bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2022‒02‒27
twenty-two papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh
  1. Comparison of Lysis and Detachment Sample Preparation Methods for Cultured Triple-Negative Breast Cancer Cells Using UHPLC-HRMS-Based Metabolomics.
  2. Mixed-mode chromatography-mass spectrometry enables targeted and untargeted screening of carboxylic acids in biological samples.
  3. Analysis of the Sphingolipidome in NAFLD.
  4. Interplay Between m6A RNA Methylation and Regulation of Metabolism in Cancer.
  5. Fatty Acid Metabolism in Ovarian Cancer: Therapeutic Implications.
  6. Overcoming Radiation Resistance in Gliomas by Targeting Metabolism and DNA Repair Pathways.
  7. Cloud-based archived metabolomics data: A resource for in-source fragmentation/annotation, meta-analysis and systems biology.
  8. A deeper look at carrier proteome effects for single-cell proteomics.
  9. DIA proteomics data from a UPS1-spiked E.coli protein mixture processed with six software tools.
  10. Comprehensive Peak Characterization (CPC) in Untargeted LC-MS Analysis.
  11. Clinical lipidomics - A community-driven roadmap to translate research into clinical applications.
  12. A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.
  13. Metabolic profiling of prostate cancer in skeletal microenvironments identifies G6PD as a key mediator of growth and survival.
  14. Comparative Evaluation of Plasma Metabolomic Data from Multiple Laboratories.
  15. Simple and Sensitive Method for the Quantitative Determination of Lipid Hydroperoxides by Liquid Chromatography/Mass Spectrometry.
  16. Nontargeted and targeted metabolomics approaches reveal the key amino acid alterations involved in multiple myeloma.
  17. Lipid Metabolism and Epigenetics Crosstalk in Prostate Cancer.
  18. Systematic Evaluation of HILIC Stationary Phases for Global Metabolomics of Human Plasma.
  19. HepaCAM‑PIK3CA axis regulates the reprogramming of glutamine metabolism to inhibit prostate cancer cell proliferation.
  20. Acute aerobic exercise reveals FAHFAs distinguish the metabolomes of overweight and normal weight runners.
  21. Measurement of Hepatic Lipids.
  22. The Potential of Metabolomics in Biomedical Applications.
  1. Metabolites. 2022 Feb 10. pii: 168. [Epub ahead of print]12(2):
    Comparison of Lysis and Detachment Sample Preparation Methods for Cultured Triple-Negative Breast Cancer Cells Using UHPLC-HRMS-Based Metabolomics.
    Blake R Rushing, Madison Schroder, Susan C J Sumner.
      Dysregulation of cellular metabolism is now a well-recognized hallmark of cancer. Studies investigating the metabolic features of cancer cells have shed new light onto processes in cancer cell biology and have identified many potential novel treatment options. The advancement of mass spectrometry-based metabolomics has improved the ability to monitor multiple metabolic pathways simultaneously in various experimental settings. However, questions still remain as to how certain steps in the metabolite extraction process affect the metabolic profiles of cancer cells. Here, we use ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) untargeted metabolomics to investigate the effects of different detachment and lysis methods on the types and abundances of metabolites extracted from MDA-MB-231 cells through the use of in-house standards libraries and pathway analysis software. Results indicate that detachment methods (trypsinization vs. scraping) had the greatest effect on metabolic profiles whereas lysis methods (homogenizer beads vs. freeze-thaw cycling) had a lesser, though still significant, effect. No singular method was clearly superior over others, with certain metabolite classes giving higher abundances or lower variation for each detachment-lysis combination. These results indicate the importance of carefully selecting sample preparation methods for cell-based metabolomics to optimize the extraction performance for certain compound classes.
    Keywords:  breast cancer; cell metabolomics; detachment; lysis; mass spectrometry; pathway analysis
    DOI:  https://doi.org/10.3390/metabo12020168
  2. Anal Methods. 2022 Feb 23.
    Mixed-mode chromatography-mass spectrometry enables targeted and untargeted screening of carboxylic acids in biological samples.
    Ondřej Hodek, Lidia Argemi-Muntadas, Adnan Khan, Thomas Moritz.
      Carboxylic acids are crucial metabolites in the tricarboxylic acid (TCA) cycle and thus participate in central carbon metabolism (CCM). Research dependent on the analysis of metabolites involved in central carbon metabolism requires fast separation and sensitive detection of carboxylic acids using liquid chromatography-mass spectrometry (LC-MS). However, successful separation of all carboxylic acids from the TCA cycle by liquid chromatography remains a challenging task because of their high polarity and thus low retention on the conventional reversed-phase columns. In this study, we tested a reversed-phase/anion exchange mixed-mode stationary phase (Waters BEH C18 AX) using liquid chromatography-tandem mass spectrometry (LC-MS/MS). We developed and optimized a method that enables a 10 minute separation of all carboxylic acids from the TCA cycle and lactic acid without prior derivatization or addition of ion-pair reagents in the mobile phase. The developed method was validated for quantification of 8 acids in murine brown preadipocytes, 5 acids in human plasma and 6 acids in Arabidopsis thaliana leaves with limits of quantification ranging from 0.1 μM for malic acid to 10 μM for isocitric acid. Moreover, the mixed-mode chromatography enabled untargeted screening of medium- to long-chain fatty acids in murine brown preadipocytes, Arabidopsis thaliana, and human plasma, where 23 fatty acids were identified by using liquid chromatography with high-resolution mass spectrometry (HRMS).
    DOI:  https://doi.org/10.1039/d1ay02143e
  3. Methods Mol Biol. 2022 ;2455 279-303
    Analysis of the Sphingolipidome in NAFLD.
    David Montefusco, Johana Lambert, Andrea Anderson, Jeremy Allegood, L Ashley Cowart.
      The relationship between sphingolipid levels and NAFLD pathology has been recognized for some time. Numerous studies using pharmacological and genetic approaches in vitro and in animal models of NAFLD have demonstrated that modifications to sphingolipid metabolism can attenuate various facets of NAFLD pathology. However, a more precise understanding of the role of sphingolipids and NAFLD pathology is essential to creating therapeutics that target this pathway. This chapter touches on the scale and variety of sphingolipid metabolites at play in NAFLD, which vary widely in their chemical structures and biological functions. With advances in liquid chromatography and tandem mass spectrometry approaches, each of thousands of individual sphingolipid species and sphingolipid metabolites can be identified and precisely quantified. These approaches are beginning to reveal specific sub-classes and species of sphingolipids that change in NAFLD, and as such, enzymes that generate them can be identified and potentially serve as therapeutic targets. Advances in lipidomics technology have been, and will continue to be, critical to these gains in our understanding of NAFLD.
    Keywords:  Alcoholic fatty liver disease; Ceramide; Lipid; Lipidomics; Metabolic syndrome; NAFLD; NASH; Non-alcoholic fatty liver disease; Non-alcoholic steatohepatitis; Sphingoid; Sphingolipids; Sphingosine; Sphingosine-1-phosphate
    DOI:  https://doi.org/10.1007/978-1-0716-2128-8_22
  4. Front Cell Dev Biol. 2022 ;10 813581
    Interplay Between m6A RNA Methylation and Regulation of Metabolism in Cancer.
    Youchaou Mobet, Xiaoyi Liu, Tao Liu, Jianhua Yu, Ping Yi.
      Methylation of adenosine in RNA to N6-methyladenosine (m6A) is widespread in eukaryotic cells with his integral RNA regulation. This dynamic process is regulated by methylases (editors/writers), demethylases (remover/erasers), and proteins that recognize methylation (effectors/readers). It is now evident that m6A is involved in the proliferation and metastasis of cancer cells, for instance, altering cancer cell metabolism. Thus, determining how m6A dysregulates metabolic pathways could provide potential targets for cancer therapy or early diagnosis. This review focuses on the link between the m6A modification and the reprogramming of metabolism in cancer. We hypothesize that m6A modification could dysregulate the expression of glucose, lipid, amino acid metabolism, and other metabolites or building blocks of cells by adaptation to the hypoxic tumor microenvironment, an increase in glycolysis, mitochondrial dysfunction, and abnormal expression of metabolic enzymes, metabolic receptors, transcription factors as well as oncogenic signaling pathways in both hematological malignancies and solid tumors. These metabolism abnormalities caused by m6A's modification may affect the metabolic reprogramming of cancer cells and then increase cell proliferation, tumor initiation, and metastasis. We conclude that focusing on m6A could provide new directions in searching for novel therapeutic and diagnostic targets for the early detection and treatment of many cancers.
    Keywords:  M6A; cancer; metabolism; metabolite; methylation; oncogenic; reprogramming
    DOI:  https://doi.org/10.3389/fcell.2022.813581
  5. Int J Mol Sci. 2022 Feb 16. pii: 2170. [Epub ahead of print]23(4):
    Fatty Acid Metabolism in Ovarian Cancer: Therapeutic Implications.
    Hyunho Yoon, Sanghoon Lee.
      Ovarian cancer is the most malignant gynecological tumor. Previous studies have reported that metabolic alterations resulting from deregulated lipid metabolism promote ovarian cancer aggressiveness. Lipid metabolism involves the oxidation of fatty acids, which leads to energy generation or new lipid metabolite synthesis. The upregulation of fatty acid synthesis and related signaling promote tumor cell proliferation and migration, and, consequently, lead to poor prognosis. Fatty acid-mediated lipid metabolism in the tumor microenvironment (TME) modulates tumor cell immunity by regulating immune cells, including T cells, B cells, macrophages, and natural killer cells, which play essential roles in ovarian cancer cell survival. Here, the types and sources of fatty acids and their interactions with the TME of ovarian cancer have been reviewed. Additionally, this review focuses on the role of fatty acid metabolism in tumor immunity and suggests that fatty acid and related lipid metabolic pathways are potential therapeutic targets for ovarian cancer.
    Keywords:  immune response; lipid metabolism; ovarian cancer; tumor microenvironment
    DOI:  https://doi.org/10.3390/ijms23042170
  6. Int J Mol Sci. 2022 Feb 17. pii: 2246. [Epub ahead of print]23(4):
    Overcoming Radiation Resistance in Gliomas by Targeting Metabolism and DNA Repair Pathways.
    Wei Meng, Joshua D Palmer, Michael Siedow, Saikh Jaharul Haque, Arnab Chakravarti.
      Gliomas represent a wide spectrum of brain tumors characterized by their high invasiveness, resistance to chemoradiotherapy, and both intratumoral and intertumoral heterogeneity. Recent advances in transomics studies revealed that enormous abnormalities exist in different biological layers of glioma cells, which include genetic/epigenetic alterations, RNA expressions, protein expression/modifications, and metabolic pathways, which provide opportunities for development of novel targeted therapeutic agents for gliomas. Metabolic reprogramming is one of the hallmarks of cancer cells, as well as one of the oldest fields in cancer biology research. Altered cancer cell metabolism not only provides energy and metabolites to support tumor growth, but also mediates the resistance of tumor cells to antitumor therapies. The interactions between cancer metabolism and DNA repair pathways, and the enhancement of radiotherapy sensitivity and assessment of radiation response by modulation of glioma metabolism are discussed herein.
    Keywords:  DNA repair; cancer metabolism; gliomas; radiation resistance
    DOI:  https://doi.org/10.3390/ijms23042246
  7. Anal Sci Adv. 2020 Jun;1(1): 70-80
    Cloud-based archived metabolomics data: A resource for in-source fragmentation/annotation, meta-analysis and systems biology.
    Amelia Palermo, Tao Huan, Duane Rinehart, Markus M Rinschen, Shuzhao Li, Valerie B O'Donnell, Eoin Fahy, Jingchuan Xue, Shankar Subramaniam, H Paul Benton, Gary Siuzdak.
      Archived metabolomics data represent a broad resource for the scientific community. However, the absence of tools for the meta-analysis of heterogeneous data types makes it challenging to perform direct comparisons in a single and cohesive workflow. Here we present a framework for the meta-analysis of metabolic pathways and interpretation with proteomic and transcriptomic data. This framework facilitates the comparison of heterogeneous types of metabolomics data from online repositories (e.g., XCMS Online, Metabolomics Workbench, GNPS, and MetaboLights) representing tens of thousands of studies, as well as locally acquired data. As a proof of concept, we apply the workflow for the meta-analysis of i) independent colon cancer studies, further interpreted with proteomics and transcriptomics data, ii) multimodal data from Alzheimer's disease and mild cognitive impairment studies, demonstrating its high-throughput capability for the systems level interpretation of metabolic pathways. Moreover, the platform has been modified for improved knowledge dissemination through a collaboration with Metabolomics Workbench and LIPID MAPS. We envision that this meta-analysis tool will help overcome the primary bottleneck in analyzing diverse datasets and facilitate the full exploitation of archival metabolomics data for addressing a broad array of questions in metabolism research and systems biology.
    Keywords:  Archived data; meta-analysis; metabolic pathways; metabolomics; proteomics; systems biology; transcriptomics
    DOI:  https://doi.org/10.1002/ansa.202000042
  8. Commun Biol. 2022 02 22. 5(1): 150
    A deeper look at carrier proteome effects for single-cell proteomics.
    Zilu Ye, Tanveer S Batth, Patrick Rüther, Jesper V Olsen.
      Multiplexing approaches using tandem mass tags with a carrier proteome to boost sensitivity have advanced single cell proteomics by mass spectrometry (SCoPE-MS). Here, we probe the carrier proteome effects in single cell proteomics with mixed species TMTpro-labeled samples. We demonstrate that carrier proteomes, while increasing overall identifications, dictate which proteins are identified. We show that quantitative precision and signal intensity are limited at high carrier levels, hindering the recognition of regulated proteins. Guidelines for optimized mass spectrometry acquisition parameters and best practices for fold-change or protein copy number-based comparisons are provided.
    DOI:  https://doi.org/10.1038/s42003-022-03095-4
  9. Data Brief. 2022 Apr;41 107829
    DIA proteomics data from a UPS1-spiked E.coli protein mixture processed with six software tools.
    Clarisse Gotti, Florence Roux-Dalvai, Charles Joly-Beauparlant, Loïc Mangnier, Mickaël Leclercq, Arnaud Droit.
      In this article, we provide a proteomic reference dataset that has been initially generated for a benchmarking of software tools for Data-Independent Acquisition (DIA) analysis. This large dataset includes 96 DIA .raw files acquired from a complex proteomic standard composed of an E.coli protein background spiked-in with 8 different concentrations of 48 human proteins (UPS1 Sigma). These 8 samples were analyzed in triplicates on an Orbitrap mass spectrometer with 4 different DIA window schemes. We also provide the spectral libraries and FASTA file used for their analysis and the software outputs of the six tools used in this study: DIA-NN, Spectronaut, ScaffoldDIA, DIA-Umpire, Skyline and OpenSWATH. This dataset also contains post-processed quantification tables where the peptides and proteins have been validated, their intensities normalized and the missing values imputed with a noise value. All the files are available on ProteomeXchange. Altogether, these files represent the most comprehensive DIA reference dataset acquired on an Orbitrap instrument ever published. It will be a very useful resource to the proteomic scientists in order to assess the performance of DIA software tools or to test their processing pipelines, to the software developers to improve their tools or develop new ones and to the students for their training on proteomics data analysis.
    Keywords:  Complex proteomic standard; Data Independent Acquisition; Software tools benchmark; Spiked UPS1 human proteins
    DOI:  https://doi.org/10.1016/j.dib.2022.107829
  10. Metabolites. 2022 Feb 02. pii: 137. [Epub ahead of print]12(2):
    Comprehensive Peak Characterization (CPC) in Untargeted LC-MS Analysis.
    Kristian Pirttilä, David Balgoma, Johannes Rainer, Curt Pettersson, Mikael Hedeland, Carl Brunius.
      LC-MS-based untargeted metabolomics is heavily dependent on algorithms for automated peak detection and data preprocessing due to the complexity and size of the raw data generated. These algorithms are generally designed to be as inclusive as possible in order to minimize the number of missed peaks. This is known to result in an abundance of false positive peaks that further complicate downstream data processing and analysis. As a consequence, considerable effort is spent identifying features of interest that might represent peak detection artifacts. Here, we present the CPC algorithm, which allows automated characterization of detected peaks with subsequent filtering of low quality peaks using quality criteria familiar to analytical chemists. We provide a thorough description of the methods in addition to applying the algorithms to authentic metabolomics data. In the example presented, the algorithm removed about 35% of the peaks detected by XCMS, a majority of which exhibited a low signal-to-noise ratio. The algorithm is made available as an R-package and can be fully integrated into a standard XCMS workflow.
    Keywords:  XCMS; algorithm; data processing; data quality; false peaks; metabolomics; peak characterization; peak detection; peak filtering; untargeted
    DOI:  https://doi.org/10.3390/metabo12020137
  11. J Mass Spectrom Adv Clin Lab. 2022 Apr;24 1-4
    Clinical lipidomics - A community-driven roadmap to translate research into clinical applications.
    Olga Vvedenskaya, Michal Holčapek, Michael Vogeser, Kim Ekroos, Peter J Meikle, Anne K Bendt.
      Lipid metabolites, beyond triglycerides and cholesterol, have been shown to have vast potential for applications in clinical applications, with substantial societal and economical value. To successfully evolve from the current research-grade methods to assays suitable for routine clinical applications, a harmonization - if not standardization - of these mass spectrometry-based workflows is necessary. Input on clinical needs and technological capabilities must be obtained from all relevant stakeholders, including wet lab scientists, informaticians and data scientists, manufacturers, and medical professionals. In order to build bridges between this diverse group of professionals, the International Lipidomics Society and its Clinical Lipidomics Interest Group were created. This opinion article is intended to provide an overview of international efforts to tackle the issues of workflow harmonization, and to serve as an open invitation for others to join this growing community.
    Keywords:  Harmonization; Lipidomics; Standardization; Translation; Validation
    DOI:  https://doi.org/10.1016/j.jmsacl.2022.02.002
  12. Metabolites. 2022 Feb 11. pii: 173. [Epub ahead of print]12(2):
    A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.
    Johannes Rainer, Andrea Vicini, Liesa Salzer, Jan Stanstrup, Josep M Badia, Steffen Neumann, Michael A Stravs, Vinicius Verri Hernandes, Laurent Gatto, Sebastian Gibb, Michael Witting.
      Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.
    Keywords:  R programming; annotation; metabolomics; reproducible research; small-compound databases; untargeted analysis
    DOI:  https://doi.org/10.3390/metabo12020173
  13. Sci Adv. 2022 Feb 25. 8(8): eabf9096
    Metabolic profiling of prostate cancer in skeletal microenvironments identifies G6PD as a key mediator of growth and survival.
    Jessica Whitburn, Srinivasa R Rao, Emma V Morris, Sho Tabata, Akiyoshi Hirayama, Tomoyoshi Soga, James R Edwards, Zeynep Kaya, Charlotte Palmer, Freddie C Hamdy, Claire M Edwards.
      The spread of cancer to bone is invariably fatal, with complex cross-talk between tumor cells and the bone microenvironment responsible for driving disease progression. By combining in silico analysis of patient datasets with metabolomic profiling of prostate cancer cells cultured with bone cells, we demonstrate the changing energy requirements of prostate cancer cells in the bone microenvironment, identifying the pentose phosphate pathway (PPP) as elevated in prostate cancer bone metastasis, with increased expression of the PPP rate-limiting enzyme glucose-6-phosphate dehydrogenase (G6PD) associated with a reduction in progression-free survival. Genetic and pharmacologic manipulation demonstrates that G6PD inhibition reduces prostate cancer growth and migration, associated with changes in cellular redox state and increased chemosensitivity. Genetic blockade of G6PD in vivo results in reduction of tumor growth within bone. In summary, we demonstrate the metabolic plasticity of prostate cancer cells in the bone microenvironment, identifying the PPP and G6PD as metabolic targets for the treatment of prostate cancer bone metastasis.
    DOI:  https://doi.org/10.1126/sciadv.abf9096
  14. Metabolites. 2022 Feb 01. pii: 135. [Epub ahead of print]12(2):
    Comparative Evaluation of Plasma Metabolomic Data from Multiple Laboratories.
    Shin Nishiumi, Yoshihiro Izumi, Akiyoshi Hirayama, Masatomo Takahashi, Motonao Nakao, Kosuke Hata, Daisuke Saigusa, Eiji Hishinuma, Naomi Matsukawa, Suzumi M Tokuoka, Yoshihiro Kita, Fumie Hamano, Nobuyuki Okahashi, Kazutaka Ikeda, Hiroki Nakanishi, Kosuke Saito, Masami Yokota Hirai, Masaru Yoshida, Yoshiya Oda, Fumio Matsuda, Takeshi Bamba.
      In mass spectrometry-based metabolomics, the differences in the analytical results from different laboratories/machines are an issue to be considered because various types of machines are used in each laboratory. Moreover, the analytical methods are unique to each laboratory. It is important to understand the reality of inter-laboratory differences in metabolomics. Therefore, we have evaluated whether the differences in analytical methods, with the exception sample pretreatment and including metabolite extraction, are involved in the inter-laboratory differences or not. In this study, nine facilities are evaluated for inter-laboratory comparisons of metabolomic analysis. Identical dried samples prepared from human and mouse plasma are distributed to each laboratory, and the metabolites are measured without the pretreatment that is unique to each laboratory. In these measurements, hydrophilic and hydrophobic metabolites are analyzed using 11 and 7 analytical methods, respectively. The metabolomic data acquired at each laboratory are integrated, and the differences in the metabolomic data from the laboratories are evaluated. No substantial difference in the relative quantitative data (human/mouse) for a little less than 50% of the detected metabolites is observed, and the hydrophilic metabolites have fewer differences between the laboratories compared with hydrophobic metabolites. From evaluating selected quantitatively guaranteed metabolites, the proportion of metabolites without the inter-laboratory differences is observed to be slightly high. It is difficult to resolve the inter-laboratory differences in metabolomics because all laboratories cannot prepare the same analytical environments. However, the results from this study indicate that the inter-laboratory differences in metabolomic data are due to measurement and data analysis rather than sample preparation, which will facilitate the understanding of the problems in metabolomics studies involving multiple laboratories.
    Keywords:  hydrophilic metabolite; hydrophobic metabolite; inter-laboratory comparison; mass spectrometry; metabolomics; relative quantification
    DOI:  https://doi.org/10.3390/metabo12020135
  15. Antioxidants (Basel). 2022 Jan 25. pii: 229. [Epub ahead of print]11(2):
    Simple and Sensitive Method for the Quantitative Determination of Lipid Hydroperoxides by Liquid Chromatography/Mass Spectrometry.
    Chongsheng Liang, Siddabasave Gowda B Gowda, Divyavani Gowda, Toshihiro Sakurai, Iku Sazaki, Hitoshi Chiba, Shu-Ping Hui.
      Lipid hydroperoxides (LOOH) are the initial products of the peroxidation of unsaturated lipids and play a crucial role in lipid oxidation due to their ability to decompose into free radicals and cause adverse effects on human health. Thus, LOOHs are commonly considered biomarkers of oxidative stress-associated pathological conditions. Despite their importance, the sensitive and selective analytical method for determination is limited, due to their low abundance, poor stability, and low ionizing efficiency. To overcome these limitations, in this study, we chemically synthesized eight fatty acid hydroperoxides (FAOOH), including FA 18:1-OOH, FA 18:2-OOH, FA 18:3-OOH, FA 20:4-OOH, FA 20:5-OOH, FA 22:1-OOH, FA 22:6-OOH as analytes, and FA 19:1-OOH as internal standard. Then, they were chemically labeled with 2-methoxypropene (2-MxP) to obtain FAOOMxP by one-step derivatization (for 10 min). A selected reaction monitoring assisted targeted analytical method was developed using liquid chromatography/tandem mass spectrometry (LC-MS/MS). The MxP-labelling improved the stability and enhanced the ionization efficiency in positive mode. Application of reverse-phase chromatography allowed coelution of analytes and internal standards with a short analysis time of 6 min. The limit of detection and quantification for FAOOH ranged from 0.1-1 pmol/µL and 1-2.5 pmol/µL, respectively. The method was applied to profile total FAOOHs in chemically oxidized human serum samples (n = 5) and their fractions of low and high-density lipoproteins (n = 4). The linoleic acid hydroperoxide (FA 18:2-OOH) and oleic acid hydroperoxide (FA 18:1-OOH) were the most abundant FAOOHs in human serum and lipoproteins. Overall, our validated LC-MS/MS methodology features enhanced detection and rapid separation that enables facile quantitation of multiple FAOOHs, therefore providing a valuable tool for determining the level of lipid peroxidation with potential diagnostic applications.
    Keywords:  2-methoxypropene; chemical derivatization; human serum; lipid hydroperoxide; lipoprotein oxidation; liquid chromatography; mass spectrometry; unsaturated fatty acids
    DOI:  https://doi.org/10.3390/antiox11020229
  16. PeerJ. 2022 ;10 e12918
    Nontargeted and targeted metabolomics approaches reveal the key amino acid alterations involved in multiple myeloma.
    Lingling Yue, Pengyun Zeng, Yanhong Li, Ye Chai, Chongyang Wu, Bingren Gao.
      Purpose: Multiple myeloma (MM), a kind of malignant neoplasm of clonal plasma cells in the bone marrow, is a refractory disease. Understanding the metabolism disorders and identification of metabolomics pathways as well as key metabolites will provide new insights for exploring diagnosis and therapeutic targets of MM.Methods: We conducted nontargeted metabolomics analysis of MM patients and normal controls (NC) using ultra-high-performance liquid chromatography (UHPLC) combined with quadrupole time-of-flight mass spectrometry (Q-TOF-MS) in 40 cases of cohort 1 subjects. The targeted metabolomics analysis of amino acids using multiple reaction monitoring-mass spectrometry (MRM-MS) was also performed in 30 cases of cohort 1 and 30 cases of cohort 2 participants, to comprehensively investigate the metabolomics disorders of MM.
    Results: The nontargeted metabolomics analysis in cohort 1 indicated that there was a significant metabolic signature change between MM patients and NC. The differential metabolites were mainly enriched in metabolic pathways related to amino acid metabolism, such as protein digestion and absorption, and biosynthesis of amino acids. Further, the targeted metabolomics analysis of amino acids in both cohort 1 and cohort 2 revealed differential metabolic profiling between MM patients and NC. We identified 12 and 14 amino acid metabolites with altered abundance in MM patients compared to NC subjects, in cohort 1 and cohort 2, respectively. Besides, key differential amino acid metabolites, such as choline, creatinine, leucine, tryptophan, and valine, may discriminate MM patients from NC. Moreover, the differential amino acid metabolites were associated with clinical indicators of MM patients.
    Conclusions: Our findings indicate that amino acid metabolism disorders are involved in MM. The differential profiles reveal the potential utility of key amino acid metabolites as diagnostic biomarkers of MM. The alterations in metabolome, especially the amino acid metabolome, may provide more evidences for elucidating the pathogenesis and development of MM.
    Keywords:  Diagnostic biomarkers; Differential amino acid metabolites; Metabolic pathways; Metabolomics profiles; Multiple myeloma
    DOI:  https://doi.org/10.7717/peerj.12918
  17. Nutrients. 2022 Feb 18. pii: 851. [Epub ahead of print]14(4):
    Lipid Metabolism and Epigenetics Crosstalk in Prostate Cancer.
    Juan C Pardo, Vicenç Ruiz de Porras, Joan Gil, Albert Font, Manel Puig-Domingo, Mireia Jordà.
      Prostate cancer (PCa) is the most commonly diagnosed malignant neoplasm in men in the Western world. Localized low-risk PCa has an excellent prognosis thanks to effective local treatments; however, despite the incorporation of new therapeutic strategies, metastatic PCa remains incurable mainly due to disease heterogeneity and the development of resistance to therapy. The mechanisms underlying PCa progression and therapy resistance are multiple and include metabolic reprogramming, especially in relation to lipid metabolism, as well as epigenetic remodelling, both of which enable cancer cells to adapt to dynamic changes in the tumour. Interestingly, metabolism and epigenetics are interconnected. Metabolism can regulate epigenetics through the direct influence of metabolites on epigenetic processes, while epigenetics can control metabolism by directly or indirectly regulating the expression of metabolic genes. Moreover, epidemiological studies suggest an association between a high-fat diet, which can alter the availability of metabolites, and PCa progression. Here, we review the alterations of lipid metabolism and epigenetics in PCa, before focusing on the mechanisms that connect them. We also discuss the influence of diet in this scenario. This information may help to identify prognostic and predictive biomarkers as well as targetable vulnerabilities.
    Keywords:  DNA methylation; cholesterol; diet; epigenetics; fatty acid; histone modifications; lipid metabolism; predictive biomarkers; prostate cancer; therapeutic vulnerabilities
    DOI:  https://doi.org/10.3390/nu14040851
  18. Metabolites. 2022 Feb 09. pii: 165. [Epub ahead of print]12(2):
    Systematic Evaluation of HILIC Stationary Phases for Global Metabolomics of Human Plasma.
    Farideh Hosseinkhani, Luojiao Huang, Anne-Charlotte Dubbelman, Faisa Guled, Amy C Harms, Thomas Hankemeier.
      Polar hydrophilic metabolites have been identified as important actors in many biochemical pathways. Despite continuous improvement and refinement of hydrophilic interaction liquid chromatography (HILIC) platforms, its application in global polar metabolomics has been underutilized. In this study, we aimed to systematically evaluate polar stationary phases for untargeted metabolomics by using HILIC columns (neutral and zwitterionic) that have been exploited widely in targeted approaches. To do so, high-resolution mass spectrometry was applied to thoroughly investigate selectivity, repeatability and matrix effect at three pH conditions for 9 classes of polar compounds using 54 authentic standards and plasma matrix. The column performance for utilization in untargeted metabolomics was assessed using plasma samples with diverse phenotypes. Our results indicate that the ZIC-c HILIC column operated at neutral pH exhibited several advantages, including superior performance for different classes of compounds, better isomer separation, repeatability and high metabolic coverage. Regardless of the column type, the retention of inorganic ions in plasma leads to extensive adduct formation and co-elution with analytes, which results in ion-suppression as part of the overall plasma matrix effect. In ZIC-c HILIC, the sodium chloride ion effect was particularly observed for amino acids and amine classes. Successful performance of HILIC for separation of plasma samples with different phenotypes highlights this mode of separation as a valuable approach in global profiling of plasma sample and discovering the metabolic changes associated with health and disease.
    Keywords:  HILIC; liquid chromatography; plasma metabolomics; polar metabolites; untargeted metabolomics
    DOI:  https://doi.org/10.3390/metabo12020165
  19. Int J Oncol. 2022 Apr;pii: 37. [Epub ahead of print]60(4):
    HepaCAM‑PIK3CA axis regulates the reprogramming of glutamine metabolism to inhibit prostate cancer cell proliferation.
    Zhenting He, Yingying Gao, Ting Li, Chaowen Yu, Liping Ou, Chunli Luo.
      Energy metabolism reprogramming is becoming an increasingly important hallmark of cancer. Specifically, cancers tend to undergo metabolic reprogramming to upregulate a cell‑dependent glutamine (Gln) metabolism. Notably, hepatocellular cell adhesion molecule (HepaCAM) has been previously reported to serve a key role as a tumour suppressor. However, the possible regulatory role of HepaCAM in Gln metabolism in prostate cancer (PCa) remains poorly understood. In the present study, bioinformatics analysis predicted a significant negative correlation among the expression of HepaCAM, phosphatidylinositol‑4,5‑bisphosphate 3‑kinase catalytic subunit α (PIK3CA), glutaminase (GLS) and solute carrier family 1 member 5 (SLC1A5), components of Gln metabolism, in clinical and genomic datasets. Immunohistochemistry results verified a negative correlation between HepaCAM and PIK3CA expression in PCa tissues. Subsequently, liquid chromatography‑tandem mass spectrometry (LC‑MS/MS) and gas chromatography‑mass spectrometry (GC‑MS) assays were performed, and the results revealed markedly reduced levels of Gln and metabolic flux in the blood samples of patients with PCa and in PCa cells. Mechanistically, overexpression of HepaCAM inhibited Gln metabolism and proliferation by regulating PIK3CA in PCa cells. In addition, Gln metabolism was discovered to be stress‑resistant in PCa cells, since the expression levels of GLS and SLC1A5 remained high for a period of time after Gln starvation. However, overexpression of HepaCAM reversed this resistance to some extent. Additionally, alpelisib, a specific inhibitor of PIK3CA, effectively potentiated the inhibitory effects of HepaCAM overexpression on Gln metabolism and cell proliferation through mass spectrometry and CCK‑8 experiments. In addition, the inhibitory effect of PIK3CA on the growth of tumor tissue in nude mice was also confirmed by immunohistochemistry in vivo. To conclude, the results from the present study revealed an abnormal Gln metabolic profile in the blood samples of patients with PCa, suggesting that it can be applied as a clinical diagnostic tool for PCa. Additionally, a key role of the HepaCAM/PIK3CA axis in regulating Gln metabolism, cell proliferation and tumour growth was identified. The combination of alpelisib treatment with the upregulation of HepaCAM expression may serve as a novel method for treating patients with PCa.
    Keywords:  5‑bisphosphate 3‑kinase catalytic subunit α; glutamine metabolic reprogramming; hepatocellular cell adhesion molecule; phosphatidyl­inositol‑4; proliferation; prostate cancer
    DOI:  https://doi.org/10.3892/ijo.2022.5327
  20. JCI Insight. 2022 Feb 22. pii: e158037. [Epub ahead of print]
    Acute aerobic exercise reveals FAHFAs distinguish the metabolomes of overweight and normal weight runners.
    Alisa B Nelson, Lisa S Chow, David B Stagg, Jacob R Gillingham, Michael D Evans, Meixia Pan, Curtis C Hughey, Chad L Myers, Xianlin Han, Peter A Crawford, Patrycja Puchalska.
      BACKGROUND: Responses of the metabolome to acute aerobic exercise may predict VO2max and longer-term outcomes, including the development of diabetes and its complications.METHODS: Serum samples were collected from overweight trained (OWT) and normal weight trained (NWT) runners prior to and immediately after a supervised 90-minute treadmill run at 60% VO2max (NWT = 14, OWT = 11) in a cross-sectional study. We applied a liquid chromatography high resolution-mass spectrometry based untargeted metabolomics platform to evaluate the effect of acute aerobic exercise on the serum metabolome.
    RESULTS: NWT and OWT metabolic profiles shared increased circulating acylcarnitines and free fatty acids (FFAs) with exercise while intermediates of adenine metabolism, inosine and hypoxanthine, were strongly correlated with body fat percentage and VO2max. Untargeted metabolomics-guided follow-up quantitative lipidomic analysis revealed that baseline levels of fatty acid esters of hydroxy fatty acids (FAHFAs) were generally diminished in the OWT group. FAHFAs negatively correlated with visceral fat mass and HOMA-IR. Strikingly, a 4-fold decrease in FAHFAs was provoked by acute aerobic running in NWT, an effect that negatively correlated with circulating IL-6, neither of which was observed in the OWT group. Machine learning models based on a pre-exercise metabolite profile that included FAHFAs, FFAs, and adenine intermediates predicted VO2max.
    CONCLUSION: These findings in overweight human participants and healthy controls indicate that exercise-provoked changes in FAHFAs distinguish normal weight from overweight individuals and could predict VO2max. These results support the notion that FAHFAs could modulate the inflammatory response, fuel utilization, and insulin resistance.
    TRIAL REGISTRATION: ClinicalTrials.gov NCT02150889.
    FUNDING: NIH DK091538, AG069781, DK098203, TR000114, UL1TR002494.
    Keywords:  Adipose tissue; Diabetes; Metabolism; Obesity
    DOI:  https://doi.org/10.1172/jci.insight.158037
  21. Methods Mol Biol. 2022 ;2455 41-48
    Measurement of Hepatic Lipids.
    Sarah Forman, Patrick Tso, Min Liu.
      Liver plays a central role in lipid metabolism, uptake of lipoproteins and lipids from the circulation (e.g., chylomicron remnant), and secretions of very low-density lipoproteins (VLDL). Therefore, measurements of lipid levels in the liver have been broadly used to check hepatic function, especially in subjects who have chronic liver diseases, such as nonalcoholic steatohepatitis (NASH), in which there is accumulation of fat, inflammation, and damage to liver cells. In this chapter, we describe the processes of extracting hepatic lipids by the method of Folch et al., and measuring the levels of cholesterol, triglycerides, phospholipids, and non-esterified fatty acids using enzymatic assays.
    Keywords:  Enzymatic assays; Folch method; Lipid analysis; Lipid extraction
    DOI:  https://doi.org/10.1007/978-1-0716-2128-8_4
  22. Metabolites. 2022 Feb 19. pii: 194. [Epub ahead of print]12(2):
    The Potential of Metabolomics in Biomedical Applications.
    Vanessa Gonzalez-Covarrubias, Eduardo Martínez-Martínez, Laura Del Bosque-Plata.
      The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.
    Keywords:  diabetes; exposome; extracellular vesicles; longevity; metabolomics; neurodegenerative diseases; obesity; pharmacometabolomics
    DOI:  https://doi.org/10.3390/metabo12020194
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