliance, Stanford University, CA, USA
| | - Amanda L Wiggenhorn
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA; Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Maria Dolores Moya-Garzon
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA
| | - Wei Wei
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA; Department of Biology, Stanford University, Stanford, CA, USA
| | - Xuchao Lyu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA; Wu Tsai Human Performance Alliance, Stanford University, CA, USA
| | - Wentao Dong
- Stanford ChEM-H, Stanford University, Stanford, CA, USA; Department of Chemical Engineering, Stanford University, Stanford, CA, USA
| | - Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Daniel E Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Shuning Zheng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Yan Guo
- Univ of Mississippi Medical Center, Jackson, MS
| | - Mario Sims
- Univ of Mississippi Medical Center, Jackson, MS
| | - Monther Abu-Remaileh
- Stanford ChEM-H, Stanford University, Stanford, CA, USA; Department of Chemical Engineering, Stanford University, Stanford, CA, USA
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; Broad Institute of Harvard and MIT, Cambridge, MA
| | - Jonathan Z Long
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford ChEM-H, Stanford University, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA; Wu Tsai Human Performance Alliance, Stanford University, CA, USA.
| | - Mark D Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
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90
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Toral PG, Abecia L, Hervás G, Yáñez-Ruiz DR, Frutos P. Plasma and milk metabolomics in lactating sheep divergent for feed efficiency. J Dairy Sci 2023; 106:3947-3960. [PMID: 37105878 DOI: 10.3168/jds.2022-22609] [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: 08/01/2022] [Accepted: 12/30/2022] [Indexed: 04/29/2023]
Abstract
Enhancing the ability of animals to convert feed into meat or milk by optimizing feed efficiency (FE) has become a priority in livestock research. Although untargeted metabolomics is increasingly used in this field and may improve our understanding of FE, no information in this regard is available in dairy ewes. This study was conducted to (1) discriminate sheep divergent for FE and (2) provide insights into the physiological mechanisms contributing to FE through high-throughput metabolomics. The ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF-MS) technique was applied to easily accessible animal fluids (plasma and milk) to assess whether their metabolome differs between high- and low-feed efficient lactating ewes (H-FE and L-FE groups, respectively; 8 animals/group). Blood and milk samples were collected on the last day of the 3-wk period used for FE estimation. A total of 793 features were detected in plasma and 334 in milk, with 100 and 38 of them, respectively, showing differences between H-FE and L-FE. The partial least-squares discriminant analysis separated both groups of animals regardless of the type of sample. Plasma allowed the detection of a greater number of differential features; however, results also supported the usefulness of milk, more easily accessible, to discriminate dairy sheep divergent for FE. Regarding pathway analysis, nitrogen metabolism (either anabolism or catabolism) seemed to play a central role in FE, with plasma and milk consistently indicating a great impact of AA metabolism. A potential influence of pathways related to energy/lipid metabolism on FE was also observed. The variable importance in the projection plot revealed 15 differential features in each matrix that contributed the most for the separation in H-FE and L-FE, such as l-proline and phosphatidylcholine 20:4e in plasma or l-pipecolic acid and phosphatidylethanolamine (18:2) in milk. Overall, untargeted metabolomics provided valuable information into metabolic pathways that may underlie FE in dairy ewes, with a special relevance of AA metabolism in determining this complex phenotype in the ovine. Further research is warranted to validate these findings.
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Affiliation(s)
- Pablo G Toral
- Instituto de Ganadería de Montaña (CSIC-University of León), Finca Marzanas s/n, 24346 Grulleros, León, Spain
| | - Leticia Abecia
- Department of Immunology, Microbiology and Parasitology, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain
| | - Gonzalo Hervás
- Instituto de Ganadería de Montaña (CSIC-University of León), Finca Marzanas s/n, 24346 Grulleros, León, Spain.
| | - David R Yáñez-Ruiz
- Estación Experimental del Zaidín (CSIC), Profesor Albareda 1, 18008 Granada, Spain
| | - Pilar Frutos
- Instituto de Ganadería de Montaña (CSIC-University of León), Finca Marzanas s/n, 24346 Grulleros, León, Spain
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91
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Teneva I, Belkinova D, Paunova-Krasteva T, Bardarov K, Moten D, Mladenov R, Dzhambazov B. Polyphasic characterisation of Microcoleusautumnalis (Gomont, 1892) Strunecky, Komárek & J.R.Johansen, 2013 (Oscillatoriales, Cyanobacteria) using a metabolomic approach as a complementary tool. Biodivers Data J 2023; 11:e100525. [PMID: 38327371 PMCID: PMC10848847 DOI: 10.3897/bdj.11.e100525] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/04/2023] [Indexed: 02/09/2024] Open
Abstract
As a result of the continuous revision of cyanobacterial taxonomy, Phormidiumautumnale (Agardh) Trevisan ex Gomont, 1892 has been transferred to the genus Microcoleus as Microcoleusautumnalis (Gomont, 1892) Strunecky, Komárek & J.R.Johansen, 2013. This transfer was based on a single strain and literature data. In the present study, we revise the taxonomic position of Microcoleusautumnalis by applying the classical approach of polyphasic taxonomy and additionally using metabolomics. Cyanobacterial strains identified as Phormidiumautumnale and Microcoleusvaginatus (type species of the genus Microcoleus) were used for comparative analyses. In addition, the taxonomic relationship between the species Phormidiumautumnale and Phormidiumuncinatum was determined on the basis of polyphasic characteristics. Monitoring of the morphological variability of Phormidiumautumnale and Microcoleusvaginatus strains showed a difference in the morphology concerning the ends of the trichomes, the shape of the apical cells, as well as the presence/absence of the calyptra and its shape. The performed TEM analysis of the thylakoid arrangement of the studied strains showed parietal arrangement of the thylakoids in the representatives of genus Phormidium and fascicular arrangement in genus Microcoleus. Molecular genetic analyses, based on 16S rDNA, revealed grouping of the investigated P.autumnale strains in a separate clade. This clade is far from the subtree, which is very clearly formed by the representatives of the type species of genus Microcoleus, namely M.vaginatus. The metabolomic analysis involving P.autumnale and M.vaginatus strains identified 39 compounds that could be used as potential biochemical markers to distinguish the two cyanobacterial species. Based on the data obtained, we suggest changing of the current status of Microcoleusautumnalis by restoring its previous appurtenance to the genus Phormidium under the name Phormidiumautumnale (Agardh) Trevisan ex Gomont, 1892 and distinguishing this species from genus Microcoleus.
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Affiliation(s)
- Ivanka Teneva
- Faculty of Biology, Plovdiv University “Paisii Hilendarski”, Plovdiv, BulgariaFaculty of Biology, Plovdiv University “Paisii Hilendarski”PlovdivBulgaria
| | - Detelina Belkinova
- Faculty of Biology, Plovdiv University “Paisii Hilendarski”, Plovdiv, BulgariaFaculty of Biology, Plovdiv University “Paisii Hilendarski”PlovdivBulgaria
- Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, Sofia, BulgariaInstitute of Biodiversity and Ecosystem Research, Bulgarian Academy of SciencesSofiaBulgaria
| | - Tsvetelina Paunova-Krasteva
- The Stephan Angeloff Institute of Мicrobiology, Bulgarian Academy of Sciences, Sofia, BulgariaThe Stephan Angeloff Institute of Мicrobiology, Bulgarian Academy of SciencesSofiaBulgaria
| | - Krum Bardarov
- InoBioTech Ltd., Sofia, BulgariaInoBioTech Ltd.SofiaBulgaria
| | - Dzhemal Moten
- Faculty of Biology, Plovdiv University “Paisii Hilendarski”, Plovdiv, BulgariaFaculty of Biology, Plovdiv University “Paisii Hilendarski”PlovdivBulgaria
| | - Rumen Mladenov
- Faculty of Biology, Plovdiv University “Paisii Hilendarski”, Plovdiv, BulgariaFaculty of Biology, Plovdiv University “Paisii Hilendarski”PlovdivBulgaria
| | - Balik Dzhambazov
- Faculty of Biology, Plovdiv University “Paisii Hilendarski”, Plovdiv, BulgariaFaculty of Biology, Plovdiv University “Paisii Hilendarski”PlovdivBulgaria
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92
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Parker EJ, Billane KC, Austen N, Cotton A, George RM, Hopkins D, Lake JA, Pitman JK, Prout JN, Walker HJ, Williams A, Cameron DD. Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools. Metabolites 2023; 13:metabo13040463. [PMID: 37110122 PMCID: PMC10142740 DOI: 10.3390/metabo13040463] [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: 02/01/2023] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.
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Affiliation(s)
| | - Kathryn C Billane
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Nichola Austen
- Department of Biology, University of Oxford, Oxford OX1 3RB, UK
| | - Anne Cotton
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Rachel M George
- biOMICS Mass Spectrometry Centre, University of Sheffield, Sheffield S10 2TN, UK
| | - David Hopkins
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
| | - Janice A Lake
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
| | - James K Pitman
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - James N Prout
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Heather J Walker
- biOMICS Mass Spectrometry Centre, University of Sheffield, Sheffield S10 2TN, UK
| | - Alex Williams
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Duncan D Cameron
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
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93
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Tanzo JT, Li VL, Wiggenhorn AL, Moya-Garzon MD, Wei W, Lyu X, Dong W, Tahir UA, Chen ZZ, Cruz DE, Deng S, Shi X, Zheng S, Guo Y, Sims M, Abu-Remaileh M, Wilson JG, Gerszten RE, Long JZ, Benson MD. CYP4F2 is a human-specific determinant of circulating N-acyl amino acid levels. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.09.531581. [PMID: 36945562 PMCID: PMC10028954 DOI: 10.1101/2023.03.09.531581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
N-acyl amino acids are a large family of circulating lipid metabolites that modulate energy expenditure and fat mass in rodents. However, little is known about the regulation and potential cardiometabolic functions of N-acyl amino acids in humans. Here, we analyze the cardiometabolic phenotype associations and genetic regulation of four plasma N-fatty acyl amino acids (N-oleoyl-leucine, N-oleoyl-phenylalanine, N-oleoyl-serine, and N-oleoyl-glycine) in 2,351 individuals from the Jackson Heart Study. N-oleoyl-leucine and N-oleoyl-phenylalanine were positively associated with traits related to energy balance, including body mass index, waist circumference, and subcutaneous adipose tissue. In addition, we identify the CYP4F2 locus as a human-specific genetic determinant of plasma N-oleoyl-leucine and N-oleoyl-phenylalanine levels. In vitro, CYP4F2-mediated hydroxylation of N-oleoyl-leucine and N-oleoyl-phenylalanine results in metabolic diversification and production of many previously unknown lipid metabolites with varying characteristics of the fatty acid tail group, including several that structurally resemble fatty acid hydroxy fatty acids (FAHFAs). By contrast, FAAH-regulated N-oleoyl-glycine and N-oleoyl-serine were inversely associated with traits related to glucose and lipid homeostasis. These data uncover a human-specific enzymatic node for the metabolism of a subset of N-fatty acyl amino acids and establish a framework for understanding the cardiometabolic roles of individual N-fatty acyl amino acids in humans.
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94
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Kao CY, Chang CT, Kuo PY, Lin CJ, Chiu HH, Liao HW. Sequential isolation of metabolites and lipids from a single sample to achieve multiomics by using TRIzol reagent. Talanta 2023; 258:124416. [PMID: 36889188 DOI: 10.1016/j.talanta.2023.124416] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 02/13/2023] [Accepted: 03/02/2023] [Indexed: 03/06/2023]
Abstract
Simultaneous extraction of various types of biomolecule from a single sample can be beneficial for multiomics studies of unique specimens. An efficient and convenient sample preparation approach must be developed that can comprehensively isolate and extract biomolecules from one sample. TRIzol reagent is widely used in biological studies for DNA, RNA, and protein isolation. This study evaluated the feasibility of using TRIzol reagent for the simultaneous isolation of not only DNA, RNA, and proteins but also metabolites and lipids from a single sample. Through the comparison of known metabolites and lipids obtained using the conventional methanol (MeOH) and methyl-tert-butyl ether (MTBE) extraction methods, we determined the presence of metabolites and lipids in the supernatant during TRIzol sequential isolation. Finally, we performed untargeted metabolomics and lipidomics to examine metabolite and lipid alterations associated with the jhp0417 mutation in Helicobacter pylori by using the TRIzol sequential isolation protocol and MeOH and MTBE extraction methods. Metabolites and lipids with significant differences isolated using the TRIzol sequential isolation protocol were consistent with those obtained using the conventional MeOH and MTBE extraction methods. These results indicated that TRIzol reagent can be used to simultaneously isolate metabolites and lipids from a single sample. Thus, TRIzol reagent can be used in biological and clinical research, especially in multiomics studies.
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Affiliation(s)
- Cheng-Yen Kao
- Institute of Microbiology and Immunology, College of Life Sciences, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - Chung-Te Chang
- Institute of Biochemistry and Molecular Biology, College of Life Sciences, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - Pei-Yun Kuo
- Institute of Microbiology and Immunology, College of Life Sciences, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - Chia-Jen Lin
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - Huai-Hsuan Chiu
- The Metabolomics Core Laboratory, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei, 10617, Taiwan; Department of Medical Research, National Taiwan University Hospital, Taipei, 10617, Taiwan
| | - Hsiao-Wei Liao
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan.
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95
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Naznin M, Badrul Alam M, Alam R, Islam S, Rakhmat S, Lee SH, Kim S. Metabolite profiling of Nymphaea rubra (Burm. f.) flower extracts using cyclic ion mobility–mass spectrometry and their associated biological activities. Food Chem 2023; 404:134544. [DOI: 10.1016/j.foodchem.2022.134544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 08/24/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022]
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96
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Farke N, Schramm T, Verhülsdonk A, Rapp J, Link H. Systematic analysis of in-source modifications of primary metabolites during flow-injection time-of-flight mass spectrometry. Anal Biochem 2023; 664:115036. [PMID: 36627043 PMCID: PMC9902335 DOI: 10.1016/j.ab.2023.115036] [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: 09/21/2022] [Revised: 12/09/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
Flow-injection mass spectrometry (FI-MS) enables metabolomics studies with a very high sample-throughput. However, FI-MS is prone to in-source modifications of analytes because samples are directly injected into the electrospray ionization source of a mass spectrometer without prior chromatographic separation. Here, we spiked authentic standards of 160 primary metabolites individually into an Escherichia coli metabolite extract and measured the thus derived 160 spike-in samples by FI-MS. Our results demonstrate that FI-MS can capture a wide range of chemically diverse analytes within 30 s measurement time. However, the data also revealed extensive in-source modifications. Across all 160 spike-in samples, we identified significant increases of 11,013 ion peaks in positive and negative mode combined. To explain these unknown m/z features, we connected them to the m/z feature of the (de-)protonated metabolite using information about mass differences and MS2 spectra. This resulted in networks that explained on average 49 % of all significant features. The networks showed that a single metabolite undergoes compound specific and often sequential in-source modifications like adductions, chemical reactions, and fragmentations. Our results show that FI-MS generates complex MS1 spectra, which leads to an overestimation of significant features, but neutral losses and MS2 spectra explain many of these features.
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Affiliation(s)
| | | | | | | | - Hannes Link
- Bacterial Metabolomics, CMFI, University Tübingen, Auf der Morgenstelle 24, 7206, Tübingen, Germany.
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97
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Dierickx S, Souvereyns M, Roelants SLKW, De Graeve M, Van Meulebroek L, De Maeseneire SL, Soetaert WKG, Vanhaecke L. Comprehensive metabolomics reveals correlation between sophorolipid biosynthesis and autophagy. N Biotechnol 2023; 75:1-12. [PMID: 36805132 DOI: 10.1016/j.nbt.2023.02.002] [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: 12/05/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 02/19/2023]
Abstract
Sophorolipids are biobased and biodegradable glycolipid surface-active agents contributing to the shift from petroleum to biobased surfactants, associated with clear environmental benefits. However, their production cost is currently too high to allow commercialisation. Therefore, a continuous sophorolipid production process was evaluated, i.e., a retentostat with an external filtration unit. Despite an initial increase in volumetric productivity, productivity eventually declined to almost 0 g L-1 h-1. Following comprehensive metabolomics on supernatant obtained from a standardised retentostat, we hypothesised exhaustion of the N-starvation-induced autophagy as the main mechanism responsible for the decline in bolaform sophorolipid productivity. Thirty-six metabolites that correlate with RNA/protein autophagy and high sophorolipid productivity were putatively identified. In conclusion, our results unveil a plausible cause of this bola sophorolipid productivity decline in an industrially relevant bioreactor set-up, which may thus impact majorly on future yeast biosurfactant regulation studies and the finetuning of bola sophorolipid production processes.
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Affiliation(s)
- Sven Dierickx
- Centre for Industrial Biotechnology and Biocatalysis (InBio.be), Department of Biotechnology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium; Laboratory of Integrative Metabolomics (LIMET), Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - Maximilien Souvereyns
- Centre for Industrial Biotechnology and Biocatalysis (InBio.be), Department of Biotechnology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | - Sophie L K W Roelants
- Centre for Industrial Biotechnology and Biocatalysis (InBio.be), Department of Biotechnology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium; Bio Base Europe Pilot Plant (BBEPP), Rodenhuizekaai 1, 9042 Ghent, Belgium
| | - Marilyn De Graeve
- Laboratory of Integrative Metabolomics (LIMET), Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - Lieven Van Meulebroek
- Laboratory of Integrative Metabolomics (LIMET), Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - Sofie L De Maeseneire
- Centre for Industrial Biotechnology and Biocatalysis (InBio.be), Department of Biotechnology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | - Wim K G Soetaert
- Centre for Industrial Biotechnology and Biocatalysis (InBio.be), Department of Biotechnology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium; Bio Base Europe Pilot Plant (BBEPP), Rodenhuizekaai 1, 9042 Ghent, Belgium
| | - Lynn Vanhaecke
- Laboratory of Integrative Metabolomics (LIMET), Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium; Institute for Global Food Security, School of Biological Sciences, Queen's University, University Road, Belfast BT7 1NN, United Kingdom.
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98
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Torres CL, Scalco FB, de Oliveira MLC, Peake RWA, Garrett R. Untargeted LC-HRMS metabolomics reveals candidate biomarkers for mucopolysaccharidoses. Clin Chim Acta 2023; 541:117250. [PMID: 36764508 DOI: 10.1016/j.cca.2023.117250] [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: 10/27/2022] [Revised: 12/19/2022] [Accepted: 02/06/2023] [Indexed: 02/11/2023]
Abstract
BACKGROUND Mucopolysaccharidoses (MPSs) are inherited genetic diseases caused by an absence or deficiency of lysosomal enzymes responsible for catabolizing glycosaminoglycans (GAGs). Undiagnosed patients, or those without adequate treatment in early life, can be severely and irreversibly affected by the disease. In this study, we applied liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics to identify potential biomarkers for MPS disorders to better understand how MPS may affect the metabolome of patients. METHODS Urine samples from 37 MPS patients (types I, II, III, IV, and VI; untreated and treated with enzyme replacement therapy (ERT)) and 38 controls were analyzed by LC-HRMS. Data were processed by an untargeted metabolomics workflow and submitted to multivariate statistical analyses to reveal significant differences between the MPS and control groups. RESULTS A total of 12 increased metabolites common to all MPS types were identified. Dipeptides, amino acids and derivatives were increased in the MPS group compared to controls. N-acetylgalactosamines 4- or 6-sulfate, important constituents of GAGs, were also elevated in MPS patients, most prominently in those with MPS VI. Notably, treated patients exhibited lower levels of the aforementioned acylaminosugars than untreated patients in all MPS types. CONCLUSIONS Untargeted metabolomics has enabled the detection of metabolites that could improve our understanding of MPS physiopathology. These potential biomarkers can be utilized in screening methods to support diagnosis and ERT monitoring.
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Affiliation(s)
- Clarisse L Torres
- Metabolomics Laboratory, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Fernanda B Scalco
- Inborn Error of Metabolism Laboratory, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Maria Lúcia C de Oliveira
- Inborn Error of Metabolism Laboratory, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Roy W A Peake
- Department of Laboratory Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rafael Garrett
- Metabolomics Laboratory, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Department of Laboratory Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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99
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Wang X, Zheng F, Sheng M, Xu G, Lin X. Retention time prediction for small samples based on integrating molecular representations and adaptive network. J Chromatogr B Analyt Technol Biomed Life Sci 2023; 1217:123624. [PMID: 36780745 DOI: 10.1016/j.jchromb.2023.123624] [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: 10/22/2022] [Revised: 01/13/2023] [Accepted: 01/27/2023] [Indexed: 02/07/2023]
Abstract
Retention time (RT) can provide orthogonal information different from that of mass spectrometry and contribute to identifying compounds. Many machine learning methods have been developed and applied to RT prediction. In application, the training data size is usually small in most chromatography systems. To enhance the performance of RT prediction, this study proposes a RT prediction method based on multi-data combinations and adaptive neural network (MDC-ANN). MDC-ANN establishes the RT prediction model for the target chromatographic system through transfer learning and a base deep learning model trained on a big dataset. It selects the optimal molecular representation combination from the multiple input candidates and automatically determines the neural network structure according to the determined input combination. MDC-ANN was compared with two new efficient deep learning methods, three transferring methods and four popular machine learning methods on 14 small datasets and showed advantages in MAE, MedAE, MRE and R2 in most cases. The experiment results illustrated that integrating multiple molecular representations can provide more information, improve the performance of RT prediction and contribute to compound annotation, different chromatographic systems may use different molecular representation combinations to obtain good RT prediction performance. Hence, MDC-ANN which automatically determines the best combination of molecular representations for a specific system is promising for predicting RTs accurately in real applications.
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Affiliation(s)
- Xiaoxiao Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Fujian Zheng
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China.
| | - Meizhen Sheng
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China.
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100
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Lenski M, Maallem S, Zarcone G, Garçon G, Lo-Guidice JM, Anthérieu S, Allorge D. Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics. Metabolites 2023; 13:metabo13020282. [PMID: 36837901 PMCID: PMC9962007 DOI: 10.3390/metabo13020282] [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: 01/09/2023] [Revised: 02/07/2023] [Accepted: 02/12/2023] [Indexed: 02/18/2023] Open
Abstract
Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites. R2 score of the linear regression between predicted and measured data achieved 0.938 and 0.898 for CCS and Rt, respectively, demonstrating the models' reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data.
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Affiliation(s)
- Marie Lenski
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
- CHU Lille, Unité Fonctionnelle de Toxicologie, F-59037 Lille, France
- Correspondence:
| | - Saïd Maallem
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
| | - Gianni Zarcone
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
| | - Guillaume Garçon
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
| | - Jean-Marc Lo-Guidice
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
| | - Sébastien Anthérieu
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
| | - Delphine Allorge
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
- CHU Lille, Unité Fonctionnelle de Toxicologie, F-59037 Lille, France
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