bims-mascan 2023-04-02 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2023–04–02
39 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Quality Control-A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome.
A Streamlined High-Throughput Plasma Proteomics Platform for Clinical Proteomics with Improved Proteome Coverage, Reproducibility, and Robustness.
Combined targeted and untargeted high-resolution mass spectrometry analyses to investigate metabolic alterations in pompe disease.
Targeted Lipidomics Analysis of Adipose and Skeletal Muscle Tissues by Multiple Reaction Monitoring Profiling.
Systematic untargeted UHPLC-Q-TOF-MS based lipidomics workflow for improved detection and annotation of lipid sub-classes in serum.
Mass Spectrometry Imaging for Single-Cell or Subcellular Lipidomics: A Review of Recent Advancements and Future Development.
Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry.
Amino acid metabolic reprogramming in tumor metastatic colonization.
A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics.
Dynamic regulation of ferroptosis by lipid metabolism.
Metabolic Reprogramming and Potential Therapeutic Targets in Lymphoma.
Sensitive, high-throughput HLA-I and HLA-II immunopeptidomics using parallel accumulation-serial fragmentation mass spectrometry.
Bioenergetic Profiling in Glioblastoma Multiforme Patients with Different Clinical Outcomes.
Untargeted Metabolomic Characterization of Glioblastoma Intra-Tumor Heterogeneity Using OrbiSIMS.
Mass Spectrometry Contribution to Pediatric Cancers Research.
Metabolic codependencies in the tumor microenvironment and gastric cancer: Difficulties and opportunities.
MS2Query: reliable and scalable MS2 mass spectra-based analogue search.
Reliable quantification of citrate isomers and isobars with direct-infusion tandem mass spectrometry.
Metabolic reprogramming in cancer: Mechanisms and therapeutics.
Data Independent Acquisition Reveals In-Depth Serum Proteome Changes in Canine Leishmaniosis.
Glutamine Starvation Affects Cell Cycle, Oxidative Homeostasis and Metabolism in Colorectal Cancer Cells.
MAD HATTER Correctly Annotates 98% of Small Molecule Tandem Mass Spectra Searching in PubChem.
Life-Threatening Cardiogenic Shock Related to Venlafaxine Poisoning-A Case Report with Metabolomic Approach.
Identifying Hair Biomarker Candidates for Alzheimer's Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies.
Proteomic profiling of colorectal adenomas identifies a predictive risk signature for development of metachronous advanced colorectal neoplasia.
Optimizing XCMS parameters for GC-MS metabolomics data processing: a case study.
Bottom-Up Proteomics: Advancements in Sample Preparation.
Oridonin inhibits Hela cell proliferation via downregulation of glutathione metabolism: a new insight from metabolomics.
The Role of Reprogrammed Glucose Metabolism in Cancer.
Coupling microdroplet-based sample preparation, multiplexed isobaric labeling, and nanoflow peptide fractionation for deep proteome profiling of tissue microenvironment.
Metabolomic Studies in Inborn Errors of Metabolism: Last Years and Future Perspectives.
Matrix Effects in GC-MS Profiling of Common Metabolites after Trimethylsilyl Derivatization.
Metabolomics Identifies a Panel of Diagnostic Biomarkers for Early Human Embryonic Development Arrest.
Development of an Untargeted Metabolomics Strategy to Study the Metabolic Rewiring of Dendritic Cells upon Lipopolysaccharide Activation.
Cross-Platform Comparison of Amino Acid Metabolic Profiling in Three Model Organisms Used in Environmental Metabolomics.
The origin of long-chain fatty acids required for de novo ether lipid/plasmalogen synthesis.
Sustained IP3-linked Ca2+ signaling promotes progression of triple negative breast cancer cells by regulating fatty acid metabolism.
Quantitative Proteomics for the Identification of Differentially Expressed Proteins in the Extracellular Vesicles of Cervical Cancer Cells.
Mass spectrometry-based proteomics approaches to interrogate skeletal muscle adaptations to exercise.

Biomolecules. 2023 03 07. pii: 491. [Epub ahead of print]13(3):

Quality Control-A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome.

Svitlana Rozanova, Julian Uszkoreit, Karin Schork, Bettina Serschnitzki, Martin Eisenacher, Lars Tönges, Katalin Barkovits-Boeddinghaus, Katrin Marcus.

  Proteomic studies using mass spectrometry (MS)-based quantification are a main approach to the discovery of new biomarkers. However, a number of analytical conditions in front and during MS data acquisition can affect the accuracy of the obtained outcome. Therefore, comprehensive quality assessment of the acquired data plays a central role in quantitative proteomics, though, due to the immense complexity of MS data, it is often neglected. Here, we address practically the quality assessment of quantitative MS data, describing key steps for the evaluation, including the levels of raw data, identification and quantification. With this, four independent datasets from cerebrospinal fluid, an important biofluid for neurodegenerative disease biomarker studies, were assessed, demonstrating that sample processing-based differences are already reflected at all three levels but with varying impacts on the quality of the quantitative data. Specifically, we provide guidance to critically interpret the quality of MS data for quantitative proteomics. Moreover, we provide the free and open source quality control tool MaCProQC, enabling systematic, rapid and uncomplicated data comparison of raw data, identification and feature detection levels through defined quality metrics and a step-by-step quality control workflow.

Keywords:  FASP; cerebrospinal fluid; in-solution digestion; label-free quantification; mass spectrometry; proteomics; quality control

DOI:  https://doi.org/10.3390/biom13030491
J Am Soc Mass Spectrom. 2023 Mar 28.

A Streamlined High-Throughput Plasma Proteomics Platform for Clinical Proteomics with Improved Proteome Coverage, Reproducibility, and Robustness.

Jongmin Woo, Qibin Zhang.

  Mass spectrometry-based clinical proteomics requires high throughput, reproducibility, robustness, and comprehensive coverage to serve the needs of clinical diagnosis, prognosis, and personalized medicine. Oftentimes these requirements are contradictory to each other. We report the development of a streamlined High-Throughput Plasma Proteomics (sHTPP) platform for untargeted profiling of the blood plasma proteome, which includes 96-well plates and simplified procedures for sample preparation, disposable trap column for peptide loading, robust liquid chromatographic system for separation, data-independent acquisition in tandem mass spectrometry, and DIA-NN, FragPipe, and in-house peptide spectral library-based data analysis. Using the optimized platform at a throughput of 60 samples per day, over 600 protein groups including 57 FDA-approved biomarkers can be consistently identified from whole human plasma, and more than 85% of the detected proteins have 100% completeness in quantitative values across 300 samples. The balance achieved between proteome coverage, throughput, and reproducibility of this sHTPP platform makes it promising in clinical settings, where a large number of samples are to be measured quickly and reliably to support various needs of clinical medicine.

Keywords:  DIA; clinical proteomics; high throughput; human plasma proteome

DOI:  https://doi.org/10.1021/jasms.3c00022
Metabolomics. 2023 Mar 29. 19(4): 29

Combined targeted and untargeted high-resolution mass spectrometry analyses to investigate metabolic alterations in pompe disease.

Mariana B M de Moraes, Hygor M R de Souza, Maria L C de Oliveira, Roy W A Peake, Fernanda B Scalco, Rafael Garrett.

   INTRODUCTION: Pompe disease is a rare, lysosomal disorder, characterized by intra-lysosomal glycogen accumulation due to an impaired function of α-glucosidase enzyme. The laboratory testing for Pompe is usually performed by enzyme activity, genetic test, or urine glucose tetrasaccharide (Glc4) screening by HPLC. Despite being a good preliminary marker, the Glc4 is not specific for Pompe.
OBJECTIVE: The purpose of the present study was to develop a simple methodology using liquid chromatography-high resolution mass spectrometry (LC-HRMS) for targeted quantitative analysis of Glc4 combined with untargeted metabolic profiling in a single analytical run to search for complementary biomarkers in Pompe disease.
METHODS: We collected 21 urine specimens from 13 Pompe disease patients and compared their metabolic signatures with 21 control specimens.
RESULTS: Multivariate statistical analyses on the untargeted profiling data revealed Glc4, creatine, sorbitol/mannitol, L-phenylalanine, N-acetyl-4-aminobutanal, N-acetyl-L-aspartic acid, and 2-aminobenzoic acid as significantly altered in Pompe disease. This panel of metabolites increased sample class prediction (Pompe disease versus control) compared with a single biomarker.
CONCLUSION: This study has demonstrated the potential of combined acquisition methods in LC-HRMS for Pompe disease investigation, allowing for routine determination of an established biomarker and discovery of complementary candidate biomarkers that may increase diagnostic accuracy, or improve the risk stratification of patients with disparate clinical phenotypes.

Keywords:  Glycogen storage disorder; High-resolution mass spectrometry; Inborn error of metabolism; Metabolomics; Urine

DOI:  https://doi.org/10.1007/s11306-023-01989-w
Methods Mol Biol. 2023 ;2640 351-368

Targeted Lipidomics Analysis of Adipose and Skeletal Muscle Tissues by Multiple Reaction Monitoring Profiling.

Xiyue Chen, Christina R Ferreira, Shihuan Kuang.

  Lipid homeostasis is critical for maintaining normal cellular functions including membrane structural integrity, cell metabolism, and signal transduction. Adipose tissue and skeletal muscle are two major tissues involved in lipid metabolism. Adipose tissue can store excessive lipids in the form of triacylglyceride (TG), which can be hydrolyzed to release free fatty acids (FFAs) under insufficient nutrition states. In the highly energy-demanding skeletal muscle, lipids serve as oxidative substrates for energy production but can cause muscle dysfunction when overloaded. Lipids undergo fascinating cycles of biogenesis and degradation depending on physiological demands, while dysregulation of lipid metabolism has been increasingly recognized as a hallmark of diseases such as obesity and insulin resistance. Thus, it is important to understand the diversity and dynamics of lipid composition in adipose tissue and skeletal muscle. Here, we describe the use of multiple reaction monitoring profiling, based on lipid class and fatty acyl chain specific fragmentation, to explore various classes of lipids in skeletal muscle and adipose tissues. We provide a detailed method for exploratory analysis of acylcarnitine (AC), ceramide (Cer), cholesteryl ester (CE), diacylglyceride (DG), FFA, phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphatidylserine (PS), sphingomyelin (SM), and TG. Characterization of lipid composition within adipose tissue and skeletal muscle under different physiological situations will provide biomarkers and therapeutic targets for obesity-related diseases.

Keywords:  Fatty acid; Lipidome; Mass spectrometry; Metabolism; Multiple reaction monitoring profiling; Obesity; Triacylglyceride/Triacylglycerol/triglyceride

DOI:  https://doi.org/10.1007/978-1-0716-3036-5_25
Metabolomics. 2023 Mar 27. 19(4): 24

Systematic untargeted UHPLC-Q-TOF-MS based lipidomics workflow for improved detection and annotation of lipid sub-classes in serum.

Seema Dhariwal, Kiran Maan, Ruchi Baghel, Apoorva Sharma, Dipankar Malakar, Poonam Rana.

   INTRODUCTION AND OBJECTIVE: Taking into consideration the challenges of lipid analytics, present study aims to design the best high-throughput workflow for detection and annotation of lipids.
MATERIAL AND METHODS: Serum lipid profiling was performed on CSH-C18 and EVO-C18 columns using UHPLC Q-TOF-MS and generated lipid features were annotated based on m/z and fragment ion using different software.
RESULT AND DISCUSSION: Better detection of features was observed in CSH-C18 than EVO-C18 with enhanced resolution except for Glycerolipids (triacylglycerols) and Sphingolipids (sphingomyelin).
CONCLUSION: The study revealed an optimized untargeted Lipidomics-workflow with comprehensive lipid profiling (CSH-C18 column) and confirmatory annotation (LipidBlast).

Keywords:  LIPID MAPS®; LipidBlast; LipidView™; Lipidomics; Q-TOF; UHPLC–MS

DOI:  https://doi.org/10.1007/s11306-023-01983-2
Molecules. 2023 Mar 17. pii: 2712. [Epub ahead of print]28(6):

Mass Spectrometry Imaging for Single-Cell or Subcellular Lipidomics: A Review of Recent Advancements and Future Development.

Dan Li, Zheng Ouyang, Xiaoxiao Ma.

  Mass Spectrometry Imaging (MSI) has emerged as a powerful imaging technique for the analysis of biological samples, providing valuable insights into the spatial distribution and structural characterization of lipids. The advancements in high-resolution MSI have made it an indispensable tool for single-cell or subcellular lipidomics. By preserving both intracellular and intercellular information, MSI enables a comprehensive analysis of lipidomics in individual cells and organelles. This enables researchers to delve deeper into the diversity of lipids within cells and to understand the role of lipids in shaping cell behavior. In this review, we aim to provide a comprehensive overview of recent advancements and future prospects of MSI for cellular/subcellular lipidomics. By keeping abreast of the cutting-edge studies in this field, we will continue to push the boundaries of the understanding of lipid metabolism and the impact of lipids on cellular behavior.

Keywords:  data analysis; deep learning; lipidomics; mass spectrometry imaging; matrix-assisted laser desorption ionization (MALDI); multimodal imaging; organelle; secondary ion mass spectrometry (SIMS); single-cell

DOI:  https://doi.org/10.3390/molecules28062712
J Proteome Res. 2023 Mar 29.

Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry.

Patrick Truong, Matthew The, Lukas Käll.

  A frequent goal, or subgoal, when processing data from a quantitative shotgun proteomics experiment is a list of proteins that are differentially abundant under the examined experimental conditions. Unfortunately, obtaining such a list is a challenging process, as the mass spectrometer analyzes the proteolytic peptides of a protein rather than the proteins themselves. We have previously designed a Bayesian hierarchical probabilistic model, Triqler, for combining peptide identification and quantification errors into probabilities of proteins being differentially abundant. However, the model was developed for data from data-dependent acquisition. Here, we show that Triqler is also compatible with data-independent acquisition data after applying minor alterations for the missing value distribution. Furthermore, we find that it has better performance than a set of compared state-of-the-art protein summarization tools when evaluated on data-independent acquisition data.

Keywords:  Bayesian hierarchical modelling; benchmark; data-independent acquisition mass spectrometry,; label-free quantification; mass spectrometry; mathematical methods; protein summarization

DOI:  https://doi.org/10.1021/acs.jproteome.2c00607
Front Oncol. 2023 ;13 1123192

Amino acid metabolic reprogramming in tumor metastatic colonization.

Zihao Wang, Xingyun Wu, Hai-Ning Chen, Kui Wang.

  Metastasis is considered as the major cause of cancer death. Cancer cells can be released from primary tumors into the circulation and then colonize in distant organs. How cancer cells acquire the ability to colonize in distant organs has always been the focus of tumor biology. To enable survival and growth in the new environment, metastases commonly reprogram their metabolic states and therefore display different metabolic properties and preferences compared with the primary lesions. For different microenvironments in various colonization sites, cancer cells must transfer to specific metabolic states to colonize in different distant organs, which provides the possibility of evaluating metastasis tendency by tumor metabolic states. Amino acids provide crucial precursors for many biosynthesis and play an essential role in cancer metastasis. Evidence has proved the hyperactivation of several amino acid biosynthetic pathways in metastatic cancer cells, including glutamine, serine, glycine, branched chain amino acids (BCAAs), proline, and asparagine metabolism. The reprogramming of amino acid metabolism can orchestrate energy supply, redox homeostasis, and other metabolism-associated pathways during cancer metastasis. Here, we review the role and function of amino acid metabolic reprogramming in cancer cells colonizing in common metastatic organs, including lung, liver, brain, peritoneum, and bone. In addition, we summarize the current biomarker identification and drug development of cancer metastasis under the amino acid metabolism reprogramming, and discuss the possibility and prospect of targeting organ-specific metastasis for cancer treatment.

Keywords:  amino acid metabolism; cancer metastasis; distant organ colonization; metabolic reprogramming; metabolic targeting

DOI:  https://doi.org/10.3389/fonc.2023.1123192
Nat Commun. 2023 Mar 31. 14(1): 1813

A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics.

Mingdu Luo, Yandong Yin, Zhiwei Zhou, Haosong Zhang, Xi Chen, Hongmiao Wang, Zheng-Jiang Zhu.

Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.

DOI: https://doi.org/10.1038/s41467-023-37539-0
Antioxid Redox Signal. 2023 Mar 27.

Dynamic regulation of ferroptosis by lipid metabolism.

Yilong Zou, Nathchar Naowarojna, Tianshu William Wu, Zijian Pan, Mengyu Li, Jingrong Regina Han.

SIGNIFICANCE: Ferroptosis is featured by the accumulation of polyunsaturated-lipid peroxidation on cellular membranes in an iron-dependent manner. Ferroptosis has been implicated in various pathophysiological processes including cancer, neurodegeneration, and ischemia-reperfusion tissue injury. However, our understanding about the dynamic and context-specific regulation of ferroptosis remains incomplete.
RECENT ADVANCES: As the major substrate for peroxidation, the cellular lipidome regulates ferroptosis sensitivity and execution by controlling the abundance and availability of polyunsaturated-lipids for peroxidative modifications. In turn, the cellular lipidome is regulated by a complex network of enzymes and transporters, as well as upstream layers of receptors, kinases and transcription factors. A number of research has shed light on the link between lipid metabolism and ferroptosis. Here we summarize our current knowledge on the role of the lipidome and associated protein regulators in various stages of ferroptosis, ranging from initiation, execution to cell death evasion by cells experiencing ferroptotic stress.
CRITICAL ISSUES: This review provides an overview of the mechanisms underlying lipid peroxidation and ferroptosis by discussing the lipid species that directly contribute to lipid peroxidation and ferroptosis, how cells regulate the abundances of these pro-ferroptosis lipids, how lipid peroxidation causes cell death, and how cells prevent and repair membrane lipid damage under ferroptotic conditions.
FUTURE DIRECTIONS: Cell fate regulation in vivo could be different from in vitro culture settings. We envision that a comprehensive and detailed understanding about these important questions in the dynamic regulation of ferroptosis in vivo will accelerate our development of ferroptosis-targeted therapies to improve human health.

DOI: https://doi.org/10.1089/ars.2023.0278
Int J Mol Sci. 2023 Mar 13. pii: 5493. [Epub ahead of print]24(6):

Metabolic Reprogramming and Potential Therapeutic Targets in Lymphoma.

Yuyang Pang, Tingxun Lu, Zijun Y Xu-Monette, Ken H Young.

  Lymphoma is a heterogeneous group of diseases that often require their metabolism program to fulfill the demand of cell proliferation. Features of metabolism in lymphoma cells include high glucose uptake, deregulated expression of enzymes related to glycolysis, dual capacity for glycolytic and oxidative metabolism, elevated glutamine metabolism, and fatty acid synthesis. These aberrant metabolic changes lead to tumorigenesis, disease progression, and resistance to lymphoma chemotherapy. This metabolic reprogramming, including glucose, nucleic acid, fatty acid, and amino acid metabolism, is a dynamic process caused not only by genetic and epigenetic changes, but also by changes in the microenvironment affected by viral infections. Notably, some critical metabolic enzymes and metabolites may play vital roles in lymphomagenesis and progression. Recent studies have uncovered that metabolic pathways might have clinical impacts on the diagnosis, characterization, and treatment of lymphoma subtypes. However, determining the clinical relevance of biomarkers and therapeutic targets related to lymphoma metabolism is still challenging. In this review, we systematically summarize current studies on metabolism reprogramming in lymphoma, and we mainly focus on disorders of glucose, amino acids, and lipid metabolisms, as well as dysregulation of molecules in metabolic pathways, oncometabolites, and potential metabolic biomarkers. We then discuss strategies directly or indirectly for those potential therapeutic targets. Finally, we prospect the future directions of lymphoma treatment on metabolic reprogramming.

Keywords:  lymphoma; metabolism; targeted therapy

DOI:  https://doi.org/10.3390/ijms24065493
bioRxiv. 2023 Mar 18. pii: 2023.03.10.532106. [Epub ahead of print]

Sensitive, high-throughput HLA-I and HLA-II immunopeptidomics using parallel accumulation-serial fragmentation mass spectrometry.

Kshiti Meera Phulphagar, Claudia Ctortecka, Alvaro Sebastian Vaca Jacome, Susan Klaeger, Eva K Verzani, Gabrielle M Hernandez, Namrata Udeshi, Karl Clauser, Jennifer Abelin, Steven A Carr.

Comprehensive, in-depth identification of the human leukocyte antigen HLA-I and HLA-II tumor immunopeptidome can inform the development of cancer immunotherapies. Mass spectrometry (MS) is powerful technology for direct identification of HLA peptides from patient derived tumor samples or cell lines. However, achieving sufficient coverage to detect rare, clinically relevant antigens requires highly sensitive MS-based acquisition methods and large amounts of sample. While immunopeptidome depth can be increased by off-line fractionation prior to MS, its use is impractical when analyzing limited amounts of primary tissue biopsies. To address this challenge, we developed and applied a high throughput, sensitive, single-shot MS-based immunopeptidomics workflow that leverages trapped ion mobility time-of-flight mass spectrometry on the Bruker timsTOF SCP. We demonstrate >2-fold improved coverage of HLA immunopeptidomes relative to prior methods with up to 15,000 distinct HLA-I and HLA-II peptides from 4e7 cells. Our optimized single-shot MS acquisition method on the timsTOF SCP maintains high coverage, eliminates the need for off-line fractionation and reduces input requirements to as few as 1e6 A375 cells for > 800 distinct HLA-I peptides. This depth is sufficient to identify HLA-I peptides derived from cancer-testis antigen, and novel/unannotated open reading frames. We also apply our optimized single-shot SCP acquisition methods to tumor derived samples, enabling sensitive, high throughput and reproducible immunopeptidome profiling with detection of clinically relevant peptides from less than 4e7 cells or 15 mg wet weight tissue.

DOI: https://doi.org/10.1101/2023.03.10.532106
Metabolites. 2023 Feb 28. pii: 362. [Epub ahead of print]13(3):

Bioenergetic Profiling in Glioblastoma Multiforme Patients with Different Clinical Outcomes.

Vivi Bafiti, Sotiris Ouzounis, Eleni Siapi, Ioanna Maria Grypari, Andreas Theofanopoulos, Vasilios Panagiotopoulos, Vasiliki Zolota, Dimitrios Kardamakis, Theodora Katsila.

  The accumulation of cell biomass is associated with dramatically increased bioenergetic and biosynthetic demand. Metabolic reprogramming, once thought as an epiphenomenon, currently relates to disease progression, also in response to extracellular fate-decisive signals. Glioblastoma multiforme patients often suffer misdiagnosis, short survival time, low quality of life, and poor disease management options. Today, tumor genetic testing and histological analysis guide diagnosis and treatment. We and others appreciate that metabolites complement translational biomarkers and molecular signatures in disease profiling and phenotyping. Herein, we coupled a mixed-methods content analysis to a mass spectrometry-based untargeted metabolomic analysis on plasma samples from glioblastoma multiforme patients to delineate the role of metabolic remodeling in biological plasticity and, hence, disease severity. Following data processing and analysis, we established a bioenergetic profile coordinated by the mitochondrial function and redox state, lipids, and energy substrates. Our findings show that epigenetic modulators are key players in glioblastoma multiforme cell metabolism, in particular when microRNAs are considered. We propose that biological plasticity in glioblastoma multiforme is a mechanism of adaptation and resistance to treatment which is eloquently revealed by bioenergetics.

Keywords:  bioenergetics; drug repurposing; epigenetic modulators; glioblastoma multiforme; metabolic reprogramming; oncometabolism; translational biomarkers; untargeted metabolomics

DOI:  https://doi.org/10.3390/metabo13030362
Anal Chem. 2023 Mar 30.

Untargeted Metabolomic Characterization of Glioblastoma Intra-Tumor Heterogeneity Using OrbiSIMS.

Wenshi He, Max K Edney, Simon M L Paine, Rian L Griffiths, David J Scurr, Ruman Rahman, Dong-Hyun Kim.

Glioblastoma (GBM) is an incurable brain cancer with a median survival of less than two years from diagnosis. The standard treatment of GBM is multimodality therapy comprising surgical resection, radiation, and chemotherapy. However, prognosis remains poor, and there is an urgent need for effective anticancer drugs. Since different regions of a single GBM contain multiple cancer subpopulations ("intra-tumor heterogeneity"), this likely accounts for therapy failure as certain cancer cells can escape from immune surveillance and therapeutic threats. Here, we present metabolomic data generated using the Orbitrap secondary ion mass spectrometry (OrbiSIMS) technique to investigate brain tumor metabolism within its highly heterogeneous tumor microenvironment. Our results demonstrate that an OrbiSIMS-based untargeted metabolomics method was able to discriminate morphologically distinct regions (viable, necrotic, and non-cancerous) within single tumors from formalin-fixed paraffin-embedded tissue archives. Specifically, cancer cells from necrotic regions were separated from viable GBM cells based on a set of metabolites including cytosine, phosphate, purine, xanthine, and 8-hydroxy-7-methylguanine. Moreover, we mapped ubiquitous metabolites across necrotic and viable regions into metabolic pathways, which allowed for the discovery of tryptophan metabolism that was likely essential for GBM cellular survival. In summary, this study first demonstrated the capability of OrbiSIMS for in situ investigation of GBM intra-tumor heterogeneity, and the acquired information can potentially help improve our understanding of cancer metabolism and develop new therapies that can effectively target multiple subpopulations within a tumor.

DOI: https://doi.org/10.1021/acs.analchem.2c05807
Medicina (Kaunas). 2023 Mar 20. pii: 612. [Epub ahead of print]59(3):

Mass Spectrometry Contribution to Pediatric Cancers Research.

Marco Agostini, Pietro Traldi, Mahmoud Hamdan.

  For over four decades, mass spectrometry-based methods have provided a wealth of information relevant to various challenges in the field of cancers research. These challenges included identification and validation of novel biomarkers for various diseases, in particular for various forms of cancer. These biomarkers serve various objectives including monitoring patient response to the various forms of therapy, differentiating subgroups of the same type of cancer, and providing proteomic data to complement datasets generated by genomic, epigenetic, and transcriptomic methods. The same proteomic data can be used to provide prognostic information and could guide scientists and medics to new and innovative targeted therapies The past decade has seen a rapid emergence of epigenetics as a major contributor to carcinogenesis. This development has given a fresh momentum to MS-based proteomics, which demonstrated to be an unrivalled tool for the analyses of protein post-translational modifications associated with chromatin modifications. In particular, high-resolution mass spectrometry has been recently used for systematic quantification of chromatin modifications. Data generated by this approach are central in the search for new therapies for various forms of cancer and will help in attempts to decipher antitumor drug resistance. To appreciate the contribution of mass spectrometry-based proteomics to biomarkers discovery and to our understanding of mechanisms behind the initiation and progression of various forms of cancer, a number of recent investigations are discussed. These investigations also include results provided by two-dimensional gel electrophoresis combined with mass spectrometry.

Keywords:  MS-based proteomics; chromatin modifications; epigenetics; metabolomics; pediatric cancer; pharmacokinetics of anticancer drugs; two-dimensional gel electrophoresis

DOI:  https://doi.org/10.3390/medicina59030612
Biomed Pharmacother. 2023 Mar 27. pii: S0753-3322(23)00389-X. [Epub ahead of print]162 114601

Metabolic codependencies in the tumor microenvironment and gastric cancer: Difficulties and opportunities.

Xihe Zhao, Kai Li, Mengyu Chen, Lei Liu.

  Oncogenesis and the development of tumors affect metabolism throughout the body. Metabolic reprogramming (also known as metabolic remodeling) is a feature of malignant tumors that is driven by oncogenic changes in the cancer cells themselves as well as by cytokines in the tumor microenvironment. These include endothelial cells, matrix fibroblasts, immune cells, and malignant tumor cells. The heterogeneity of mutant clones is affected by the actions of other cells in the tumor and by metabolites and cytokines in the microenvironment. Metabolism can also influence immune cell phenotype and function. Metabolic reprogramming of cancer cells is the result of a convergence of both internal and external signals. The basal metabolic state is maintained by internal signaling, while external signaling fine-tunes the metabolic process based on metabolite availability and cellular needs. This paper reviews the metabolic characteristics of gastric cancer, focusing on the intrinsic and extrinsic mechanisms that drive cancer metabolism in the tumor microenvironment, and interactions between tumor cell metabolic changes and microenvironment metabolic changes. This information will be helpful for the individualized metabolic treatment of gastric cancers.

Keywords:  Codependencies; Gastric cancer; Immunal reprogramming; Metabolism; Microenviroment

DOI:  https://doi.org/10.1016/j.biopha.2023.114601
Nat Commun. 2023 Mar 29. 14(1): 1752

MS2Query: reliable and scalable MS2 mass spectra-based analogue search.

Niek F de Jonge, Joris J R Louwen, Elena Chekmeneva, Stephane Camuzeaux, Femke J Vermeir, Robert S Jansen, Florian Huber, Justin J J van der Hooft.

Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrate improved reliability and scalability. Thereby, MS2Query offers exciting opportunities to further increase the annotation rate of metabolomics profiles of complex metabolite mixtures and to discover new biology.

DOI: https://doi.org/10.1038/s41467-023-37446-4
Talanta. 2023 Mar 23. pii: S0039-9140(23)00228-X. [Epub ahead of print]259 124477

Reliable quantification of citrate isomers and isobars with direct-infusion tandem mass spectrometry.

Qingyu Hu, Yuting Sun, Xiyan Mu, Yulan Wang, Huiru Tang.

  Direct-infusion tandem mass spectrometry (DI-MS/MS) is an excellent tool for large cohort high-throughput quantitative metabolomics, MS imaging and single cell studies but incapable of discriminating isomers/isobars with similar MS spectral features. With experimental and density-functional theory (DFT) approaches, here, we comprehensively investigated the fragmentation pathways and characteristics of differential ion-mobility spectrometry (DMS) for three citrate isomers (citrate, isocitrate, glucaro-1,4-lactone) and an isobar (quinate) co-existing in biological sample such as urine. Results showed that all these compounds gave better MS spectra in negative-ion mode than positive-ion one and had numerous fragment ions under collision-induced dissociation (CID) with sequential losses of H2O and CO2. All observed fragment ions were assignable by combining experimental with DFT calculation results. A DI-DMS-MS/MS method was then developed to simultaneously quantify these four isomers/isobars with m/z 191-87 (CoV, -5.5 V), 191-73 (CoV, -3.5 V), 191-85 (CoV, -29.5 V) and m/z 191-93 (CoV, -41.5 V) for citrate, isocitrate, glucaro-1,4-lactone and quinate, respectively. The low limit-of-quantification was below 5.5 nM whilst accuracy was above 94% for all above compounds. The urinary concentrations of them in human and C57BL/6 mouse samples were further quantified showing clear inter-individual and inter-species level differences with significantly higher levels of isocitrate, glucaro-1,4-lactone and quinate in human urine samples than mouse ones. This provides an approach to understand the detailed fragmentation pathways for organic isomers/isobars and a high-throughput MS strategy to quantify them in complex mixtures for metabolomics, lipidomics, foodomics and exposomics especially when chromatographic separations are not useable.

Keywords:  Citrate isomers; Collision-induced dissociation; Differential ion-mobility spectrometry; Direct-infusion tandem mass spectrometry

DOI:  https://doi.org/10.1016/j.talanta.2023.124477
MedComm (2020). 2023 Apr;4(2): e218

Metabolic reprogramming in cancer: Mechanisms and therapeutics.

Shiqi Nong, Xiaoyue Han, Yu Xiang, Yuran Qian, Yuhao Wei, Tingyue Zhang, Keyue Tian, Kai Shen, Jing Yang, Xuelei Ma.

  Cancer cells characterized by uncontrolled growth and proliferation require altered metabolic processes to maintain this characteristic. Metabolic reprogramming is a process mediated by various factors, including oncogenes, tumor suppressor genes, changes in growth factors, and tumor-host cell interactions, which help to meet the needs of cancer cell anabolism and promote tumor development. Metabolic reprogramming in tumor cells is dynamically variable, depending on the tumor type and microenvironment, and reprogramming involves multiple metabolic pathways. These metabolic pathways have complex mechanisms and involve the coordination of various signaling molecules, proteins, and enzymes, which increases the resistance of tumor cells to traditional antitumor therapies. With the development of cancer therapies, metabolic reprogramming has been recognized as a new therapeutic target for metabolic changes in tumor cells. Therefore, understanding how multiple metabolic pathways in cancer cells change can provide a reference for the development of new therapies for tumor treatment. Here, we systemically reviewed the metabolic changes and their alteration factors, together with the current tumor regulation treatments and other possible treatments that are still under investigation. Continuous efforts are needed to further explore the mechanism of cancer metabolism reprogramming and corresponding metabolic treatments.

Keywords:  cancer metabolism; cancer therapy; glycolysis; metabolic reprogramming

DOI:  https://doi.org/10.1002/mco2.218
Metabolites. 2023 Feb 28. pii: 365. [Epub ahead of print]13(3):

Data Independent Acquisition Reveals In-Depth Serum Proteome Changes in Canine Leishmaniosis.

Franjo Martinković, Marin Popović, Ozren Smolec, Vladimir Mrljak, Peter David Eckersall, Anita Horvatić.

  Comprehensive profiling of serum proteome provides valuable clues of health status and pathophysiological processes, making it the main strategy in biomarker discovery. However, the high dynamic range significantly decreases the number of detectable proteins, obstructing the insights into the underlying biological processes. To circumvent various serum enrichment methods, obtain high-quality proteome wide information using the next-generation proteomic, and study host response in canine leishmaniosis, we applied data-independent acquisition mass spectrometry (DIA-MS) for deep proteomic profiling of clinical samples. The non-depleted serum samples of healthy and naturally Leishmania-infected dogs were analyzed using the label-free 60-min gradient sequential window acquisition of all theoretical mass spectra (SWATH-MS) method. As a result, we identified 554 proteins, 140 of which differed significantly in abundance. Those were included in lipid metabolism, hematological abnormalities, immune response, and oxidative stress, providing valuable information about the complex molecular basis of the clinical and pathological landscape in canine leishmaniosis. Our results show that DIA-MS is a method of choice for understanding complex pathophysiological processes in serum and serum biomarker development.

Keywords:  biomarker discovery; canine; data independent acquisition; leishmaniosis; mass spectrometry; proteomics; serum

DOI:  https://doi.org/10.3390/metabo13030365
Antioxidants (Basel). 2023 Mar 10. pii: 683. [Epub ahead of print]12(3):

Glutamine Starvation Affects Cell Cycle, Oxidative Homeostasis and Metabolism in Colorectal Cancer Cells.

Martina Spada, Cristina Piras, Giulia Diana, Vera Piera Leoni, Daniela Virginia Frau, Gabriele Serreli, Gabriella Simbula, Roberto Loi, Antonio Noto, Federica Murgia, Paola Caria, Luigi Atzori.

  Cancer cells adjust their metabolism to meet energy demands. In particular, glutamine addiction represents a distinctive feature of several types of tumors, including colorectal cancer. In this study, four colorectal cancer cell lines (Caco-2, HCT116, HT29 and SW480) were cultured with or without glutamine. The growth and proliferation rate, colony-forming capacity, apoptosis, cell cycle, redox homeostasis and metabolomic analysis were evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide test (MTT), flow cytometry, high-performance liquid chromatography and gas chromatography/mass spectrometry techniques. The results show that glutamine represents an important metabolite for cell growth and that its deprivation reduces the proliferation of colorectal cancer cells. Glutamine depletion induces cell death and cell cycle arrest in the GO/G1 phase by modulating energy metabolism, the amino acid content and antioxidant defenses. Moreover, the combined glutamine starvation with the glycolysis inhibitor 2-deoxy-D-glucose exerted a stronger cytotoxic effect. This study offers a strong rationale for targeting glutamine metabolism alone or in combination with glucose metabolism to achieve a therapeutic benefit in the treatment of colon cancer.

Keywords:  antioxidant defenses; cancer cell metabolism; colorectal cancer; energetic pathways; glutamine starvation; metabolomics

DOI:  https://doi.org/10.3390/antiox12030683
Metabolites. 2023 Feb 21. pii: 314. [Epub ahead of print]13(3):

MAD HATTER Correctly Annotates 98% of Small Molecule Tandem Mass Spectra Searching in PubChem.

Martin A Hoffmann, Fleming Kretschmer, Marcus Ludwig, Sebastian Böcker.

  Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics usually relies on mass spectrometry, a technology capable of detecting thousands of compounds in a biological sample. Metabolite annotation is executed using tandem mass spectrometry. Spectral library search is far from comprehensive, and numerous compounds remain unannotated. So-called in silico methods allow us to overcome the restrictions of spectral libraries, by searching in much larger molecular structure databases. Yet, after more than a decade of method development, in silico methods still do not reach the correct annotation rates that users would wish for. Here, we present a novel computational method called Mad Hatter for this task. Mad Hatter combines CSI:FingerID results with information from the searched structure database via a metascore. Compound information includes the melting point, and the number of words in the compound description starting with the letter 'u'. We then show that Mad Hatter reaches a stunning 97.6% correct annotations when searching PubChem, one of the largest and most comprehensive molecular structure databases. Unfortunately, Mad Hatter is not a real method. Rather, we developed Mad Hatter solely for the purpose of demonstrating common issues in computational method development and evaluation. We explain what evaluation glitches were necessary for Mad Hatter to reach this annotation level, what is wrong with similar metascores in general, and why metascores may screw up not only method evaluations but also the analysis of biological experiments. This paper may serve as an example of problems in the development and evaluation of machine learning models for metabolite annotation.

Keywords:  database search; in silico methods; metabolite annotation; metascores; molecular structure; parody paper; tandem mass spectrometry

DOI:  https://doi.org/10.3390/metabo13030314
Metabolites. 2023 Feb 27. pii: 353. [Epub ahead of print]13(3):

Life-Threatening Cardiogenic Shock Related to Venlafaxine Poisoning-A Case Report with Metabolomic Approach.

Romain Magny, Bruno Mégarbane, Pauline Guillaud, Lucie Chevillard, Nicolas Auzeil, Pauline Thiebot, Sebastian Voicu, Isabelle Malissin, Nicolas Deye, Laurence Labat, Pascal Houzé.

  Metabolomics in clinical toxicology aim at reliably identifying and semi-quantifying a broad array of endogenous and exogenous metabolites using dedicated analytical methods. Here, we developed a three-step-based workflow to investigate the metabolic impact of the antidepressant drug venlafaxine in a poisoned patient who developed life-threatening cardiac failure managed with extracorporeal membrane oxygenation. Both targeted quantitative and untargeted semi-quantitative metabolomic analyses using liquid chromatography hyphenated to high-resolution tandem mass spectrometry were performed to determine the plasma kinetics of venlafaxine, O-desmethyl-venlafaxine, and N-desmethyl-venlafaxine and to identify sixteen different venlafaxine-derived metabolites including one unknown (i.e., venlafaxine conjugated to a hexosyl-radical), respectively. Correlations between the quantitative metabolomic data and annotated endogenous metabolites suggested impaired amino acid and lipid metabolism, Krebs cycle, and kynurenine pathway. This preliminary study represents a first step towards a more extensive application of toxicometabolomics in clinical toxicology and a useful workflow to identify the biomarkers of toxicity.

Keywords:  ECMO; analytical toxicology; cardiotoxicity; clinical toxicology; management; metabolomics; molecular network; poisoning; venlafaxine

DOI:  https://doi.org/10.3390/metabo13030353
J Am Soc Mass Spectrom. 2023 Mar 27.

Identifying Hair Biomarker Candidates for Alzheimer's Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies.

Chih-Wei Chang, Jen-Yi Hsu, Ping-Zu Hsiao, Yuan-Chih Chen, Pao-Chi Liao.

  High-resolution mass spectrometry (HRMS)-based untargeted metabolomics strategies have emerged as an effective tool for discovering biomarkers of Alzheimer's disease (AD). There are various HRMS-based untargeted metabolomics strategies for biomarker discovery, including the data-dependent acquisition (DDA) method, the combination of full scan and target MS/MS, and the all ion fragmentation (AIF) method. Hair has emerged as a potential biospecimen for biomarker discovery in clinical research since it might reflect the circulating metabolic profiles over several months, while the analytical performances of the different data acquisition methods for hair biomarker discovery have been rarely investigated. Here, the analytical performances of three data acquisition methods in HRMS-based untargeted metabolomics for hair biomarker discovery were evaluated. The human hair samples from AD patients (N = 23) and cognitively normal individuals (N = 23) were used as an example. The most significant number of discriminatory features was acquired using the full scan (407), which is approximately 10-fold higher than that using the DDA strategy (41) and 11% higher than that using the AIF strategy (366). Only 66% of discriminatory chemicals discovered in the DDA strategy were discriminatory features in the full scan dataset. Moreover, compared to the deconvoluted MS/MS spectra with coeluted and background ions from the AIF method, the MS/MS spectrum obtained from the targeted MS/MS approach is cleaner and purer. Therefore, an untargeted metabolomics strategy combining the full scan with the targeted MS/MS method could obtain most discriminatory features along with a high quality MS/MS spectrum for discovering the AD biomarkers.

Keywords:  Alzheimer’s disease; biomarker discovery; data acquisition; high-resolution mass spectrometry; untargeted metabolomics

DOI:  https://doi.org/10.1021/jasms.2c00294
Gastroenterology. 2023 Mar 24. pii: S0016-5085(23)00507-3. [Epub ahead of print]

Proteomic profiling of colorectal adenomas identifies a predictive risk signature for development of metachronous advanced colorectal neoplasia.

Jacob Mathias Bech, Thilde Terkelsen, Annette Snejbjerg Bartels, Fabian Coscia, Sophia Doll, Siqi Zhao, Zhaojun Zhang, Nils Brünner, Jan Lindebjerg, Gunvor Iben Madsen, Xiangdong Fang, Matthias Mann, José Manuel Afonso Moreira.

   BACKGROUND & AIMS: Colonic adenomatous polyps, or adenomas, are frequent precancerous lesions and the origin of most cases of colorectal adenocarcinoma. However, we know from epidemiological studies that although most colorectal cancers originate from adenomas, only a small fraction of adenomas (3-5%) ever progress to cancer. At present, there are no molecular markers to guide follow-up surveillance programs.
METHODS: We profiled by mass spectrometry (MS)-based proteomics combined with machine learning analysis a selected cohort of formalin fixed paraffin embedded HG adenomas with long clinical follow-up, collected as part of the Danish national screening program. We grouped subjects in the cohort according to their subsequent history of findings: a non-metachronous advanced neoplasia group (G0), with no new HG adenomas or CRC up to 10 years after polypectomy, and a metachronous advanced neoplasia group (G1) where individuals developed a new HG adenoma or CRC within five years of diagnosis.
RESULTS: We generated a proteome dataset from 98 selected HG adenoma samples, including 20 technical replicates, of which 45 samples belonged to the non-metachronous advanced neoplasia group and 53 to the metachronous advanced neoplasia group. The clear distinction of these two groups seen in an UMAP plot indicated that the information contained within the abundance of the ∼5,000 proteins was sufficient to predict future occurrence of HG adenomas or development of CRC.
CONCLUSIONS: We performed an in-depth analysis of quantitative proteomic data from 98 resected adenoma samples using various novel algorithms and statistical packages, and found that their proteome can predict development of metachronous advanced lesions and progression several years in advance.

Keywords:  biomarkers; colonic adenomatous polyps; colorectal cancer; progression

DOI:  https://doi.org/10.1053/j.gastro.2023.03.208
Metabolomics. 2023 Mar 28. 19(4): 26

Optimizing XCMS parameters for GC-MS metabolomics data processing: a case study.

Emile Kelly Porto Dos Santos, Gisele André Baptista Canuto.

   BACKGROUND AND AIMS: Optimizing metabolomics data processing parameters is a challenging and fundamental task to obtain reliable results. Automated tools have been developed to assist this optimization for LC-MS data. GC-MS data require substantial modifications in processing parameters, as the chromatographic profiles are more robust, with more symmetrical and Gaussian peaks. This work compared an automated XCMS parameter optimization using the Isotopologue Parameter Optimization (IPO) software with manual optimization of GC-MS metabolomics data. Additionally, the results were compared to online XCMS platform.
METHODS: GC-MS data from control and test groups of intracellular metabolites from Trypanosoma cruzi trypomastigotes were used. Optimizations were performed on the quality control (QC) samples.
RESULTS: The results in terms of the number of molecular features extracted, repeatability, missing values, and the search for significant metabolites showed the importance of optimizing the parameters for peak detection, alignment, and grouping, especially those related to peak width (fwhm, bw) and noise ratio (snthresh).
CONCLUSION: This is the first time that a systematic optimization using IPO has been performed on GC-MS data. The results demonstrate that there is no universal approach for optimization but automated tools are valuable at this stage of the metabolomics workflow. The online XCMS proves to be an interesting processing tool, helping, above all, in the choice of parameters as a starting point for adjustments and optimizations. Although the tools are easy to use, there is still a need for technical knowledge about the analytical methods and instruments used.

Keywords:  Data analysis; Data processing; Gas chromatography-mass spectrometry; IPO; XCMS

DOI:  https://doi.org/10.1007/s11306-023-01992-1
Int J Mol Sci. 2023 Mar 10. pii: 5350. [Epub ahead of print]24(6):

Bottom-Up Proteomics: Advancements in Sample Preparation.

Van-An Duong, Hookeun Lee.

  Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics is a powerful technique for profiling proteomes of cells, tissues, and body fluids. Typical bottom-up proteomic workflows consist of the following three major steps: sample preparation, LC-MS/MS analysis, and data analysis. LC-MS/MS and data analysis techniques have been intensively developed, whereas sample preparation, a laborious process, remains a difficult task and the main challenge in different applications. Sample preparation is a crucial stage that affects the overall efficiency of a proteomic study; however, it is prone to errors and has low reproducibility and throughput. In-solution digestion and filter-aided sample preparation are the typical and widely used methods. In the past decade, novel methods to improve and facilitate the entire sample preparation process or integrate sample preparation and fractionation have been reported to reduce time, increase throughput, and improve reproducibility. In this review, we have outlined the current methods used for sample preparation in proteomics, including on-membrane digestion, bead-based digestion, immobilized enzymatic digestion, and suspension trapping. Additionally, we have summarized and discussed current devices and methods for integrating different steps of sample preparation and peptide fractionation.

Keywords:  FASP; LC-MS/MS; S-Trap; SP3; automation; in-solution digestion; proteomics; sample preparation

DOI:  https://doi.org/10.3390/ijms24065350
J Pharm Pharmacol. 2023 Mar 27. pii: rgad025. [Epub ahead of print]

Oridonin inhibits Hela cell proliferation via downregulation of glutathione metabolism: a new insight from metabolomics.

Weijia Wang, Nan Zhang.

   OBJECTIVES: This study aims to elucidate Oridonin' s inhibitory mechanism to cervical cancer using metabolomics methods and pharmacological assays.
METHODS: Network pharmacology and KEGG pathway analysis are used to identify overlapped targets and involved metabolic pathways. UPLC-MS/MS metabolomics analysis is used to determine altered metabolites after Oridonin treatment. Other bioassays are also employed to uncover the changes in critical molecules that are highly related to altered metabolites.
KEY FINDINGS: Seventy-five overlapped targets are identified between Oridonin and cervical cancer. Twenty-one metabolites involved in tricarboxylic acid cycle glutathione metabolism, branched-chain amino acid metabolism and so on changes significantly after Oridonin treatment. Oridonin treatment significantly reduces the content of cysteine and inhibit the catalytic activity of glutamine-cysteine ligase subunit, a rate-limiting enzyme for the synthesis of glutathione. As a result, the content of glutathione is also reduced. The antioxidant enzyme glutathione peroxidase 4 which uses glutathione as a cofactor, is inactivated, resulting in a burst release of reactive oxygen species. The ATP content is also significantly reduced in Hela cells after Oridonin treatment.
CONCLUSIONS: This study finds that Oridonin treatment induces Hela cell apoptosis possibly via inhibition of the glutathione metabolism.

Keywords:  cell apoptosis; cervical cancer; glutathione metabolism; metabolomics; oridonin; reactive oxygen species

DOI:  https://doi.org/10.1093/jpp/rgad025
Metabolites. 2023 Feb 25. pii: 345. [Epub ahead of print]13(3):

The Role of Reprogrammed Glucose Metabolism in Cancer.

Meran Keshawa Ediriweera, Sharmila Jayasena.

  Cancer cells reprogram their metabolism to meet biosynthetic needs and to adapt to various microenvironments. Accelerated glycolysis offers proliferative benefits for malignant cells by generating glycolytic products that move into branched pathways to synthesize proteins, fatty acids, nucleotides, and lipids. Notably, reprogrammed glucose metabolism and its associated events support the hallmark features of cancer such as sustained cell proliferation, hijacked apoptosis, invasion, metastasis, and angiogenesis. Overproduced enzymes involved in the committed steps of glycolysis (hexokinase, phosphofructokinase-1, and pyruvate kinase) are promising pharmacological targets for cancer therapeutics. In this review, we summarize the role of reprogrammed glucose metabolism in cancer cells and how it can be manipulated for anti-cancer strategies.

Keywords:  cancer; cancer treatment; glucose; metabolism

DOI:  https://doi.org/10.3390/metabo13030345
bioRxiv. 2023 Mar 13. pii: 2023.03.13.531822. [Epub ahead of print]

Coupling microdroplet-based sample preparation, multiplexed isobaric labeling, and nanoflow peptide fractionation for deep proteome profiling of tissue microenvironment.

Marija Veličković, Thomas L Fillmore, Kwame Attah, Camilo Posso, James C Pino, Rui Zhao, Sarah M Williams, Dušan Veličković, Jon M Jacobs, Kristin E Burnum-Johnson, Ying Zhu, Paul D Piehowski.

There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity at a cell-type-specific level to better understand and predict the function of complex biological systems, such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverages due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (Microdroplet Processing in One pot for Trace Samples), the multiplexed isobaric labelling, and a nanoflow peptide fractionation approach. The integrated workflow allowed to maximize proteome coverage of laser-isolated tissue samples containing nanogram proteins. We demonstrated the deep spatial proteomics can quantify more than 5,000 unique proteins from a small-sized human pancreatic tissue pixel (∼60,000 µm2) and reveal unique islet microenvironments.

DOI: https://doi.org/10.1101/2023.03.13.531822
Metabolites. 2023 Mar 18. pii: 447. [Epub ahead of print]13(3):

Metabolomic Studies in Inborn Errors of Metabolism: Last Years and Future Perspectives.

Marcello Cossu, Roberta Pintus, Marco Zaffanello, Michele Mussap, Fabiola Serra, Maria Antonietta Marcialis, Vassilios Fanos.

  The inborn errors of metabolism (IEMs or Inherited Metabolic Disorders) are a heterogeneous group of diseases caused by a deficit of some specific metabolic pathways. IEMs may present with multiple overlapping symptoms, sometimes difficult delayed diagnosis and postponed therapies. Additionally, many IEMs are not covered in newborn screening and the diagnostic profiling in the metabolic laboratory is indispensable to reach a correct diagnosis. In recent years, Metabolomics helped to obtain a better understanding of pathogenesis and pathophysiology of IEMs, by validating diagnostic biomarkers, discovering new specific metabolic patterns and new IEMs itself. The expansion of Metabolomics in clinical biochemistry and laboratory medicine has brought these approaches in clinical practice as part of newborn screenings, as an exam for differential diagnosis between IEMs, and evaluation of metabolites in follow up as markers of severity or therapies efficacy. Lastly, several research groups are trying to profile metabolomics data in platforms to have a holistic vision of the metabolic, proteomic and genomic pathways of every single patient. In 2018 this team has made a review of literature to understand the value of Metabolomics in IEMs. Our review offers an update on use and perspectives of metabolomics in IEMs, with an overview of the studies available from 2018 to 2022.

Keywords:  biomarkers; inborn errors of metabolism; metabolomic; newborn screening

DOI:  https://doi.org/10.3390/metabo13030447
Molecules. 2023 Mar 15. pii: 2653. [Epub ahead of print]28(6):

Matrix Effects in GC-MS Profiling of Common Metabolites after Trimethylsilyl Derivatization.

Elena Tarakhovskaya, Andrea Marcillo, Caroline Davis, Sanja Milkovska-Stamenova, Antje Hutschenreuther, Claudia Birkemeyer.

  Metabolite profiling using gas chromatography coupled to mass spectrometry (GC-MS) is one of the most frequently applied and standardized methods in research projects using metabolomics to analyze complex samples. However, more than 20 years after the introduction of non-targeted approaches using GC-MS, there are still unsolved challenges to accurate quantification in such investigations. One particularly difficult aspect in this respect is the occurrence of sample-dependent matrix effects. In this project, we used model compound mixtures of different compositions to simplify the study of the complex interactions between common constituents of biological samples in more detail and subjected those to a frequently applied derivatization protocol for GC-MS analysis, namely trimethylsilylation. We found matrix effects as signal suppression and enhancement of carbohydrates and organic acids not to exceed a factor of ~2, while amino acids can be more affected. Our results suggest that the main reason for our observations may be an incomplete transfer of carbohydrate and organic acid derivatives during the injection process and compound interaction at the start of the separation process. The observed effects were reduced at higher target compound concentrations and by using a more suitable injection-liner geometry.

Keywords:  compound saturation; gas chromatography–mass spectrometry; metabolomics; quantification; signal enhancement; signal suppression

DOI:  https://doi.org/10.3390/molecules28062653
J Proteome Res. 2023 Mar 28.

Metabolomics Identifies a Panel of Diagnostic Biomarkers for Early Human Embryonic Development Arrest.

Yifei Liu, Xuemei Fan, Huanhuan Pang, Saiya Liu, Bohong Wang, Chunmei Wang, Qingya Yan, Wenping Song, Zeping Hu, Qingfei Liu, Yun Shi.

  Early embryonic development arrest (EEDA) is a unique form of early spontaneous abortion in pregnant women, which is previously suggested to be associated with metabolic abnormalities. Noninvasive biomarkers would significantly improve its diagnosis and clinical outcome. Here, we performed a targeted metabolomics study in plasma from EEDA patients (n = 27) and normal pregnant women (NPW, n = 27) using liquid chromatography coupled with mass spectrometry (LC-MS) to identify potential diagnostic marker metabolites. Our results showed significantly different plasma metabolic profiles between EEDA patients and NPW. Particularly, EEDA patients showed significant alterations in amino acid, carbohydrate, and vitamin metabolism, which were characterized by 21 significantly increased metabolites and five decreased metabolites in plasma. Further receiver operating characteristic analysis showed that an optimal combination of S-methyl-5'-thioadenosine, kynurenine, leucine, and malate could be used as a panel of metabolites for EEDA diagnosis. The area under the curve of the metabolite panel was 0.941, suggesting a better performance than any single metabolite for the diagnosis of EEDA. In summary, our study identifies a panel of differential metabolites in plasma that could act as potential biomarkers for the diagnosis of EEDA in clinical settings.

Keywords:  diagnosis; early embryo development arrest; potential biomarkers; targeted metabolomics

DOI:  https://doi.org/10.1021/acs.jproteome.2c00816
Metabolites. 2023 Feb 21. pii: 311. [Epub ahead of print]13(3):

Development of an Untargeted Metabolomics Strategy to Study the Metabolic Rewiring of Dendritic Cells upon Lipopolysaccharide Activation.

Jessica Michieletto, Aurélie Delvaux, Emeline Chu-Van, Christophe Junot, François Fenaille, Florence A Castelli.

  Dendritic cells (DCs) are essential immune cells for defense against external pathogens. Upon activation, DCs undergo profound metabolic alterations whose precise nature remains poorly studied at a large scale and is thus far from being fully understood. The goal of the present work was to develop a reliable and accurate untargeted metabolomics workflow to get a deeper insight into the metabolism of DCs when exposed to an infectious agent (lipopolysaccharide, LPS, was used to mimic bacterial infection). As DCs transition rapidly from a non-adherent to an adherent state upon LPS exposure, one of the leading analytical challenges was to implement a single protocol suitable for getting comparable metabolomic snapshots of those two cellular states. Thus, a thoroughly optimized and robust sample preparation method consisting of a one-pot solvent-assisted method for the simultaneous cell lysis/metabolism quenching and metabolite extraction was first implemented to measure intracellular DC metabolites in an unbiased manner. We also placed special emphasis on metabolome coverage and annotation by using a combination of hydrophilic interaction liquid chromatography and reverse phase columns coupled to high-resolution mass spectrometry in conjunction with an in-house developed spectral database to identify metabolites at a high confidence level. Overall, we were able to characterize up to 171 unique meaningful metabolites in DCs. We then preliminarily compared the metabolic profiles of DCs derived from monocytes of 12 healthy donors upon in vitro LPS activation in a time-course experiment. Interestingly, the resulting data revealed differential and time-dependent activation of some particular metabolic pathways, the most impacted being nucleotides, nucleotide sugars, polyamines pathways, the TCA cycle, and to a lesser extent, the arginine pathway.

Keywords:  adherent cells; dendritic cells; high-resolution mass spectrometry; immunometabolomics; liquid chromatography; metabolomics; sample preparation

DOI:  https://doi.org/10.3390/metabo13030311
Metabolites. 2023 Mar 08. pii: 402. [Epub ahead of print]13(3):

Cross-Platform Comparison of Amino Acid Metabolic Profiling in Three Model Organisms Used in Environmental Metabolomics.

Jessica C D'eon, Brian P Lankadurai, André J Simpson, Eric J Reiner, David G Poirier, Greg C Vanlerberghe, Myrna J Simpson.

  Environmental metabolomics is a promising approach to study pollutant impacts to target organisms in both terrestrial and aquatic environments. To this end, both nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based methods are used to profile amino acids in different environmental metabolomic studies. However, these two methods have not been compared directly which is an important consideration for broader comparisons in the environmental metabolomics field. We compared the quantification of 18 amino acids in the tissue extracts of Daphnia magna, a common model organism used in both ecotoxicology and ecology, using both 1H NMR spectroscopy and liquid chromatography with tandem MS (LC-MS/MS). 1H NMR quantification of amino acids agreed with the LC-MS/MS quantification for 17 of 18 amino acids measured. We also tested both quantitative methods in a D. magna sub-lethal exposure study to copper and lithium. Again, both NMR and LC-MS/MS measurements showed agreement. We extended our analyses with extracts from the earthworm Eisenia fetida and the plant model Nicotiana tabacum. The concentrations of amino acids by both 1H NMR and LC-MS/MS, agreed and demonstrated the robustness of both techniques for quantitative metabolomics. These findings demonstrate the compatibility of these two analytical platforms for amino acid profiling in environmentally relevant model organisms and emphasizes that data from either method is robust for comparisons across studies to further build the knowledge base related to pollutant exposure impacts and toxic responses of diverse environmental organisms.

Keywords:  Daphnia magna; Eisenia fetida; LC-MS/MS; NMR spectroscopy; Nicotiana tabacum; metabolite profiling

DOI:  https://doi.org/10.3390/metabo13030402
J Lipid Res. 2023 Mar 27. pii: S0022-2275(23)00037-8. [Epub ahead of print] 100364

The origin of long-chain fatty acids required for de novo ether lipid/plasmalogen synthesis.

Serhii Chornyi, Rob Ofman, Janet Koster, Hans R Waterham.

  Peroxisomes are single-membrane bounded organelles, that in humans play a dual role in lipid metabolism, including the degradation of very long-chain fatty acids and the synthesis of ether lipids/plasmalogens. The first step in de novo ether lipid synthesis is mediated by the peroxisomal enzyme glyceronephosphate O-acyltransferase, which has a strict substrate specificity reacting only with the long-chain acyl-CoAs. The aim of this study was to determine the origin of these long-chain acyl-CoAs. To this end, we developed a sensitive method for the measurement of de novo ether phospholipid synthesis in cells and, by CRISPR/Cas9 genome editing, generated a series of HeLa cell lines with deficiencies of proteins involved in peroxisomal biogenesis, beta-oxidation, ether lipid synthesis, or metabolite transport. Our results show that the long-chain acyl-CoAs required for the first step of ether lipid synthesis can be imported from the cytosol by the peroxisomal ABCD proteins, in particular ABCD3. Furthermore, we show that these acyl-CoAs can be produced intraperoxisomally by chain shortening of CoA esters of very long-chain fatty acids via beta-oxidation. Our results demonstrate that peroxisomal beta-oxidation and ether lipid synthesis are intimately connected and that the peroxisomal ABC transporters play a crucial role in de novo ether lipid synthesis.

Keywords:  Biosynthesis; Fatty acid; Metabolism; Phospholipids; Transport; Zellweger syndrome

DOI:  https://doi.org/10.1016/j.jlr.2023.100364
Front Cell Dev Biol. 2023 ;11 1071037

Sustained IP3-linked Ca2+ signaling promotes progression of triple negative breast cancer cells by regulating fatty acid metabolism.

Riccardo Filadi, Agnese De Mario, Matteo Audano, Patrizia Romani, Silvia Pedretti, Cesar Cardenas, Sirio Dupont, Cristina Mammucari, Nico Mitro, Paola Pizzo.

  Rewiring of mitochondrial metabolism has been described in different cancers as a key step for their progression. Calcium (Ca2+) signaling regulates mitochondrial function and is known to be altered in several malignancies, including triple negative breast cancer (TNBC). However, whether and how the alterations in Ca2+ signaling contribute to metabolic changes in TNBC has not been elucidated. Here, we found that TNBC cells display frequent, spontaneous inositol 1,4,5-trisphosphate (IP3)-dependent Ca2+ oscillations, which are sensed by mitochondria. By combining genetic, pharmacologic and metabolomics approaches, we associated this pathway with the regulation of fatty acid (FA) metabolism. Moreover, we demonstrated that these signaling routes promote TNBC cell migration in vitro, suggesting they might be explored to identify potential therapeutic targets.

Keywords:  Ca2+; IP3; MCU; TNBC; acylcarnitine; breast cancer; fatty acids; mitochondria

DOI:  https://doi.org/10.3389/fcell.2023.1071037
Viruses. 2023 Mar 08. pii: 702. [Epub ahead of print]15(3):

Quantitative Proteomics for the Identification of Differentially Expressed Proteins in the Extracellular Vesicles of Cervical Cancer Cells.

Víctor Acevedo-Sánchez, Roy S Martínez-Ruiz, Sergio R Aguilar-Ruíz, Honorio Torres-Aguilar, Pedro Chávez-Olmos, Efraín Garrido, Rafael Baltiérrez-Hoyos, María de Los A Romero-Tlalolini.

  The extracellular vesicles (EVs) in a tumoral microenvironment can exert different functions by transferring their content, which has been poorly described in cervical cancer. Here, we tried to clarify the proteomic content of these EVs, comparing those derived from cancerous HPV (+) keratinocytes (HeLa) versus those derived from normal HPV (-) keratinocytes (HaCaT). We performed a quantitative proteomic analysis, using LC-MS/MS, of the EVs from HeLa and HaCaT cell lines. The up- and downregulated proteins in the EVs from the HeLa cell line were established, along with the cellular component, molecular function, biological processes, and signaling pathways in which they participate. The biological processes with the highest number of upregulated proteins are cell adhesion, proteolysis, lipid metabolic process, and immune system processes. Interestingly, three of the top five signaling pathways with more up- and downregulated proteins are part of the immune response. Due to their content, we can infer that EVs can have a significant role in migration, invasion, metastasis, and the activation or suppression of immune system cells in cancer.

Keywords:  HPV; HeLa cells; cancer; exosomes; extracellular vesicles; proteomics

DOI:  https://doi.org/10.3390/v15030702
Scand J Med Sci Sports. 2023 Mar 27.

Mass spectrometry-based proteomics approaches to interrogate skeletal muscle adaptations to exercise.

Daniel T Cervone, Roger Moreno-Justicia, Júlia Prats Quesada, Atul S Deshmukh.

  Acute exercise and chronic exercise training elicit beneficial whole-body changes in physiology that ultimately depend on profound alterations to the dynamics of tissue-specific proteins. Since the work accomplished during exercise owes predominantly to skeletal muscle, it has received the majority of interest from exercise scientists that attempt to unravel adaptive mechanisms accounting for salutary metabolic effects and performance improvements that arise from training. Contemporary scientists are also beginning to use mass spectrometry-based proteomics, which is emerging as a powerful approach to interrogate the muscle protein signature in a more comprehensive manner. Collectively, these technologies facilitate the analysis of skeletal muscle protein dynamics from several viewpoints, including changes to intracellular proteins (expression proteomics), secreted proteins (secretomics), post-translational modifications as well as fiber-, cell-, and organelle-specific changes. This review aims to highlight recent literature that has leveraged new workflows and advances in mass spectrometry-based proteomics to further our understanding of training-related changes in skeletal muscle. We call attention to untapped areas in skeletal muscle proteomics research relating to exercise training and metabolism, as well as basic points of contention when applying mass spectrometry-based analyses, particularly in the study of human biology. We further encourage researchers to couple the hypothesis-generating and descriptive nature of omics data with functional analyses that propel our understanding of the complex adaptive responses in skeletal muscle that occur with acute and chronic exercise.

Keywords:  fiber-type; heterogeneity; organellar; post-translational modification; secretomics; singlecell; training

DOI:  https://doi.org/10.1111/sms.14334