bims-mascan 2023-01-08 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2023–01–08
twenty-six papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Robust and high-throughput lipidomic quantitation of human blood samples using flow injection analysis with tandem mass spectrometry for clinical use.
Lipid network and moiety analysis for revealing enzymatic dysregulation and mechanistic alterations from lipidomics data.
Progress of potential drugs targeted in lipid metabolism research.
The Crux Toolkit for Analysis of Bottom-Up Tandem Mass Spectrometry Proteomics Data.
Developing quantitative assays for six urinary glycoproteins using parallel reaction monitoring, data-independent acquisition, and TMT-based data-dependent acquisition.
Characterization of metabolic reprogramming by metabolomics in the oncocytic thyroid cancer cell line XTC.UC1.
Pre-analytical processing of plasma and serum samples for combined proteome and metabolome analysis.
Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry.
LipidA-IDER to Explore the Global Lipid A Repertoire of Drug-Resistant Gram-Negative Bacteria.
Lipidomics analysis in drug discovery and development.
Optimization of Higher-Energy Collisional Dissociation Fragmentation Energy for Intact Protein-Level Tandem Mass Tag Labeling.
Rapid Multi-Omics Sample Preparation for Mass Spectrometry.
AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification.
Comprehensive Glycan Analysis of Sphingolipids in Human Serum/Plasma.
Clinical lipidomics in the era of the big data.
Illuminating lactate in cells, mice, and patient samples.
Screening and diagnosis of triple negative breast cancer based on rapid metabolic fingerprinting by conductive polymer spray ionization mass spectrometry and machine learning.
Evaluation of the Potential Risk of Analyzing Phosphopeptides with Easy-nLC 1200-Coupled Mass Spectrometers.
Nutrient metabolism of the nucleus pulposus: A literature review.
Targeting fatty acid metabolism in glioblastoma.
MATRIX platform to analyze translation machinery remodeling in glioblastoma cells.
A novel screening method for free non-standard amino acids in human plasma samples using AccQ·Tag reagents and LC-MS/MS.
Mass Spectrometry of Neutral Glycosphingolipids.
Lipid droplets and ferroptosis as new players in brain cancer glioblastoma progression and therapeutic resistance.
Carnitine octanoyltransferase is important for the assimilation of exogenous acetyl-L-carnitine into acetyl-CoA in mammalian cells.
STAT proteins in cancer: orchestration of metabolism.

Anal Bioanal Chem. 2023 Jan 04.

Robust and high-throughput lipidomic quantitation of human blood samples using flow injection analysis with tandem mass spectrometry for clinical use.

Jakub Idkowiak, Robert Jirásko, Denisa Kolářová, Josef Bártl, Tomáš Hájek, Michela Antonelli, Zuzana Vaňková, Denise Wolrab, Roman Hrstka, Hana Študentová, Bohuslav Melichar, Karolína Pešková, Michal Holčapek.

  Direct infusion of lipid extracts into the ion source of a mass spectrometer is a well-established method for lipid analysis. In most cases, nanofluidic devices are used for sample introduction. However, flow injection analysis (FIA) based on sample infusion from a chromatographic pump can offer a simple alternative to shotgun-based approaches. Here, we describe important modification of a method based on FIA and tandem mass spectrometry (MS/MS). We focus on minimizing contamination of the FIA/MS both to render the lipidomic platform more robust and to increase its capacity and applicability for long-sequence measurements required in clinical applications. Robust validation of the developed method confirms its suitability for lipid quantitation in human plasma analysis. Measurements of standard human plasma reference material (NIST SRM 1950) and a set of plasma samples collected from kidney cancer patients and from healthy volunteers yielded highly similar results between FIA-MS/MS and ultra-high-performance supercritical fluid chromatography (UHPSFC)/MS, thereby demonstrating that all modifications have practically no effect on the statistical output. Newly modified FIA-MS/MS allows for the quantitation of 141 lipid species in plasma (11 major lipid classes) within 5.7 min. Finally, we tested the method in a clinical laboratory of the General University Hospital in Prague. In the clinical setting, the method capacity reached 257 samples/day. We also show similar performance of the classification models trained based on the results obtained in clinical settings and the analytical laboratory at the University of Pardubice. Together, these findings demonstrate the high potential of the modified FIA-MS/MS for application in clinical laboratories to measure plasma and serum lipid profiles.

Keywords:  Direct infusion lipidomics; Flow injection analysis; High-throughput lipidomics; Lipid quantitation; Mass spectrometry; Validation

DOI:  https://doi.org/10.1007/s00216-022-04490-w
Brief Bioinform. 2023 Jan 02. pii: bbac572. [Epub ahead of print]

Lipid network and moiety analysis for revealing enzymatic dysregulation and mechanistic alterations from lipidomics data.

Tim D Rose, Nikolai Köhler, Lisa Falk, Lucie Klischat, Olga E Lazareva, Josch K Pauling.

  Lipidomics is of growing importance for clinical and biomedical research due to many associations between lipid metabolism and diseases. The discovery of these associations is facilitated by improved lipid identification and quantification. Sophisticated computational methods are advantageous for interpreting such large-scale data for understanding metabolic processes and their underlying (patho)mechanisms. To generate hypothesis about these mechanisms, the combination of metabolic networks and graph algorithms is a powerful option to pinpoint molecular disease drivers and their interactions. Here we present lipid network explorer (LINEX$^2$), a lipid network analysis framework that fuels biological interpretation of alterations in lipid compositions. By integrating lipid-metabolic reactions from public databases, we generate dataset-specific lipid interaction networks. To aid interpretation of these networks, we present an enrichment graph algorithm that infers changes in enzymatic activity in the context of their multispecificity from lipidomics data. Our inference method successfully recovered the MBOAT7 enzyme from knock-out data. Furthermore, we mechanistically interpret lipidomic alterations of adipocytes in obesity by leveraging network enrichment and lipid moieties. We address the general lack of lipidomics data mining options to elucidate potential disease mechanisms and make lipidomics more clinically relevant.

Keywords:  disease mechanisms; lipid metabolic networks; lipidomics; network enrichment

DOI:  https://doi.org/10.1093/bib/bbac572
Front Pharmacol. 2022 ;13 1067652

Progress of potential drugs targeted in lipid metabolism research.

Kai Liang, Jian-Ye Dai.

  Lipids are a class of complex hydrophobic molecules derived from fatty acids that not only form the structural basis of biological membranes but also regulate metabolism and maintain energy balance. The role of lipids in obesity and other metabolic diseases has recently received much attention, making lipid metabolism one of the attractive research areas. Several metabolic diseases are linked to lipid metabolism, including diabetes, obesity, and atherosclerosis. Additionally, lipid metabolism contributes to the rapid growth of cancer cells as abnormal lipid synthesis or uptake enhances the growth of cancer cells. This review introduces the potential drug targets in lipid metabolism and summarizes the important potential drug targets with recent research progress on the corresponding small molecule inhibitor drugs. The significance of this review is to provide a reference for the clinical treatment of metabolic diseases related to lipid metabolism and the treatment of tumors, hoping to deepen the understanding of lipid metabolism and health.

Keywords:  cancer; fatty acid; lipid metabolism; metabolic disease; small molecule inhibitor

DOI:  https://doi.org/10.3389/fphar.2022.1067652
J Proteome Res. 2023 Jan 04.

The Crux Toolkit for Analysis of Bottom-Up Tandem Mass Spectrometry Proteomics Data.

Attila Kertesz-Farkas, Frank Lawrence Nii Adoquaye Acquaye, Kishankumar Bhimani, Jimmy K Eng, William E Fondrie, Charles Grant, Michael R Hoopmann, Andy Lin, Yang Y Lu, Robert L Moritz, Michael J MacCoss, William Stafford Noble.

  The Crux tandem mass spectrometry data analysis toolkit provides a collection of algorithms for analyzing bottom-up proteomics tandem mass spectrometry data. Many publications have described various individual components of Crux, but a comprehensive summary has not been published since 2014. The goal of this work is to summarize the functionality of Crux, focusing on developments since 2014. We begin with empirical results demonstrating our recently implemented speedups to the Tide search engine. Other new features include a new score function in Tide, two new confidence estimation procedures, as well as three new tools: Param-medic for estimating search parameters directly from mass spectrometry data, Kojak for searching cross-linked mass spectra, and DIAmeter for searching data independent acquisition data against a sequence database.

Keywords:  database search; false discovery rate control; mass spectrometry; open source software

DOI:  https://doi.org/10.1021/acs.jproteome.2c00615
Proteomics. 2023 Jan 02. e2200072

Developing quantitative assays for six urinary glycoproteins using parallel reaction monitoring, data-independent acquisition, and TMT-based data-dependent acquisition.

Sean Ponce, Hui Zhang.

  Quantitative approaches encompassing parallel reaction monitoring (PRM), data independent acquisition (DIA), and data dependent acquisition (DDA) are commonly used to investigate protein expression profiles. However, analytical performances of assays developed using PRM, DIA and Tandem Mass Tag (TMT) based DDA for quantitative proteomics have yet not been investigated. Here, we developed assays for six glycopeptides identified from glycoproteins, including Leucine-rich alpha-2-glycoprotein (LRG1), Prostaglandin-H2 D-isomerase (PTGDS), Aminopeptidase N (ANPEP), CD63 antigen (CD63), Clusterin (CLU), and Prostatic acid phosphatase (ACPP), using PRM, DDA, and DIA and evaluated the analytical performances of each assay using the different acquisition modes. We also compared assays in each acquisition mode on three different orbitrap instruments: Thermo Fisher Q Exactive, Exploris 480, and Lumos. We found that DIA showed the largest linear range, highest sensitivity, and most reproducibility. We then applied our developed DIA assays to urine samples from non-aggressive n = 48) and aggressive (n = 35) prostate cancer patients. In conclusion, we developed assays for the six glycoproteins, evaluated the analytical performances of each assay in DIA, PRM, and PRM acquisition modes on three types of mass spectrometry instruments, and chose the DIA assays for the quantitative analysis of urine samples from patients with aggressive and non-aggressive prostate cancer.

Keywords:  aggressive prostate cancer; data-dependent acquisition; data-independent acquisition; mass spectrometry; parallel reaction monitoring

DOI:  https://doi.org/10.1002/pmic.202200072
Sci Rep. 2023 Jan 04. 13(1): 149

Characterization of metabolic reprogramming by metabolomics in the oncocytic thyroid cancer cell line XTC.UC1.

Tomomi Kurashige, Mika Shimamura, Koichiro Hamada, Michiko Matsuse, Norisato Mitsutake, Yuji Nagayama.

Oncocytic thyroid cancer is characterized by the aberrant accumulation of abnormal mitochondria in the cytoplasm and a defect in oxidative phosphorylation. We performed metabolomics analysis to compare metabolic reprogramming among the oncocytic and non-oncocytic thyroid cancer cell lines XTC.UC1 and TPC1, respectively, and a normal thyroid cell line Nthy-ori 3-1. We found that although XTC.UC1 cells exhibit higher glucose uptake than TPC1 cells, the glycolytic intermediates are not only utilized to generate end-products of glycolysis, but also diverted to branching pathways such as lipid metabolism and the serine synthesis pathway. Glutamine is preferentially used to produce glutathione to reduce oxidative stress in XTC.UC1 cells, rather than to generate α-ketoglutarate for anaplerotic flux into the TCA cycle. Thus, growth, survival and redox homeostasis of XTC.UC1 cells rely more on both glucose and glutamine than do TPC1 cells. Furthermore, XTC.UC1 cells contained higher amounts of intracellular amino acids which is due to higher expression of the amino acid transporter ASCT2 and enhanced autophagy, thus providing the building blocks for macromolecules and energy production. These metabolic alterations are required for oncocytic cancer cells to compensate their defective mitochondrial function and to alleviate excess oxidative stress.

DOI: https://doi.org/10.1038/s41598-023-27461-2
Front Mol Biosci. 2022 ;9 961448

Pre-analytical processing of plasma and serum samples for combined proteome and metabolome analysis.

Hagen M Gegner, Thomas Naake, Aurélien Dugourd, Torsten Müller, Felix Czernilofsky, Georg Kliewer, Evelyn Jäger, Barbara Helm, Nina Kunze-Rohrbach, Ursula Klingmüller, Carsten Hopf, Carsten Müller-Tidow, Sascha Dietrich, Julio Saez-Rodriguez, Wolfgang Huber, Rüdiger Hell, Gernot Poschet, Jeroen Krijgsveld.

  Metabolomic and proteomic analyses of human plasma and serum samples harbor the power to advance our understanding of disease biology. Pre-analytical factors may contribute to variability and bias in the detection of analytes, especially when multiple labs are involved, caused by sample handling, processing time, and differing operating procedures. To better understand the impact of pre-analytical factors that are relevant to implementing a unified proteomic and metabolomic approach in a clinical setting, we assessed the influence of temperature, sitting times, and centrifugation speed on the plasma and serum metabolomes and proteomes from six healthy volunteers. We used targeted metabolic profiling (497 metabolites) and data-independent acquisition (DIA) proteomics (572 proteins) on the same samples generated with well-defined pre-analytical conditions to evaluate criteria for pre-analytical SOPs for plasma and serum samples. Time and temperature showed the strongest influence on the integrity of plasma and serum proteome and metabolome. While rapid handling and low temperatures (4°C) are imperative for metabolic profiling, the analyzed proteomics data set showed variability when exposed to temperatures of 4°C for more than 2 h, highlighting the need for compromises in a combined analysis. We formalized a quality control scoring system to objectively rate sample stability and tested this score using external data sets from other pre-analytical studies. Stringent and harmonized standard operating procedures (SOPs) are required for pre-analytical sample handling when combining proteomics and metabolomics of clinical samples to yield robust and interpretable data on a longitudinal scale and across different clinics. To ensure an adequate level of practicability in a clinical routine for metabolomics and proteomics studies, we suggest keeping blood samples up to 2 h on ice (4°C) prior to snap-freezing as a compromise between stability and operability. Finally, we provide the methodology as an open-source R package allowing the systematic scoring of proteomics and metabolomics data sets to assess the stability of plasma and serum samples.

Keywords:  biomarker; metabolomics; plasma; proteomics; sample preparation

DOI:  https://doi.org/10.3389/fmolb.2022.961448
Anal Chem. 2023 Jan 03.

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry.

Carter K Asef, Markace A Rainey, Brianna M Garcia, Goncalo J Gouveia, Amanda O Shaver, Franklin E Leach, Alison M Morse, Arthur S Edison, Lauren M McIntyre, Facundo M Fernández.

Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insights into areas requiring further improvement.

DOI: https://doi.org/10.1021/acs.analchem.2c03749
Anal Chem. 2023 Jan 04.

LipidA-IDER to Explore the Global Lipid A Repertoire of Drug-Resistant Gram-Negative Bacteria.

Xue Li Guan, Johnathan Yi-Xiong Loh, Marco Lizwan, Sharon Cui Mun Chan, Jeric Mun Chung Kwan, Tze Peng Lim, Tse Hsien Koh, Li-Yang Hsu, Bernett Teck Kwong Lee.

With the global emergence of drug-resistant bacteria causing difficult-to-treat infections, there is an urgent need for a tool to facilitate studies on key virulence and antimicrobial resistant factors. Mass spectrometry (MS) has contributed substantially to the elucidation of the structure-function relationships of lipid A, the endotoxic component of lipopolysaccharide which also serves as an important protective barrier against antimicrobials. Here, we present LipidA-IDER, an automated structure annotation tool for system-level scale identification of lipid A from high-resolution tandem mass spectrometry (MS2) data. LipidA-IDER was validated against previously reported structures of lipid A in the reference bacteria, Escherichia coli and Pseudomonas aeruginosa. Using MS2 data of variable quality, we demonstrated LipidA-IDER annotated lipid A with a performance of 71.2% specificity and 70.9% sensitivity, offering greater accuracy than existing lipidomics software. The organism-independent workflow was further applied to a panel of six bacterial species: E. coli and Gram-negative members of ESKAPE pathogens. A comprehensive atlas comprising 188 distinct lipid A species, including remodeling intermediates, was generated and can be integrated with software including MS-DIAL and Metabokit for identification and semiquantitation. Systematic comparison of a pair of polymyxin-sensitive and polymyxin-resistant Acinetobacter baumannii isolated from a human patient unraveled multiple key lipid A structural features of polymyxin resistance within a single analysis. Probing the lipid A landscape of bacteria using LipidA-IDER thus holds immense potential for advancing our understanding of the vast diversity and structural complexity of a key lipid virulence and antimicrobial-resistant factor. LipidA-IDER is freely available at https://github.com/Systems-Biology-Of-Lipid-Metabolism-Lab/LipidA-IDER.

DOI: https://doi.org/10.1021/acs.analchem.1c03566
Curr Opin Chem Biol. 2022 Dec 29. pii: S1367-5931(22)00141-7. [Epub ahead of print]72 102256

Lipidomics analysis in drug discovery and development.

Sarantos Kostidis, Elena Sánchez-López, Martin Giera.

  Despite being a relatively new addition to the Omics' landscape, lipidomics is increasingly being recognized as an important tool for the identification of druggable targets and biochemical markers. In this review we present recent advances of lipid analysis in drug discovery and development. We cover current state of the art technologies which are constantly evolving to meet demands in terms of sensitivity and selectivity. A careful selection of important examples is then provided, illustrating the versatility of lipidomics analysis in the drug discovery and development process. Integration of lipidomics with other omics', stem-cell technologies, and metabolic flux analysis will open new avenues for deciphering pathophysiological mechanisms and the discovery of novel targets and biomarkers.

Keywords:  Biomarker; Drug development; Drug discovery; Lipidomics; Mass spectrometry

DOI:  https://doi.org/10.1016/j.cbpa.2022.102256
J Proteome Res. 2023 Jan 05.

Optimization of Higher-Energy Collisional Dissociation Fragmentation Energy for Intact Protein-Level Tandem Mass Tag Labeling.

Yanting Guo, Trishika Chowdhury, Meena Seshadri, Kellye A Cupp-Sutton, Qingyu Wang, Dahang Yu, Si Wu.

  Isobaric chemical tag labeling (e.g., TMT) is a commonly used approach in quantitative proteomics, and quantification is enabled through detection of low-mass reporter ions generated after MS2 fragmentation. Recently, we have introduced and optimized an intact protein-level TMT labeling platform that demonstrated >90% labeling efficiency in complex samples with top-down proteomics. Higher-energy collisional dissociation (HCD) is commonly utilized for isobaric tag-labeled peptide fragmentation because it produces accurate reporter ion intensities and avoids loss of low mass ions. HCD energies have been optimized for isobaric tag labeled-peptides but have not been systematically evaluated for isobaric tag-labeled intact proteins. In this study, we report a systematic evaluation of normalized HCD fragmentation energies (NCEs) on TMT-labeled HeLa cell lysate using top-down proteomics. Our results suggested that reporter ions often result in higher ion intensities at higher NCEs. Optimal fragmentation of intact proteins for identification, however, required relatively lower NCE. We further demonstrated that a stepped NCE scheme with energies from 30% to 50% resulted in optimal quantification and identification of TMT-labeled HeLa proteins. These parameters resulted in an average reporter ion intensity of ∼4E4 and average proteoform spectrum matches (PrSMs) of >1000 per RPLC-MS/MS run with a 1% false discovery rate (FDR) cutoff.

Keywords:  higher-energy collisional dissociation; protein-level TMT labeling; quantitative proteomics; stepped HCD scheme; top-down proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.2c00549
Anal Chem. 2023 Jan 02.

Rapid Multi-Omics Sample Preparation for Mass Spectrometry.

Laura K Muehlbauer, Annie Jen, Yunyun Zhu, Yuchen He, Evgenia Shishkova, Katherine A Overmyer, Joshua J Coon.

Multi-omics analysis is a powerful and increasingly utilized approach to gain insight into complex biological systems. One major hindrance with multi-omics, however, is the lengthy and wasteful sample preparation process. Preparing samples for mass spectrometry (MS)-based multi-omics involves extraction of metabolites and lipids with organic solvents, precipitation of proteins, and overnight digestion of proteins. These existing workflows are disparate and laborious. Here, we present a simple, efficient, and unified approach to prepare lipids, metabolites, and proteins for MS analysis. Our approach, termed the Bead-enabled Accelerated Monophasic Multi-omics (BAMM) method, combines an n-butanol-based monophasic extraction with unmodified magnetic beads and accelerated protein digestion. We demonstrate that the BAMM method affords comparable depth, quantitative reproducibility, and recovery of biomolecules as state-of-the-art multi-omics methods (e.g., Matyash extraction and overnight protein digestion). However, the BAMM method only requires about 3 h to perform, which saves 11 steps and 19 h on average compared to published multi-omics methods. Furthermore, we validate the BAMM method for multiple sample types and formats (biofluid, culture plate, and pellet) and show that in all cases, it produces high biomolecular coverage and data quality.

DOI: https://doi.org/10.1021/acs.analchem.2c02042
Anal Chem. 2023 Jan 04.

AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification.

Xing-Cai Wang, Jia-Ni Zhang, Juan-Juan Zhao, Xiao-Meng Guo, Shu-Fang Li, Qing-Xia Zheng, Ping-Ping Liu, Peng Lu, Hai-Yan Fu, Yong-Jie Yu, Yuanbin She.

Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS1 and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS1 spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS1 and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.

DOI: https://doi.org/10.1021/acs.analchem.2c01795
Methods Mol Biol. 2023 ;2613 289-299

Comprehensive Glycan Analysis of Sphingolipids in Human Serum/Plasma.

Hisatoshi Hanamatsu, Takashi Nishikaze, Jun-Ichi Furukawa.

  Glycosphingolipids (GSLs) are glycolipids with ceramide and carbohydrate head groups that play an important role in numerous biological processes. Previously, we performed GSL-glycan analysis of various cell lines and virus-infected cells using a glycoblotting approach. Recently, we developed several methods for sialic acid linkage-specific chemical modification to distinguish sialylated glycan isomers by mass spectrometry. In this chapter, we describe a method for analyzing GSL-glycans in human serum/plasma using glycoblotting combined with aminolysis-SALSA (sialic acid linkage-specific alkylamidation) and lactone-driven ester-to-amide derivatization (LEAD)-SALSA for comprehensive and detailed structural glycomics.

Keywords:  Glycoblotting; Glycosphingolipid-glycan; Human plasma; Human serum; Lactone-driven ester-to-amide derivatization; Mass spectrometry; Sialic acid linkage-specific alkylamidation

DOI:  https://doi.org/10.1007/978-1-0716-2910-9_21
Clin Chem Lab Med. 2023 Jan 04.

Clinical lipidomics in the era of the big data.

Aleš Kvasnička, Lukáš Najdekr, Dana Dobešová, Barbora Piskláková, Eliška Ivanovová, David Friedecký.

  Lipidomics as a branch of metabolomics provides unique information on the complex lipid profile in biological materials. In clinically focused studies, hundreds of lipids together with available clinical information proved to be an effective tool in the discovery of biomarkers and understanding of pathobiochemistry. However, despite the introduction of lipidomics nearly twenty years ago, only dozens of big data studies using clinical lipidomics have been published to date. In this review, we discuss the lipidomics workflow, statistical tools, and the challenges of standartisation. The consequent summary divided into major clinical areas of cardiovascular disease, cancer, diabetes mellitus, neurodegenerative and liver diseases is demonstrating the importance of clinical lipidomics. In these publications, the potential of lipidomics for prediction, diagnosis or finding new targets for the treatment of selected diseases can be seen. The first of these results have already been implemented in clinical practice in the field of cardiovascular diseases, while in other areas we can expect the application of the results summarized in this review in the near future.

Keywords:  big data; clinical lipidomics; cohorts; large-scale

DOI:  https://doi.org/10.1515/cclm-2022-1105
Cell Metab. 2023 Jan 03. pii: S1550-4131(22)00544-7. [Epub ahead of print]35(1): 5-7

Illuminating lactate in cells, mice, and patient samples.

Zefan Li, Hui-Wang Ai.

Lactate has emerged as a central metabolic fuel and an important signaling molecule. In this issue of Cell Metabolism, Li et al. develop a high-quality lactate sensor, allowing them to monitor lactate levels in cells, subcellular organelles, live mice, and human body fluids.

DOI: https://doi.org/10.1016/j.cmet.2022.12.007
Front Cell Dev Biol. 2022 ;10 1075810

Screening and diagnosis of triple negative breast cancer based on rapid metabolic fingerprinting by conductive polymer spray ionization mass spectrometry and machine learning.

Yaoyao Song, Yan Zhang, Songhai Xie, Xiaowei Song.

  We present the use of conductive spray polymer ionization mass spectrometry (CPSI-MS) combined with machine learning (ML) to rapidly gain the metabolic fingerprint from 1 μl liquid extraction from the biopsied tissue of triple-negative breast cancer (TNBC) in China. The 76 discriminative metabolite markers are verified at the primary carcinoma site and can also be successfully tracked in the serum. The Lasso classifier featured with 15- and 22-metabolites detected by CPSI-MS achieve a sensitivity of 88.8% for rapid serum screening and a specificity of 91.1% for tissue diagnosis, respectively. Finally, the expression levels of their corresponding upstream enzymes and transporters have been initially confirmed. In general, CPSI-MS/ML serves as a cost-effective tool for the rapid screening, diagnosis, and precise characterization for the TNBC metabolism reprogramming in the clinical practice.

Keywords:  ambient ionization mass spectrometry; biomarkers; clinical screening and diagnosis; metabolomics; triple-negative breast cancer

DOI:  https://doi.org/10.3389/fcell.2022.1075810
ACS Omega. 2022 Dec 27. 7(51): 47806-47811

Evaluation of the Potential Risk of Analyzing Phosphopeptides with Easy-nLC 1200-Coupled Mass Spectrometers.

Xiaoxiao Duan, Yuanya Zhang, Xiahe Huang, Xiao Ma, Hui Gao, Yingchun Wang.

Liquid chromatography-mass spectrometry (LC-MS) is a major tool for the large-scale qualitative and/or quantitative analysis of protein phosphorylation in cells or tissues. The performance of LC is pivotal for the success of phosphoproteomics in both sensitivity and reproducibility. Here, we report that the widely used Easy-nLC 1200 has poor performance in analyzing phosphopeptides, particularly in terms of sensitivity and reproducibility, whereas its predecessor, Easy-nLC 1000, has a much better performance. Therefore, we suggest that Easy-nLC 1200 is not appropriate for LC-MS-based proteomics analysis for samples with a limited amount, particularly phosphopeptides from plants.

DOI: https://doi.org/10.1021/acsomega.2c05616
N Am Spine Soc J. 2023 Mar;13 100191

Nutrient metabolism of the nucleus pulposus: A literature review.

Joe Kodama, Kevin J Wilkinson, Satoru Otsuru.

  Cells take in, consume, and synthesize nutrients for numerous physiological functions. This includes not only energy production but also macromolecule biosynthesis, which will further influence cellular signaling, redox homeostasis, and cell fate commitment. Therefore, alteration in cellular nutrient metabolism is associated with pathological conditions. Intervertebral discs, particularly the nucleus pulposus (NP), are avascular and exhibit unique metabolic preferences. Clinical and preclinical studies have indicated a correlation between intervertebral degeneration (IDD) and systemic metabolic diseases such as diabetes, obesity, and dyslipidemia. However, a lack of understanding of the nutrient metabolism of NP cells is masking the underlying mechanism. Indeed, although previous studies indicated that glucose metabolism is essential for NP cells, the downstream metabolic pathways remain unknown, and the potential role of other nutrients, like amino acids and lipids, is understudied. In this literature review, we summarize the current understanding of nutrient metabolism in NP cells and discuss other potential metabolic pathways by referring to a human NP transcriptomic dataset deposited to the Gene Expression Omnibus, which can provide us hints for future studies of nutrient metabolism in NP cells and novel therapies for IDD.

Keywords:  Amino acid; Degeneration; Glucose; Intervertebral disc; Lipid; Metabolism; Nucleus pulposus

DOI:  https://doi.org/10.1016/j.xnsj.2022.100191
J Clin Invest. 2023 Jan 03. pii: e163448. [Epub ahead of print]133(1):

Targeting fatty acid metabolism in glioblastoma.

Jason Miska, Navdeep S Chandel.

Glioblastoma (GBM) is a primary tumor of the brain defined by its uniform lethality and resistance to conventional therapies. There have been considerable efforts to untangle the metabolic underpinnings of this disease to find novel therapeutic avenues for treatment. An emerging focus in this field is fatty acid (FA) metabolism, which is critical for numerous diverse biological processes involved in GBM pathogenesis. These processes can be classified into four broad fates: anabolism, catabolism, regulation of ferroptosis, and the generation of signaling molecules. Each fate provides a unique perspective by which we can inspect GBM biology and gives us a road map to understanding this complicated field. This Review discusses the basic, translational, and clinical insights into each of these fates to provide a contemporary understanding of FA biology in GBM. It is clear, based on the literature, that there are far more questions than answers in the field of FA metabolism in GBM, and substantial efforts should be made to untangle these complex processes in this intractable disease.

DOI: https://doi.org/10.1172/JCI163448
STAR Protoc. 2022 Dec 16. pii: S2666-1667(22)00799-7. [Epub ahead of print]3(4): 101919

MATRIX platform to analyze translation machinery remodeling in glioblastoma cells.

J J David Ho, Tyler A Cunningham, Jonathan R Krieger, Jonathan H Schatz, Stephen Lee.

  Here, we present a protocol using MATRIX (mass spectrometry analysis of active translation factors using ribosome density fractionation and isotopic labeling experiments) platform to investigate changes of the protein synthesis machinery in U87MG glioblastoma cells in response to the rocaglate silvestrol. This protocol describes steps to perform SILAC (stable isotope labeling by amino acids in cell culture), ribosome density fractionation, protein isolation, and mass spectrometry analysis. This approach can be applied to study any adaptive remodeling of protein synthesis machineries. For complete details on the use and execution of this protocol, please refer to Ho et al. (2021).1.

Keywords:  Cancer; Mass Spectrometry; Protein Biochemistry; Proteomics

DOI:  https://doi.org/10.1016/j.xpro.2022.101919
Anal Methods. 2023 Jan 05.

A novel screening method for free non-standard amino acids in human plasma samples using AccQ·Tag reagents and LC-MS/MS.

Daniel H J Ng, Li Yan Chan, Laura Fitzner, Julia Katharina Keppler, Shareef M Ismail, Simon Hird, Peter Hancock, Schwarz Karin, Demetrowitsch Tobias.

There are at least 500 naturally occurring amino acids, of which only 20 standard proteinogenic amino acids are used universally across all organisms in the synthesis of peptides and proteins. Non-standard amino acids can be incorporated into proteins or are intermediates and products of metabolic pathways. While the analysis of standard amino acids is well-defined, the analysis of non-standard amino acids can be challenging due to the wide range of physicochemical properties, and the lack of both reference standards and information in curated databases to aid compound identification. It has been shown that the use of an AccQ·Tag™ derivatization kit along with LC-MS/MS is an attractive option for the analysis of free standard amino acids in complex samples because it is fast, sensitive, reproducible, and selective. It has been demonstrated that the most abundant quantitative transition for MS/MS analysis of 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) derivatized amino acids corresponds to the fragmentation of the molecule at the 6-aminoquinoline carbonyl group producing a common m/z 171 fragment ion and occurs at similar mass spectrometry collision energy and cone voltages. In this study, the unique properties of AQC derivatized amino acids producing high intensity common fragment ions, along with chromatographic separation of amino acids under generic chromatography conditions, were used to develop a novel screening method for the detection of trace levels of non-standard amino acids in complex matrices. Structural elucidation was carried out by comparing the MS/MS fragment ion mass spectra generated with in silico predicted fragmentation spectra to enable a putative identification, which was confirmed using an appropriate analytical standard. This workflow was applied to screen human plasma samples for bioactive thiol-group modified cysteine amino acids and S-allylmercaptocysteine (SAMC), S-allylcysteine sulfoxide (SACS or alliin) and S-propenylcysteine (S1PC) are reported for the first time to be present in human plasma samples after the administration of garlic supplements.

DOI: https://doi.org/10.1039/d2ay01588a
Methods Mol Biol. 2023 ;2613 127-144

Mass Spectrometry of Neutral Glycosphingolipids.

Akemi Suzuki, Atit Silsirivanit, Takashi Watanabe, Junko Matsuda, Kei-Ichiro Inamori, Jin-Ichi Inokuchi.

  This chapter describes the protocols for mass spectrometry (MS) applied to the structural characterization of neutral glycosphingolipids (GSLs) and the determination of neutral GSL contents in biological materials. The structural characterization is performed by thin layer chromatography-matrix assisted laser desorption ionization/mass spectrometry (TLC-MALDI/MS) and liquid chromatography-electrospray ionization/mass spectrometry (LC-ESI/MS) with reversed phase separation. The content determination is carried out by LC-ESI/MS with multiple reaction monitoring (MRM). These protocols provide clues for the functions of neutral GSLs at the level of a single GSL molecular species.

Keywords:  Ceramide structures; Glycan sequences; Glycosphingolipids (GSLs); Liquid chromatography-electrospray ionization mass spectrometry (LC-ESI/MS); Mass spectrometry (MS); Multiple reaction monitoring (MRM); Reversed phase liquid chromatography; Thin layer chromatography-matrix assisted laser desorption ionization/mass spectrometry (TLC-MALDI/MS)

DOI:  https://doi.org/10.1007/978-1-0716-2910-9_11
Front Oncol. 2022 ;12 1085034

Lipid droplets and ferroptosis as new players in brain cancer glioblastoma progression and therapeutic resistance.

Ayenachew Bezawork-Geleta, James Dimou, Matthew J Watt.

  A primary brain tumor glioblastoma is the most lethal of all cancers and remains an extremely challenging disease. Apparent oncogenic signaling in glioblastoma is genetically complex and raised at any stage of the disease's progression. Many clinical trials have shown that anticancer drugs for any specific oncogene aberrantly expressed in glioblastoma show very limited activity. Recent discoveries have highlighted that alterations in tumor metabolism also contribute to disease progression and resistance to current therapeutics for glioblastoma, implicating an alternative avenue to improve outcomes in glioblastoma patients. The roles of glucose, glutamine and tryptophan metabolism in glioblastoma pathogenesis have previously been described. This article provides an overview of the metabolic network and regulatory changes associated with lipid droplets that suppress ferroptosis. Ferroptosis is a newly discovered type of nonapoptotic programmed cell death induced by excessive lipid peroxidation. Although few studies have focused on potential correlations between tumor progression and lipid droplet abundance, there has recently been increasing interest in identifying key players in lipid droplet biology that suppress ferroptosis and whether these dependencies can be effectively exploited in cancer treatment. This article discusses how lipid droplet metabolism, including lipid synthesis, storage, and use modulates ferroptosis sensitivity or tolerance in different cancer models, focusing on glioblastoma.

Keywords:  brain cancer; cell death; glioma; lipid droplet (LD); lipids; metabolism; therapeutic vulnerabilities

DOI:  https://doi.org/10.3389/fonc.2022.1085034
J Biol Chem. 2022 Dec 29. pii: S0021-9258(22)01291-1. [Epub ahead of print] 102848

Carnitine octanoyltransferase is important for the assimilation of exogenous acetyl-L-carnitine into acetyl-CoA in mammalian cells.

Jake Hsu, Nina Fatuzzo, Nielson Weng, Wojciech Michno, Wentao Dong, Maryline Kienle, Yuqin Dai, Anca Pasca, Monther Abu-Remaileh, Natalie Rasgon, Benedetta Bigio, Carla Nasca, Chaitan Khosla.

  In eukaryotes carnitine is best known for its ability to shuttle esterified fatty acids across mitochondrial membranes for β-oxidation. It also returns to the cytoplasm, in the form of acetyl-L-carnitine (LAC), some of the resulting acetyl groups for post-translational protein modification and lipid biosynthesis. While dietary LAC supplementation has been clinically investigated, its effects on cellular metabolism are not well understood. To explain how exogenous LAC influences mammalian cell metabolism, we synthesized isotope-labeled forms of LAC and its analogs. In cultures of glucose-limited U87MG glioma cells, exogenous LAC contributed more robustly to intracellular acetyl-CoA pools than did β-hydroxybutyrate, the predominant circulating ketone body in mammals. The fact that most LAC-derived acetyl-CoA is cytosolic is evident from strong labeling of fatty acids in U87MG cells by exogenous 13C2-acetyl-L-carnitine. We found that the addition of d3-acetyl-L-carnitine increases the supply of acetyl-CoA for cytosolic post-translational modifications due to its strong kinetic isotope effect on acetyl-CoA carboxylase, the first committed step in fatty acid biosynthesis. Surprisingly, whereas cytosolic carnitine acetyltransferase (CRAT) is believed to catalyze acetyl group transfer from LAC to Coenzyme A, CRAT-/- U87MG cells were unimpaired in their ability to assimilate exogenous LAC into acetyl-CoA. We identified carnitine octanoyltransferase (CROT) as the key enzyme in this process, implicating a role for peroxisomes in efficient LAC utilization. Our work has opened the door to further biochemical investigations of a new pathway for supplying acetyl-CoA to certain glucose-starved cells.

Keywords:  acetyl coenzyme A (acetyl‐CoA); acetylcarnitine; cell metabolism; energy metabolism; enzyme turnover; metabolic regulation; mitochondrial metabolism; peroxisome

DOI:  https://doi.org/10.1016/j.jbc.2022.102848
Nat Rev Cancer. 2023 Jan 03.

STAT proteins in cancer: orchestration of metabolism.

Yi-Jia Li, Chunyan Zhang, Antons Martincuks, Andreas Herrmann, Hua Yu.

Reprogrammed metabolism is a hallmark of cancer. However, the metabolic dependency of cancer, from tumour initiation through disease progression and therapy resistance, requires a spectrum of distinct reprogrammed cellular metabolic pathways. These pathways include aerobic glycolysis, oxidative phosphorylation, reactive oxygen species generation, de novo lipid synthesis, fatty acid β-oxidation, amino acid (notably glutamine) metabolism and mitochondrial metabolism. This Review highlights the central roles of signal transducer and activator of transcription (STAT) proteins, notably STAT3, STAT5, STAT6 and STAT1, in orchestrating the highly dynamic metabolism not only of cancer cells but also of immune cells and adipocytes in the tumour microenvironment. STAT proteins are able to shape distinct metabolic processes that regulate tumour progression and therapy resistance by transducing signals from metabolites, cytokines, growth factors and their receptors; defining genetic programmes that regulate a wide range of molecules involved in orchestration of metabolism in cancer and immune cells; and regulating mitochondrial activity at multiple levels, including energy metabolism and lipid-mediated mitochondrial integrity. Given the central role of STAT proteins in regulation of metabolic states, they are potential therapeutic targets for altering metabolic reprogramming in cancer.

DOI: https://doi.org/10.1038/s41568-022-00537-3