bims-mascan 2021-11-28 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2021–11–28
34 papers selected by
Giovanny Rodríguez Blanco, University of Edinburgh

Opti-nQL: An Optimized, Versatile and Sensitive Nano-LC Method for MS-Based Lipidomics Analysis.
The Hitchhiker's Guide to Untargeted Lipidomics Analysis: Practical Guidelines.
Review of Recent Advances in Lipid Analysis of Biological Samples via Ambient Ionization Mass Spectrometry.
Mass Spectrometry-Based Metabolomic and Lipidomic Analysis of the Effect of High Fat/High Sugar Diet and GreenshellTM Mussel Feeding on Plasma of Ovariectomized Rats.
Quantitative structure-retention relationship for reliable metabolite identification and quantification in metabolomics using ion-pair reversed-phase chromatography coupled with tandem mass spectrometry.
Succinate receptor 1 inhibits mitochondrial respiration in cancer cells addicted to glutamine.
Automated Multidimensional Nanoscale Chromatography for Ultrasensitive Targeted Mass Spectrometry.
PALP: A rapid imaging technique for stratifying ferroptosis sensitivity in normal and tumor tissues in situ.
Rapid sample preparation workflow based on enzymatic nanoreactors for potential serum biomarker discovery in pancreatic cancer.
Plasma Metabolomics of Intermediate and Neovascular Age-Related Macular Degeneration Patients.
Mitochondria-Mediated Apoptosis of HCC Cells Triggered by Knockdown of Glutamate Dehydrogenase 1: Perspective for Its Inhibition through Quercetin and Permethylated Anigopreissin A.
Metabolism, HDACs, and HDAC Inhibitors: A Systems Biology Perspective.
Untargeted Lipidomics of Non-Small Cell Lung Carcinoma Demonstrates Differentially Abundant Lipid Classes in Cancer vs. Non-Cancer Tissue.
An Asp to Strike Out Cancer? Therapeutic Possibilities Arising from Aspartate's Emerging Roles in Cell Proliferation and Survival.
Comprehensive Metabolomics and Lipidomics Profiling of Prostate Cancer Tissue Reveals Metabolic Dysregulations Associated with Disease Development.
Metabonomics study of the effects of single copy mutant KRAS in the presence or absence of WT allele using human HCT116 isogenic cell lines.
Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches.
Fatty Acid Metabolism Reprogramming in Advanced Prostate Cancer.
Multiple Infusion Start Time Mass Spectrometry Imaging of Dynamic SIL-Glutathione Biosynthesis Using Infrared Matrix-Assisted Laser Desorption Electrospray Ionization.
Investigating the K+ sensitivity of cellular metabolism by extracellular flux analysis.
Metabolic stress induces GD2+ cancer stem cell-like phenotype in triple-negative breast cancer.
Endothelial Heme Dynamics Drive Cancer Cell Metabolism by Shaping the Tumor Microenvironment.
Scaling up single-cell proteomics.
Targeted Quantification of Carbon Metabolites Identifies Metabolic Progression Markers and an Undiagnosed Case of SDH-Deficient Clear Cell Renal Cell Carcinoma in a German Cohort.
Close interactions between lncRNAs, lipid metabolism and ferroptosis in cancer.
The influence of dexamethasone on hepatic fatty acids metabolism and transport in human steatotic HepG2 cell line exposed to palmitate.
Statins affect cancer cell plasticity with distinct consequences for tumor progression and metastasis.
Mass spectrometry in clinical glycomics: The path from biomarker identification to clinical implementation.
Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification.
A proposal for score assignment to characterize biological processes from mass spectral analysis of serum.
The clinical translation of eicosanoids and other oxylipins, although challenging, should be actively pursued.
Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers.
More sensitivity is always better: Measuring sub-clinical levels of serum thyroglobulin on a µLC-MS/MS system.
Developing a robust, fast and reliable measurement method for the analysis of methylarginine derivatives and related metabolites.

Metabolites. 2021 Oct 21. pii: 720. [Epub ahead of print]11(11):

Opti-nQL: An Optimized, Versatile and Sensitive Nano-LC Method for MS-Based Lipidomics Analysis.

Angela Cattaneo, Giuseppe Martano, Umberto Restuccia, Laura Tronci, Michele Bianchi, Angela Bachi, Vittoria Matafora.

  Lipidomics is the comprehensive analysis of lipids in a given biological system. This investigation is often limited by the low amount and high complexity of biological samples, therefore highly sensitive lipidomics methods are required. Nanoflow-LC/MS offers extremely high sensitivity; however, it is challenging as a more demanding maintenance is often needed compared to conventional microflow-LC approaches. Here, we developed a sensitive and reproducible lipidomics LC method, termed Opti-nQL, which can be applied to any biological system. Opti-nQL has been validated with cellular lipid extracts of human and mouse origin and with different lipid extraction methods. Among the resulting 4000 detected features, 700 and even more unique lipid molecular species have been identified covering 16 lipid sub-classes, while 400 lipids were uniquely structure defined by MS/MS. These results were obtained by analyzing an amount of lipids extract equivalent to 40 ng of proteins, being highly suitable for low abundant samples. MS analysis showed that theOpti-nQL method increases the number of identified lipids, which is evidenced by injecting 20 times less material than in microflow based chromatography, being more reproducible and accurate thus enhancing robustness of lipidomics analysis.

Keywords:  lipid species; lipidomics; nano-LC-MS/MS; quantitative analysis; sensitivity

DOI:  https://doi.org/10.3390/metabo11110720
Metabolites. 2021 Oct 20. pii: 713. [Epub ahead of print]11(11):

The Hitchhiker's Guide to Untargeted Lipidomics Analysis: Practical Guidelines.

Dmitrii Smirnov, Pavel Mazin, Maria Osetrova, Elena Stekolshchikova, Ekaterina Khrameeva.

  Lipidomics is a newly emerged discipline involving the identification and quantification of thousands of lipids. As a part of the omics field, lipidomics has shown rapid growth both in the number of studies and in the size of lipidome datasets, thus, requiring specific and efficient data analysis approaches. This paper aims to provide guidelines for analyzing and interpreting lipidome data obtained using untargeted methods that rely on liquid chromatography coupled with mass spectrometry (LC-MS) to detect and measure the intensities of lipid compounds. We present a state-of-the-art untargeted LC-MS workflow for lipidomics, from study design to annotation of lipid features, focusing on practical, rather than theoretical, approaches for data analysis, and we outline possible applications of untargeted lipidomics for biological studies. We provide a detailed R notebook designed specifically for untargeted lipidome LC-MS data analysis, which is based on xcms software.

Keywords:  LC-MS; bioinformatics; lipidome

DOI:  https://doi.org/10.3390/metabo11110713
Metabolites. 2021 Nov 15. pii: 781. [Epub ahead of print]11(11):

Review of Recent Advances in Lipid Analysis of Biological Samples via Ambient Ionization Mass Spectrometry.

Haiyan Lu, Hua Zhang, Shuling Xu, Lingjun Li.

  The rapid and direct structural characterization of lipids proves to be critical for studying the functional roles of lipids in many biological processes. Among numerous analytical techniques, ambient ionization mass spectrometry (AIMS) allows for a direct molecular characterization of lipids from various complex biological samples with no/minimal sample pretreatment. Over the recent years, researchers have expanded the applications of the AIMS techniques to lipid structural elucidation via a combination with a series of derivatization strategies (e.g., the Paternò-Büchi (PB) reaction, ozone-induced dissociation (OzID), and epoxidation reaction), including carbon-carbon double bond (C=C) locations and sn-positions isomers. Herein, this review summarizes the reaction mechanisms of various derivatization strategies for C=C bond analysis, typical instrumental setup, and applications of AIMS in the structural elucidation of lipids from various biological samples (e.g., tissues, cells, and biofluids). In addition, future directions of AIMS for lipid structural elucidation are discussed.

Keywords:  ambient ionization mass spectrometry; biological samples; lipidomics; structural elucidation

DOI:  https://doi.org/10.3390/metabo11110781
Metabolites. 2021 Oct 31. pii: 754. [Epub ahead of print]11(11):

Mass Spectrometry-Based Metabolomic and Lipidomic Analysis of the Effect of High Fat/High Sugar Diet and GreenshellTM Mussel Feeding on Plasma of Ovariectomized Rats.

Maryam Abshirini, Diana Cabrera, Karl Fraser, Parkpoom Siriarchavatana, Frances M Wolber, Matthew R Miller, Hong Sabrina Tian, Marlena C Kruger.

  This study aimed to examine the changes in lipid and metabolite profiles of ovariectomized (OVX) rats with diet-induced metabolic syndrome-associated osteoarthritis (MetOA) after supplementation with greenshell mussel (GSM) using an untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics approach. Ninety-six rats were fed with one of four diets: control, control supplemented with GSM + GSM, high fat/high sugar (HFHS), or high fat/high sugar enriched with GSM (HFHS + GSM). After 8 weeks on experimental diets, half of the rats in each group underwent OVX and the other half were sham operated. After being fed for an additional 28 weeks, blood samples were collected for the metabolomics analysis. Lipid and polar metabolites were extracted from plasma and analysed by LC-MS. We identified 29 lipid species from four lipid subclasses (phosphatidylcholine, lysophosphatidylcholine, diacylglycerol, and triacylglycerol) and a set of eight metabolites involved in amino acid metabolism (serine, threonine, lysine, valine, histidine, pipecolic acid, 3-methylcytidine, and cholic acid) as potential biomarkers for the effect of HFHS diet and GSM supplementation. GSM incorporation more specifically in the control diet generated significant alterations in the levels of several lipids and metabolites. Further studies are required to validate these findings that identify potential biomarkers to follow OA progression and to monitor the impact of GSM supplementation.

Keywords:  diet-induced obese rats; lipidomic; metabolic dysregulation; metabolomics; osteoarthritis

DOI:  https://doi.org/10.3390/metabo11110754
Talanta. 2021 Nov 17. pii: S0039-9140(21)00981-4. [Epub ahead of print]238(Pt 2): 123059

Quantitative structure-retention relationship for reliable metabolite identification and quantification in metabolomics using ion-pair reversed-phase chromatography coupled with tandem mass spectrometry.

Qingyu Hu, Yuting Sun, Peihong Yuan, Hehua Lei, Huiqin Zhong, Yulan Wang, Huiru Tang.

  Hydrophilic metabolites are essential for all biological systems with multiple functions and their quantitative analysis forms an important part of metabolomics. However, poor retention of these metabolites on reversed-phase (RP) chromatographic column hinders their effective analysis with RPLC-MS methods. Herein, we developed a method for detecting hydrophilic metabolites using the ion-pair reversed-phase liquid-chromatography coupled with mass spectrometry (IPRP-LC-MS/MS) in scheduled multiple-reaction-monitoring (sMRM) mode. We first developed a hexylamine-based IPRP-UHPLC-QTOFMS method and experimentally measured retention time (tR) for 183 hydrophilic metabolites. We found that tRs of these metabolites were dominated by their electrostatic potential depending upon the numbers and types of their ionizable groups. We then systematically investigated the quantitative structure-retention relationship (QSRR) and constructed QSRR models using the measured tR. Subsequently, we developed a retention time predictive model using the random-forest regression algorithm (r2 = 0.93, q2 = 0.70, MAE = 1.28 min) for predicting metabolite retention time, which was applied in IPRP-UHPLC-MS/MS method in sMRM mode for quantitative metabolomic analysis. Our method can simultaneously quantify more than 260 metabolites. Moreover, we found that this method was applicable for multiple major biological matrices including biofluids and tissues. This approach offers an efficient method for large-scale quantitative hydrophilic metabolomic profiling even when metabolite standards are unavailable.

Keywords:  Ion-pair reversed-phase chromatography; Metabolomics; Quantitative structure-retention relationship; Scheduled MRM; UHPLC-MS

DOI:  https://doi.org/10.1016/j.talanta.2021.123059
Cancer Lett. 2021 Nov 20. pii: S0304-3835(21)00591-7. [Epub ahead of print]

Succinate receptor 1 inhibits mitochondrial respiration in cancer cells addicted to glutamine.

Philipp Rabe, Aenne-Dorothea Liebing, Petra Krumbholz, Robert Kraft, Claudia Stäubert.

  Cancer cells display metabolic alterations to meet the bioenergetic demands for their high proliferation rates. Succinate is a central metabolite of the tricarboxylic acid (TCA) cycle, but was also shown to act as an oncometabolite and to specifically activate the succinate receptor 1 (SUCNR1), which is expressed in several types of cancer. However, functional studies focusing on the connection between SUCNR1 and cancer cell metabolism are still lacking. In the present study, we analyzed the role of SUCNR1 for cancer cell metabolism and survival applying different signal transduction, metabolic and imaging analyses. We chose a gastric, a lung and a pancreatic cancer cell line for which our data revealed functional expression of SUCNR1. Further, presence of glutamine (Gln) caused high respiratory rates and elevated expression of SUCNR1. Knockdown of SUCNR1 resulted in a significant increase of mitochondrial respiration and superoxide production accompanied by an increase in TCA cycle throughput and a reduction of cancer cell survival in the analyzed cancer cell lines. Combination of SUCNR1 knockdown and treatment with the chemotherapeutics cisplatin and gemcitabine further increased cancer cell death. In summary, our data implicates that SUCNR1 is crucial for Gln-addicted cancer cells by limiting TCA cycle throughput, mitochondrial respiration and the production of reactive oxygen species, highlighting its potential as a pharmacological target for cancer treatment.

Keywords:  Cancer metabolism; GPR91; Glutaminolysis; Metabolite-sensing GPCR; SUCNR1

DOI:  https://doi.org/10.1016/j.canlet.2021.11.024
Methods Mol Biol. 2022 ;2393 207-224

Automated Multidimensional Nanoscale Chromatography for Ultrasensitive Targeted Mass Spectrometry.

Paolo Cifani, Alex Kentsis.

  Recent advances in nanoscale separations and high-resolution mass spectrometry permit highly sensitive and accurate analyses of complex protein mixtures. Here, we describe improved methods for nanoscale multidimensional chromatography coupled to targeted mass spectrometry (tMS) to achieve ultrasensitive quantification of peptides in complex proteomes. The presented chromatographic system consists of capillary strong-cation exchange (SCX) chromatography column, from which peptides are eluted directly onto high-resolution reversed-phase (RP) analytical columns and nanoelectrospray ion source. SCX prefractionation is used to separate phosphorylated peptides, permitting their ultrasensitive quantification. Resolution and robustness of this chromatographic system, together with the orthogonality of SCX and RP separations, permit scheduling of large panels of targeted MS assays. This design also enables seamless scaling to three-dimensional separations, thereby enabling large-scale, ultrasensitive quantitative proteomics.

Keywords:  Accumulated ion monitoring; Chromatography; Electrospray ionization; Mass spectrometry; Multidimensional chromatography; Nanoscale separation; Proteomics; Reversed phase; Strong cation exchange

DOI:  https://doi.org/10.1007/978-1-0716-1803-5_11
Cell Chem Biol. 2021 Nov 18. pii: S2451-9456(21)00478-5. [Epub ahead of print]

PALP: A rapid imaging technique for stratifying ferroptosis sensitivity in normal and tumor tissues in situ.

Fengxiang Wang, Emily T Graham, Nathchar Naowarojna, Zhennan Shi, Yuqi Wang, Guanglei Xie, Lili Zhou, Wendy Salmon, Jie-Min Jia, Xi Wang, Yuwei Huang, Stuart L Schreiber, Yilong Zou.

  Ferroptosis is an emerging cancer suppression strategy. However, how to select cancer patients for treating with ferroptosis inducers remains challenging. Here, we develop photochemical activation of membrane lipid peroxidation (PALP), which uses targeted lasers to induce localized polyunsaturated fatty acyl (PUFA)-lipid peroxidation for reporting ferroptosis sensitivity in cells and tissues. PALP captured by BODIPY-C11 can be suppressed by lipophilic antioxidants and iron chelation, and is dependent on PUFA-lipid levels. Moreover, we develop PALPv2, for studying lipid peroxidation on selected membranes along the z axis in live cells using two-photon microscopes. Using PALPv1, we detect PUFA-lipids in multiple tissues, and validate a PUFA-phospholipid reduction during muscle aging as previously reported. Patterns of PALPv1 signals across multiple cancer cell types in vitro and in vivo are concordant with their ferroptosis susceptibility and PUFA-phospholipid levels. We envision that PALP will enable rapid stratification of ferroptosis sensitivity in cancer patients and facilitate PUFA-lipid research.

Keywords:  cancer; ferroptosis sensitivity stratification; imaging; in situ quantitation; lipid peroxidation; polyunsaturated lipid

DOI:  https://doi.org/10.1016/j.chembiol.2021.11.001
Talanta. 2021 Nov 05. pii: S0039-9140(21)00940-1. [Epub ahead of print]238(Pt 2): 123018

Rapid sample preparation workflow based on enzymatic nanoreactors for potential serum biomarker discovery in pancreatic cancer.

Chenxin Zhu, Shuang Yang, Hengchao Li, Yuning Wang, Yueting Xiong, Fenglin Shen, Lei Zhang, Pengyuan Yang, Xiaohui Liu.

  Mass spectrometry (MS)-based proteomics have been extensively applied in clinical practice to discover potential protein and peptide biomarkers. However, the traditional sample pretreatment workflow remains labor-intensive and time-consuming, which limits the application of MS-based proteomic biomarker discovery studies in a high throughput manner. In the current work, we improved the previously reported procedure of the simple and rapid sample preparation methods (RSP) by introducing macroporous ordered siliceous foams (MOSF), namely RSP-MOSF. With the aid of MOSF, we further reduced the digestion time to 10 min, facilitating the whole sample handling process within 30 min. Combining with 30 min direct data independent acquisition (DIA) of LC-MS/MS, we accomplished a serum sample analysis in 1 h. Comparing with the RSP method, the performance of protein and peptide identification, quantitation, as well as the reproducibility of RSP-MOSF is comparable or even outperformed the RSP method. We further applied this workflow to analyze serum samples for potential candidate biomarker discovery of pancreatic cancer. Overall, 576 serum proteins were detected with 41 proteins significantly changed, which could serve as potential biomarkers for pancreatic cancer. Additionally, we evaluated the performance of RSP-MOSF method in a 96-well plate format which demonstrated an excellent reproducibility of the analysis. These results indicated that RSP-MOSF method had the potential to be applied on an automatic platform for further scaled analysis.

Keywords:  Macroporous ordered siliceous foams; Pancreatic cancer; Proteomic analysis; Serum pretreatment

DOI:  https://doi.org/10.1016/j.talanta.2021.123018
Cells. 2021 Nov 12. pii: 3141. [Epub ahead of print]10(11):

Plasma Metabolomics of Intermediate and Neovascular Age-Related Macular Degeneration Patients.

Sabrina L Mitchell, Chunyu Ma, William K Scott, Anita Agarwal, Margaret A Pericak-Vance, Jonathan L Haines, Dean P Jones, Karan Uppal, Milam A Brantley.

  To characterize metabolites and metabolic pathways altered in intermediate and neovascular age-related macular degeneration (IAMD and NVAMD), high resolution untargeted metabolomics was performed via liquid chromatography-mass spectrometry on plasma samples obtained from 91 IAMD patients, 100 NVAMD patients, and 195 controls. Plasma metabolite levels were compared between: AMD patients and controls, IAMD patients and controls, and NVAMD and IAMD patients. Partial least-squares discriminant analysis and linear regression were used to identify discriminatory metabolites. Pathway analysis was performed to determine metabolic pathways altered in AMD. Among the comparisons, we identified 435 unique discriminatory metabolic features. Using computational methods and tandem mass spectrometry, we identified 11 metabolic features whose molecular identities had been previously verified and confirmed the molecular identities of three additional discriminatory features. Included among the discriminatory metabolites were acylcarnitines, phospholipids, amino acids, and steroid metabolites. Pathway analysis revealed that lipid, amino acid, and vitamin metabolism pathways were altered in NVAMD, IAMD, or AMD in general, including the carnitine shuttle pathway which was significantly altered in all comparisons. Finally, few discriminatory features were identified between IAMD patients and controls, suggesting that plasma metabolic profiles of IAMD patients are more similar to controls than to NVAMD patients.

Keywords:  IAMD; NVAMD; acylcarnitines; age-related macular degeneration; carnitine shuttle; metabolomics; phospholipids

DOI:  https://doi.org/10.3390/cells10113141
Biomedicines. 2021 Nov 11. pii: 1664. [Epub ahead of print]9(11):

Mitochondria-Mediated Apoptosis of HCC Cells Triggered by Knockdown of Glutamate Dehydrogenase 1: Perspective for Its Inhibition through Quercetin and Permethylated Anigopreissin A.

Michela Marsico, Anna Santarsiero, Ilaria Pappalardo, Paolo Convertini, Lucia Chiummiento, Alessandra Sardone, Maria Antonietta Di Noia, Vittoria Infantino, Simona Todisco.

  Metabolic reprogramming is a hallmark of cancer cells required to ensure high energy needs and the maintenance of redox balance. A relevant metabolic change of cancer cell bioenergetics is the increase in glutamine metabolism. Hepatocellular carcinoma (HCC), one of the most lethal cancer and which requires the continuous development of new therapeutic strategies, shows an up-regulation of human glutamate dehydrogenase 1 (hGDH1). GDH1 function may be relevant in cancer cells (or HCC) to drive the glutamine catabolism from L-glutamate towards the synthesis of α-ketoglutarate (α-KG), thus supplying key tricarboxylic acid cycle (TCA cycle) metabolites. Here, the effects of hGLUD1 gene silencing (siGLUD1) and GDH1 inhibition were evaluated. Our results demonstrate that siGLUD1 in HepG2 cells induces a significant reduction in cell proliferation (58.8% ± 10.63%), a decrease in BCL2 expression levels, mitochondrial mass (75% ± 5.89%), mitochondrial membrane potential (30% ± 7.06%), and a significant increase in mitochondrial superoxide anion (25% ± 6.55%) compared to control/untreated cells. The inhibition strategy leads us to identify two possible inhibitors of hGDH1: quercetin and Permethylated Anigopreissin A (PAA). These findings suggest that hGDH1 could be a potential candidate target to impair the metabolic reprogramming of HCC cells.

Keywords:  GLUD1; HCC; Permethylated Anigopreissin A (PAA); apoptosis; hGDH1; inhibition; mitochondrial mass; quercetin; redox homeostasis

DOI:  https://doi.org/10.3390/biomedicines9111664
Metabolites. 2021 Nov 20. pii: 792. [Epub ahead of print]11(11):

Metabolism, HDACs, and HDAC Inhibitors: A Systems Biology Perspective.

Jacob King, Maya Patel, Sriram Chandrasekaran.

  Histone deacetylases (HDACs) are epigenetic enzymes that play a central role in gene regulation and are sensitive to the metabolic state of the cell. The cross talk between metabolism and histone acetylation impacts numerous biological processes including development and immune function. HDAC inhibitors are being explored for treating cancers, viral infections, inflammation, neurodegenerative diseases, and metabolic disorders. However, how HDAC inhibitors impact cellular metabolism and how metabolism influences their potency is unclear. Discussed herein are recent applications and future potential of systems biology methods such as high throughput drug screens, cancer cell line profiling, single cell sequencing, proteomics, metabolomics, and computational modeling to uncover the interplay between metabolism, HDACs, and HDAC inhibitors. The synthesis of new systems technologies can ultimately help identify epigenomic and metabolic biomarkers for patient stratification and the design of effective therapeutics.

Keywords:  epigenome; gene regulation; histone acetylation; histone deacetylases; metabolomics; proteomics; transcriptomics

DOI:  https://doi.org/10.3390/metabo11110792
Metabolites. 2021 Oct 28. pii: 740. [Epub ahead of print]11(11):

Untargeted Lipidomics of Non-Small Cell Lung Carcinoma Demonstrates Differentially Abundant Lipid Classes in Cancer vs. Non-Cancer Tissue.

Joshua M Mitchell, Robert M Flight, Hunter N B Moseley.

  Lung cancer remains the leading cause of cancer death worldwide and non-small cell lung carcinoma (NSCLC) represents 85% of newly diagnosed lung cancers. In this study, we utilized our untargeted assignment tool Small Molecule Isotope Resolved Formula Enumerator (SMIRFE) and ultra-high-resolution Fourier transform mass spectrometry to examine lipid profile differences between paired cancerous and non-cancerous lung tissue samples from 86 patients with suspected stage I or IIA primary NSCLC. Correlation and co-occurrence analysis revealed significant lipid profile differences between cancer and non-cancer samples. Further analysis of machine-learned lipid categories for the differentially abundant molecular formulas identified a high abundance sterol, high abundance and high m/z sphingolipid, and low abundance glycerophospholipid metabolic phenotype across the NSCLC samples. At the class level, higher abundances of sterol esters and lower abundances of cardiolipins were observed suggesting altered stearoyl-CoA desaturase 1 (SCD1) or acetyl-CoA acetyltransferase (ACAT1) activity and altered human cardiolipin synthase 1 or lysocardiolipin acyltransferase activity respectively, the latter of which is known to confer apoptotic resistance. The presence of a shared metabolic phenotype across a variety of genetically distinct NSCLC subtypes suggests that this phenotype is necessary for NSCLC development and may result from multiple distinct genetic lesions. Thus, targeting the shared affected pathways may be beneficial for a variety of genetically distinct NSCLC subtypes.

Keywords:  Fourier-transform mass spectrometry; SMIRFE; lipidomics; non-small cell lung carcinoma

DOI:  https://doi.org/10.3390/metabo11110740
Biomolecules. 2021 Nov 10. pii: 1666. [Epub ahead of print]11(11):

An Asp to Strike Out Cancer? Therapeutic Possibilities Arising from Aspartate's Emerging Roles in Cell Proliferation and Survival.

Iiro Taneli Helenius, Hanumantha Rao Madala, Jing-Ruey Joanna Yeh.

  A better understanding of the metabolic constraints of a tumor may lead to more effective anticancer treatments. Evidence has emerged in recent years shedding light on a crucial aspartate dependency of many tumor types. As a precursor for nucleotide synthesis, aspartate is indispensable for cell proliferation. Moreover, the malate-aspartate shuttle plays a key role in redox balance, and a deficit in aspartate can lead to oxidative stress. It is now recognized that aspartate biosynthesis is largely governed by mitochondrial metabolism, including respiration and glutaminolysis in cancer cells. Therefore, under conditions that suppress mitochondrial metabolism, including mutations, hypoxia, or chemical inhibitors, aspartate can become a limiting factor for tumor growth and cancer cell survival. Notably, aspartate availability has been associated with sensitivity or resistance to various therapeutics that are presently in the clinic or in clinical trials, arguing for a critical need for more effective aspartate-targeting approaches. In this review, we present current knowledge of the metabolic roles of aspartate in cancer cells and describe how cancer cells maintain aspartate levels under different metabolic states. We also highlight several promising aspartate level-modulating agents that are currently under investigation.

Keywords:  GOT1; alpha-ketoglutarate; asparagine; aspartate; cancer metabolism; glutaminase; hypoxia; mitochondrial DNA mutation; mitochondrial respiration; oxidative phosphorylation

DOI:  https://doi.org/10.3390/biom11111666
J Proteome Res. 2021 Nov 23.

Comprehensive Metabolomics and Lipidomics Profiling of Prostate Cancer Tissue Reveals Metabolic Dysregulations Associated with Disease Development.

Ana Rita Lima, Márcia Carvalho, Susana S Aveiro, Tânia Melo, M Rosário Domingues, Catarina Macedo-Silva, Nuno Coimbra, Carmen Jerónimo, Rui Henrique, Maria de Lourdes Bastos, Paula Guedes de Pinho, Joana Pinto.

  Prostate cancer (PCa) is a global health problem that affects millions of men every year. In the past decade, metabolomics and related subareas, such as lipidomics, have demonstrated an enormous potential to identify novel mechanisms underlying PCa development and progression, providing a good basis for the development of new and more effective therapies and diagnostics. In this study, a multiplatform metabolomics and lipidomics approach, combining untargeted mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based techniques, was applied to PCa tissues to investigate dysregulations associated with PCa development, in a cohort of 40 patients submitted to radical prostatectomy for PCa. Results revealed significant alterations in the levels of 26 metabolites and 21 phospholipid species in PCa tissue compared with adjacent nonmalignant tissue, suggesting dysregulation in 13 metabolic pathways associated with PCa development. The most affected metabolic pathways were amino acid metabolism, nicotinate and nicotinamide metabolism, purine metabolism, and glycerophospholipid metabolism. A clear interconnection between metabolites and phospholipid species participating in these pathways was observed through correlation analysis. Overall, these dysregulations may reflect the reprogramming of metabolic responses to produce high levels of cellular building blocks required for rapid PCa cell proliferation.

Keywords:  lipidomics; metabolic pathways; metabolomics; prostate cancer; tissue

DOI:  https://doi.org/10.1021/acs.jproteome.1c00754
Metabolomics. 2021 Nov 25. 17(12): 104

Metabonomics study of the effects of single copy mutant KRAS in the presence or absence of WT allele using human HCT116 isogenic cell lines.

Dorna Varshavi, Dorsa Varshavi, Nicola McCarthy, Kirill Veselkov, Hector C Keun, Jeremy R Everett.

   INTRODUCTION: KRAS was one of the earliest human oncogenes to be described and is one of the most commonly mutated genes in different human cancers, including colorectal cancer. Despite KRAS mutants being known driver mutations, KRAS has proved difficult to target therapeutically, necessitating a comprehensive understanding of the molecular mechanisms underlying KRAS-driven cellular transformation.
OBJECTIVES: To investigate the metabolic signatures associated with single copy mutant KRAS in isogenic human colorectal cancer cells and to determine what metabolic pathways are affected.
METHODS: Using NMR-based metabonomics, we compared wildtype (WT)-KRAS and mutant KRAS effects on cancer cell metabolism using metabolic profiling of the parental KRAS G13D/+ HCT116 cell line and its isogenic, derivative cell lines KRAS +/- and KRAS G13D/-.
RESULTS: Mutation in the KRAS oncogene leads to a general metabolic remodelling to sustain growth and counter stress, including alterations in the metabolism of amino acids and enhanced glutathione biosynthesis. Additionally, we show that KRASG13D/+ and KRASG13D/- cells have a distinct metabolic profile characterized by dysregulation of TCA cycle, up-regulation of glycolysis and glutathione metabolism pathway as well as increased glutamine uptake and acetate utilization.
CONCLUSIONS: Our study showed the effect of a single point mutation in one KRAS allele and KRAS allele loss in an isogenic genetic background, hence avoiding confounding genetic factors. Metabolic differences among different KRAS mutations might play a role in their different responses to anticancer treatments and hence could be exploited as novel metabolic vulnerabilities to develop more effective therapies against oncogenic KRAS.

Keywords:  Cells; Colorectal cancer; HCT116; KRAS; Metabolic profiling; Metabolomics; Metabonomics; Mutations; NMR

DOI:  https://doi.org/10.1007/s11306-021-01852-w
Nat Prod Rep. 2021 Nov 17. 38(11): 1967-1993

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches.

Mehdi A Beniddir, Kyo Bin Kang, Grégory Genta-Jouve, Florian Huber, Simon Rogers, Justin J J van der Hooft.

Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.

DOI: https://doi.org/10.1039/d1np00023c
Metabolites. 2021 Nov 09. pii: 765. [Epub ahead of print]11(11):

Fatty Acid Metabolism Reprogramming in Advanced Prostate Cancer.

Huan Xu, Yanbo Chen, Meng Gu, Chong Liu, Qi Chen, Ming Zhan, Zhong Wang.

  Prostate cancer (PCa) is a carcinoma in which fatty acids are abundant. Fatty acid metabolism is rewired during PCa development. Although PCa can be treated with hormone therapy, after prolonged treatment, castration-resistant prostate cancer can develop and can lead to increased mortality. Changes to fatty acid metabolism occur systemically and locally in prostate cancer patients, and understanding these changes may lead to individualized treatments, especially in advanced, castration-resistant prostate cancers. The fatty acid metabolic changes are not merely reflective of oncogenic activity, but in many cases, these represent a critical factor in cancer initiation and development. In this review, we analyzed the literature regarding systemic changes to fatty acid metabolism in PCa patients and how these changes relate to obesity, diet, circulating metabolites, and peri-prostatic adipose tissue. We also analyzed cellular fatty acid metabolism in prostate cancer, including fatty acid uptake, de novo lipogenesis, fatty acid elongation, and oxidation. This review broadens our view of fatty acid switches in PCa and presents potential candidates for PCa treatment and diagnosis.

Keywords:  castration-resistant prostate cancer; fatty acid; metabolic reprogramming; neuroendocrine prostate cancer

DOI:  https://doi.org/10.3390/metabo11110765
J Proteome Res. 2021 Nov 22.

Multiple Infusion Start Time Mass Spectrometry Imaging of Dynamic SIL-Glutathione Biosynthesis Using Infrared Matrix-Assisted Laser Desorption Electrospray Ionization.

Allyson L Mellinger, Kenneth P Garrard, Sitora Khodjaniyazova, Zahid N Rabbani, Michael P Gamcsik, David C Muddiman.

  Due to the high association of glutathione metabolism perturbation with a variety of disease states, there is a dire need for analytical techniques to study glutathione kinetics. Additionally, the elucidation of microenvironmental effects on changes in glutathione metabolism would significantly improve our understanding of the role of glutathione in disease. We therefore present a study combining a multiple infusion start time protocol, stable isotope labeling technology, infrared matrix-assisted laser desorption electrospray ionization, and high-resolution accurate mass-mass spectrometry imaging to study spatial changes in glutathione kinetics across in sectioned mouse liver tissues. After injecting a mouse with the isotopologues [2-13C,15N]-glycine, [1,2-13C2]-glycine, and [1,2-13C2,15N]-glycine at three different time points, we were able to fully resolve and spatially map their metabolism into three isotopologues of glutathione and calculate their isotopic enrichment in glutathione. We created a tool in the open-source mass spectrometry imaging software MSiReader to accurately compute the percent isotope enrichment (PIE) of these labels in glutathione and visualize them in heat-maps of the tissue sections. In areas of high flux, we found that each label enriched an approximate median of 1.6%, 1.8%, and 1.5%, respectively, of the glutathione product pool measured in each voxel. This method may be adapted to study the heterogeneity of glutathione flux in diseased versus healthy tissues.

Keywords:  IR-MALDESI; glutathione; kinetic metabolism; mass spectrometry imaging; stable isotope labeling

DOI:  https://doi.org/10.1021/acs.jproteome.1c00636
STAR Protoc. 2021 Dec 17. 2(4): 100876

Investigating the K+ sensitivity of cellular metabolism by extracellular flux analysis.

Sandra Burgstaller, Helmut Bischof, Robert Lukowski, Wolfgang F Graier, Roland Malli.

  We have recently demonstrated that the activity of hexokinase 2 is dependent on the intracellular potassium ion (K+) concentration ([K+]). To analyze the K+ dependency of the cell metabolism in cell populations, we used an extracellular flux analyzer to assess oxygen consumption and acidification rates as well-established measures of oxidative- and glycolytic metabolic activities. This protocol describes in detail how a potential K+ sensitivity of the cell metabolism can be elucidated by extracellular flux analysis. For complete details on the use and execution of this protocol, please refer to Bischof et al. (2021).

Keywords:  Cancer; Cell Biology; Cell-based Assays; High Throughput Screening; Metabolism; Molecular Biology

DOI:  https://doi.org/10.1016/j.xpro.2021.100876
Br J Cancer. 2021 Nov 22.

Metabolic stress induces GD2+ cancer stem cell-like phenotype in triple-negative breast cancer.

Appalaraju Jaggupilli, Stanley Ly, Khoa Nguyen, Vivek Anand, Bin Yuan, Fouad El-Dana, Yuanqing Yan, Zoe Arvanitis, Danthasinghe Waduge Badrajee Piyarathna, Nagireddy Putluri, Helen Piwnica-Worms, Henry Charles Manning, Michael Andreeff, V Lokesh Battula.

BACKGROUND: Metabolic stress resulting from nutrient deficiency is one of the hallmarks of a growing tumour. Here, we tested the hypothesis that metabolic stress induces breast cancer stem-like cell (BCSC) phenotype in triple-negative breast cancer (TNBC).
METHODS: Flow cytometry for GD2 expression, mass spectrometry and Ingenuity Pathway Analysis for metabolomics, bioinformatics, in vitro tumorigenesis and in vivo models were used.
RESULTS: Serum/glucose deprivation not only increased stress markers but also enhanced GD2+ BCSC phenotype and function in TNBC cells. Global metabolomics profiling identified upregulation of glutathione biosynthesis in GD2high cells, suggesting a role of glutamine in the BCSC phenotype. Cueing from the upregulation of the glutamine transporters in primary breast tumours, inhibition of glutamine uptake using small-molecule inhibitor V9302 reduced GD2+ cells by 70-80% and BCSC characteristics in TNBC cells. Mechanistic studies revealed inhibition of the mTOR pathway and induction of ferroptosis by V9302 in TNBC cells. Finally, inhibition of glutamine uptake significantly reduced in vivo tumour growth in a TNBC patient-derived xenograft model using NSG (non-obese diabetic/severe combined immunodeficiency with a complete null allele of the IL-2 receptor common gamma chain) mice.
CONCLUSION: Here, we show metabolic stress results in GD2+ BCSC phenotype in TNBC and glutamine contributes to GD2+ phenotype, and targeting the glutamine transporters could complement conventional chemotherapy in TNBC.

DOI: https://doi.org/10.1038/s41416-021-01636-y
Biomedicines. 2021 Oct 28. pii: 1557. [Epub ahead of print]9(11):

Endothelial Heme Dynamics Drive Cancer Cell Metabolism by Shaping the Tumor Microenvironment.

Sara Petrillo, Francesco De Giorgio, Joanna Kopecka, Tullio Genova, Veronica Fiorito, Anna Lucia Allocco, Francesca Bertino, Deborah Chiabrando, Federico Mussano, Fiorella Altruda, Luca Munaron, Chiara Riganti, Emanuela Tolosano.

  The crosstalk among cancer cells (CCs) and stromal cells within the tumor microenvironment (TME) has a prominent role in cancer progression. The significance of endothelial cells (ECs) in this scenario relies on multiple vascular functions. By forming new blood vessels, ECs support tumor growth. In addition to their angiogenic properties, tumor-associated ECs (TECs) establish a unique vascular niche that actively modulates cancer development by shuttling a selected pattern of factors and metabolites to the CC. The profile of secreted metabolites is strictly dependent on the metabolic status of the cell, which is markedly perturbed in TECs. Recent evidence highlights the involvement of heme metabolism in the regulation of energy metabolism in TECs. The present study shows that interfering with endothelial heme metabolism by targeting the cell membrane heme exporter Feline Leukemia Virus subgroup C Receptor 1a (FLVCR1a) in TECs, resulted in enhanced fatty acid oxidation (FAO). Moreover, FAO-derived acetyl-CoA was partly consumed through ketogenesis, resulting in ketone bodies (KBs) accumulation in FLVCR1a-deficient TECs. Finally, the results from this study also demonstrate that TECs-derived KBs can be secreted in the extracellular environment, inducing a metabolic rewiring in the CC. Taken together, these data may contribute to finding new metabolic vulnerabilities for cancer therapy.

Keywords:  FLVCR1a; cancer cell metabolism; endothelial cell metabolism; heme metabolism; ketone bodies; tumor endothelial cells; tumor microenvironment

DOI:  https://doi.org/10.3390/biomedicines9111557
Mol Cell Proteomics. 2021 Nov 19. pii: S1535-9476(21)00151-1. [Epub ahead of print] 100179

Scaling up single-cell proteomics.

Nikolai Slavov.

Single-cell tandem mass-spectrometry (MS) has enabled analyzing hundreds of single cells per day and quantifying thousands of proteins across the cells. The broad dissemination of these capabilities can empower the dissection of pathophysiological mechanisms in heterogeneous tissues. Key requirements for achieving this goal include robust protocols performed on widely accessible hardware, robust quality controls, community standards, and automated data analysis pipelines that can pinpoint analytical problems and facilitate their timely resolution. Towards meeting these requirements, this perspective outlines both existing resources and outstanding opportunities, such as parallelization, for catalyzing the wide dissemination of quantitative single-cell proteomics analysis that can be scaled up to tens of thousands of single cells. Indeed, simultaneous parallelization of the analysis of peptides and single cells is a promising approach for multiplicative increase in the speed of performing deep and quantitative single-cell proteomics. The community is ready to begin a virtuous cycle of increased adoption fueling the development of more technology and resources for single-cell proteomics that in turn drive broader adoption, scientific discoveries, and clinical applications.

DOI: https://doi.org/10.1016/j.mcpro.2021.100179
Metabolites. 2021 Nov 09. pii: 764. [Epub ahead of print]11(11):

Targeted Quantification of Carbon Metabolites Identifies Metabolic Progression Markers and an Undiagnosed Case of SDH-Deficient Clear Cell Renal Cell Carcinoma in a German Cohort.

Doreen William, Kati Erdmann, Jonas Ottemöller, Anastasios Mangelis, Catleen Conrad, Mirko Peitzsch, Evelin Schröck, Graeme Eisenhofer, Aristeidis Zacharis, Susanne Füssel, Daniela Aust, Barbara Klink, Susan Richter.

  Renal cell carcinoma (RCC) is among the 10 most common cancer entities and can be categorised into distinct subtypes by differential expression of Krebs cycle genes. We investigated the predictive value of several targeted metabolites with regards to tumour stages and patient survival in an unselected cohort of 420 RCCs. Unsupervised hierarchical clustering of metabolite ratios identified two main clusters separated by α-ketoglutarate (α-KG) levels and sub-clusters with differential levels of the oncometabolite 2-hydroxyglutarate (2HG). Sub-clusters characterised by high 2HG were enriched in higher tumour stages, suggesting metabolite profiles might be suitable predictors of tumour stage or survival. Bootstrap forest models based on single metabolite signatures showed that lactate, 2HG, citrate, aspartate, asparagine, and glutamine better predicted the cancer-specific survival (CSS) of clear cell RCC patients, whereas succinate and α-ketoglutarate were better CSS predictors for papillary RCC patients. Additionally, this assay identifies rare cases of tumours with SDHx mutations, which are caused predominantly by germline mutations and which predispose to development of different neoplasms. Hence, analysis of selected metabolites should be further evaluated for potential utility in liquid biopsies, which can be obtained using less invasive methods and potentially facilitate disease monitoring for both patients and caregivers.

Keywords:  Krebs cycle; metabolic profiling; renal cell carcinoma; subtypes; succinate dehydrogenase mutations; survival analysis

DOI:  https://doi.org/10.3390/metabo11110764
Int J Biol Sci. 2021 ;17(15): 4493-4513

Close interactions between lncRNAs, lipid metabolism and ferroptosis in cancer.

Jingjing Huang, Jin Wang, Hua He, Zichen Huang, Sufang Wu, Chao Chen, Wenbing Liu, Li Xie, Yongguang Tao, Li Cong, Yiqun Jiang.

  Abnormal lipid metabolism including synthesis, uptake, modification, degradation and transport has been considered a hallmark of malignant tumors and contributes to the supply of substances and energy for rapid cell growth. Meanwhile, abnormal lipid metabolism is also associated with lipid peroxidation, which plays an important role in a newly discovered type of regulated cell death termed ferroptosis. Long noncoding RNAs (lncRNAs) have been proven to be associated with the occurrence and progression of cancer. Growing evidence indicates that lncRNAs are key regulators of abnormal lipid metabolism and ferroptosis in cancer. In this review, we mainly summarized the mechanism by which lncRNAs regulate aberrant lipid metabolism in cancer, illustrated that lipid metabolism can also influence the expression of lncRNAs, and discussed the mechanism by which lncRNAs affect ferroptosis. A comprehensive understanding of the interactions between lncRNAs, lipid metabolism and ferroptosis could help us to develop novel strategies for precise cancer treatment in the future.

Keywords:  Cancer; Ferroptosis; Lipid metabolism; LncRNAs

DOI:  https://doi.org/10.7150/ijbs.66181
Biochem Biophys Res Commun. 2021 Nov 13. pii: S0006-291X(21)01555-2. [Epub ahead of print]585 132-138

The influence of dexamethasone on hepatic fatty acids metabolism and transport in human steatotic HepG2 cell line exposed to palmitate.

Klaudia Sztolsztener, Ewa Harasim-Symbor, Adrian Chabowski, Karolina Konstantynowicz-Nowicka.

  Dexamethasone (DEX) is a synthetic glucocorticoid with anti-inflammatory properties. We evaluated a potentially protective dexamethasone influence on hepatocellular lipid metabolism and fatty acid (FA) transporters expression. The HepG2 cells were incubated with palmitic acid (PA) and/or dexamethasone in two different time expositions (16 h and 40 h). Intracellular and extracellular lipid and sphingolipid concentrations were estimated by the gas-liquid chromatography and high-performance liquid chromatography, respectively. The protein expression involved in FA uptake and lipid metabolism was determined by immunoblotting. The treatment of HepG2 with dexamethasone and palmitate enhanced lipid transport to the cell via increased especially FABPpm expression and resulted in the increased triacylglycerol (TAG), diacylglycerol (DAG) and ceramide deposition. Dexamethasone with palmitate treatment altered FA composition resulting in the elevated n-3 polyunsaturated fatty acid (PUFA) activity in DAG and TAG and the diminished n-6 PUFA activity in DAG after prolonged exposure. We may speculate that although protective lipid secretion into media and decrease in inflammatory FA precursors dexamethasone treatment exacerbated lipotoxicity in HepG2 cells.

Keywords:  Dexamethasone; Fatty acid; Glucocorticoid; Lipid metabolism; Lipid transporter

DOI:  https://doi.org/10.1016/j.bbrc.2021.11.044
Cell Rep. 2021 Nov 23. pii: S2211-1247(21)01542-4. [Epub ahead of print]37(8): 110056

Statins affect cancer cell plasticity with distinct consequences for tumor progression and metastasis.

Madeleine Dorsch, Manuela Kowalczyk, Mélanie Planque, Geronimo Heilmann, Sebastian Urban, Philip Dujardin, Jan Forster, Kristina Ueffing, Silke Nothdurft, Sebastian Oeck, Annika Paul, Sven T Liffers, Farnusch Kaschani, Markus Kaiser, Alexander Schramm, Jens T Siveke, Monte M Winslow, Sarah-Maria Fendt, Perihan Nalbant, Barbara M Grüner.

  Statins are among the most commonly prescribed drugs, and around every fourth person above the age of 40 is on statin medication. Therefore, it is of utmost clinical importance to understand the effect of statins on cancer cell plasticity and its consequences to not only patients with cancer but also patients who are on statins. Here, we find that statins induce a partial epithelial-to-mesenchymal transition (EMT) phenotype in cancer cells of solid tumors. Using a comprehensive STRING network analysis of transcriptome, proteome, and phosphoproteome data combined with multiple mechanistic in vitro and functional in vivo analyses, we demonstrate that statins reduce cellular plasticity by enforcing a mesenchymal-like cell state that increases metastatic seeding ability on one side but reduces the formation of (secondary) tumors on the other due to heterogeneous treatment responses. Taken together, we provide a thorough mechanistic overview of the consequences of statin use for each step of cancer development, progression, and metastasis.

Keywords:  barcode screening; cellular plasticity; cholesterol pathway; mesenchymal cell state shift; statins

DOI:  https://doi.org/10.1016/j.celrep.2021.110056
Clin Mass Spectrom. 2020 Nov;18 1-12

Mass spectrometry in clinical glycomics: The path from biomarker identification to clinical implementation.

N de Haan, M Wuhrer, L R Ruhaak.

  Over the past decades, the genome and proteome have been widely explored for biomarker discovery and personalized medicine. However, there is still a large need for improved diagnostics and stratification strategies for a wide range of diseases. Post-translational modification of proteins by glycosylation affects protein structure and function, and glycosylation has been implicated in many prevalent human diseases. Numerous proteins for which the plasma levels are nowadays evaluated in clinical practice are glycoproteins. While the glycosylation of these proteins often changes with disease, their glycosylation status is largely ignored in the clinical setting. Hence, the implementation of glycomic markers in the clinic is still in its infancy. This is for a large part caused by the high complexity of protein glycosylation itself and of the analytical techniques required for their robust quantification. Mass spectrometry-based workflows are particularly suitable for the quantification of glycans and glycoproteins, but still require advances for their transformation from a biomedical research setting to a clinical laboratory. In this review, we describe why and how glycomics is expected to find its role in clinical tests and the status of current mass spectrometry-based methods for clinical glycomics.

Keywords:  Absolute quantitation; Calibration; Clinical glycomics; Glycoprotein; Mass spectrometry

DOI:  https://doi.org/10.1016/j.clinms.2020.08.001
Metabolites. 2021 Nov 11. pii: 772. [Epub ahead of print]11(11):

Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification.

Gang Xing, Vishnu Sresht, Zhongyuan Sun, Yuji Shi, Michelle F Clasquin.

  A key unmet need in metabolomics continues to be the specific, selective, accurate detection of traditionally difficult to retain molecules including simple sugars, sugar phosphates, carboxylic acids, and related amino acids. Designed to retain the metabolites of central carbon metabolism, this Mixed Mode (MM) chromatography applies varied pH, salt concentration and organic content to a positively charged quaternary amine polyvinyl alcohol stationary phase. This MM method is capable of separating glucose from fructose, and four hexose monophosphates a single chromatographic run. Coupled to a QExactive Orbitrap Mass Spectrometer with negative ESI, linearity, LLOD, %CV, and mass accuracy were assessed using 33 metabolite standards. The standards were linear on average >3 orders of magnitude (R2 > 0.98 for 30/33) with LLOD < 1 pmole (26/33), median CV of 12% over two weeks, and median mass accuracy of 0.49 ppm. To assess the breadth of metabolome coverage and better define the structural elements dictating elution, we injected 607 unique metabolites and determined that 398 are well retained. We then split the dataset of 398 documented RTs into training and test sets and trained a message-passing neural network (MPNN) to predict RT from a featurized heavy atom connectivity graph. Unlike traditional QSAR methods that utilize hand-crafted descriptors or pre-defined structural keys, the MPNN aggregates atomic features across the molecular graph and learns to identify molecular subgraphs that are correlated with variations in RTs. For sugars, sugar phosphates, carboxylic acids, and isomers, the model achieves a predictive RT error of <2 min on 91%, 50%, 77%, and 72% of held-out compounds from these subsets, with overall root mean square errors of 0.11, 0.34, 0.18, and 0.53 min, respectively. The model was then applied to rank order metabolite IDs for molecular features altered by GLS2 knockout in mouse primary hepatocytes.

Keywords:  LCMS; MPNN; machine learning; metabolomics; retention time prediction

DOI:  https://doi.org/10.3390/metabo11110772
Clin Mass Spectrom. 2020 Nov;18 13-26

A proposal for score assignment to characterize biological processes from mass spectral analysis of serum.

Joanna Roder, Lelia Net, Carlos Oliveira, Krista Meyer, Senait Asmellash, Sabine Kasimir-Bauer, Harvey Pass, Jeffrey Weber, Heinrich Roder, Julia Grigorieva.

   Introduction: Most diseases involve a complex interplay between multiple biological processes at the cellular, tissue, organ, and systemic levels. Clinical tests and biomarkers based on the measurement of a single or few analytes may not be able to capture the complexity of a patient's disease. Novel approaches for comprehensively assessing biological processes from easily obtained samples could help in the monitoring, treatment, and understanding of many conditions.
Objectives: We propose a method of creating scores associated with specific biological processes from mass spectral analysis of serum samples.
Methods: A score for a process of interest is created by: (i) identifying mass spectral features associated with the process using set enrichment analysis methods, and (ii) combining these features into a score using a principal component analysis-based approach. We investigate the creation of scores using cohorts of patients with non-small cell lung cancer, melanoma, and ovarian cancer. Since the circulating proteome is amenable to the study of immune responses, which play a critical role in cancer development and progression, we focus on functions related to the host response to disease.
Results: We demonstrate the feasibility of generating scores, their reproducibility, and their associations with clinical outcomes. Once the scores are constructed, only 3 µL of serum is required for the assessment of multiple biological functions from the circulating proteome.
Conclusion: These mass spectrometry-based scores could be useful for future multivariate biomarker or test development studies for informing treatment, disease monitoring and improving understanding of the roles of various biological functions in multiple disease settings.

Keywords:  AIR, acute inflammatory response; ALK, anaplastic lymphoma kinase; ANG, angiogenesis; APR, acute phase reaction; BRCA1/2, Breast Cancer Gene 1, Breast Cancer Gene 2; Biological scores; Biomarker; CA, complement activation; CI, confidence interval; CPH, Cox proportional hazards; CV, coefficient of variation; ECM, extracellular matrix organization; EGFR, epidermal growth factor receptor; FDA, US Food and Drug Administration; GLY, glycolysis; HR, hazard ratio; HbA1c, hemoglobin A1c; IFN1, interferon type 1 signaling and response; IFNg, Interferon γ signaling and response; IRn, type n immune response; IT, immune tolerance; LC MS-MS, liquid chromatography with tandem mass spectrometry; MALDI ToF, matrix-assisted laser desorption/ionization time of flight; MRM, multiple reaction monitoring; MS, mass spectral; Mass spectrometry; NSCLC, non-small cell lung cancer; OS, overall survival; PC, principal component; PCA, principal component analysis; PCn, principal component n; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; Proteomics; QC, quality control; Serum proteome; Set enrichment analysis; WH, wound healing; m/Z, mass/charge

DOI:  https://doi.org/10.1016/j.clinms.2020.09.001
J Mass Spectrom Adv Clin Lab. 2021 Aug;21 27-30

The clinical translation of eicosanoids and other oxylipins, although challenging, should be actively pursued.

Cécile Gladine, Maria Fedorova.



Keywords:  CE, cholesteryl ester; CVD, cardiovascular disease; LDL, low density lipoprotein; NFκB, nuclear factor kappa B; PC, phosphatidylcholine; PL, phospholipid; PPAR, peroxisome proliferator-activated receptor; PUFA, polyunsaturated fatty acid; TG, triglyceride; oxCE, oxidized CE; oxLDL, oxidized LDL; oxTG, oxidized TG

DOI:  https://doi.org/10.1016/j.jmsacl.2021.08.003
Biomedicines. 2021 Nov 07. pii: 1636. [Epub ahead of print]9(11):

Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers.

Roshan Shafiha, Basak Bahcivanci, Georgios V Gkoutos, Animesh Acharjee.

  Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease that presents a great challenge for treatment and prevention.. This study aims to implement a machine learning approach that employs such datasets to identify potential biomarker targets. We developed a pipeline to identify potential biomarkers for NAFLD that includes five major processes, namely, a pre-processing step, a feature selection and a generation of a random forest model and, finally, a downstream feature analysis and a provision of a potential biological interpretation. The pre-processing step includes data normalising and variable extraction accompanied by appropriate annotations. A feature selection based on a differential gene expression analysis is then conducted to identify significant features and then employ them to generate a random forest model whose performance is assessed based on a receiver operating characteristic curve. Next, the features are subjected to a downstream analysis, such as univariate analysis, a pathway enrichment analysis, a network analysis and a generation of correlation plots, boxplots and heatmaps. Once the results are obtained, the biological interpretation and the literature validation is conducted over the identified features and results. We applied this pipeline to transcriptomics and lipidomic datasets and concluded that the C4BPA gene could play a role in the development of NAFLD. The activation of the complement pathway, due to the downregulation of the C4BPA gene, leads to an increase in triglyceride content, which might further render the lipid metabolism. This approach identified the C4BPA gene, an inhibitor of the complement pathway, as a potential biomarker for the development of NAFLD.

Keywords:  NAFLD; biomarker; lipidomics; machine learning; transcriptomics

DOI:  https://doi.org/10.3390/biomedicines9111636
Clin Mass Spectrom. 2020 Jan;15 29-35

More sensitivity is always better: Measuring sub-clinical levels of serum thyroglobulin on a µLC-MS/MS system.

Christopher M Shuford, Jay S Johnson, J Will Thompson, Patricia L Holland, Andrew N Hoofnagle, Russell P Grant.

  Although liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays for thyroglobulin (Tg) are resistant to autoantibody (TgAb) interference, recent studies have demonstrated approximately 40% of TgAb-positive individuals with recurrent thyroid cancer have Tg concentrations below the lower limit of quantification (LLOQ) of the LC-MS/MS assays described to date (i.e., <0.5 ng/mL), resulting in false-negative findings during post-thyroidectomy monitoring. To reduce false negative results due to insufficient analytical sensitivity, a new Tg assay was developed on a commercially available LC-MS/MS system operating at microliter/minute flow-rates (i.e., µLC-MS/MS) to maximize the analytical sensitivity and achieve a LLOQ of 0.02 ng/mL. When applied to the measurement of TgAb-negative and TgAb-positive patient serum samples previously measuring below the LLOQ of current immunometric and LC-MS/MS assays (LLOQ, 0.1-0.2 ng/mL), concentrations were measurable by µLC-MS/MS in 66% and 44% of samples, respectively - possibly explaining the persistence of TgAb in those patients. Patients with low Tg concentrations measured by µLC-MS/MS (<0.1 ng/mL) also exhibited elevation in their Tg concentrations upon hormone stimulation, indicating the detected Tg was produced from remnant thyroid tissue and would be suitable as a tissue biomarker. Forty-eight TgAb-positive patient specimens with undetectable Tg by both conventional LC-MS/MS (<0.15 ng/mL) and immunometric (<0.1 ng/mL) assays demonstrated measureable Tg concentrations by the new µLC-MS/MS assay in approximately one-third of the specimens, despite all patients being disease free at the time of collection, suggesting interference-free monitoring of low Tg levels may be feasible prior to the on-set of recurrent disease using a sensitive LC-MS/MS assay.

Keywords:  Autoantibody; Liquid chromatography; Mass spectrometry; Micro-flow; Thyroglobulin; Thyroid cancer

DOI:  https://doi.org/10.1016/j.clinms.2020.01.001
J Mass Spectrom Adv Clin Lab. 2021 Jan;19 34-45

Developing a robust, fast and reliable measurement method for the analysis of methylarginine derivatives and related metabolites.

Duygu Eryavuz Onmaz, Sedat Abusoglu, Havva Yaglioglu, Gulsum Abusoglu, Ali Unlu.

   Background: Nitric oxide (NO) plays an important role in endothelial homeostasis. Asymmetric dimethyl arginine (ADMA), L-N monomethyl arginine (L-NMMA) and symmetric dimethyl arginine (SDMA), which are derivatives of methylarginine, directly or indirectly reduce NO production. Therefore, these metabolites are an important risk factor for various diseases, including cardiovascular diseases. Numerous methods have been developed for the measurement of methylarginine derivatives, but various difficulties have been encountered. This study aimed to develop a reliable, fast and cost-effective method for the analysis and measurement of methylarginine derivatives (ADMA, SDMA, L-NMMA) and related metabolites (arginine, citrulline, homoarginine, ornithine), and to validate this method according to Clinical and Laboratory Standards Institute (CLSI) protocols.
Methods: For the analysis of ADMA, SDMA, L-NMMA, arginine, homoarginine, citrulline, ornithine, 200 Âµl of serum were precipitated with methanol, and subsequently derivatized with a butanol solution containing 5% acetyl chloride. Butyl derivatives were separated using a C18 reverse phase column with a 5 min run time. Detection of analytes was achieved by utilising the specific fragmentation patterns identified through tandem mass spectrometry.
Results: The method was linear for ADMA, SDMA, L-NMMA, ornithine, arginine, homoarginine and citrulline in the ranges of 0.023-6.0, 0.021-5.5, 0.019-5.0, 0.015-250, 0.015-250, 0.019-5 and 0.015-250 µM, respectively. The inter-assay CV% values for all analytes was less than 9.8%.
Conclusions: Data obtained from method validation studies shows that the developed method is highly sensitive, precise and accurate. Short analysis time, cost-effectiveness, and multiplexed analysis of these metabolites, with the same pretreatment steps, are the main advantages of the method.

Keywords:  ADMA; ADMA, asymmetric dimethyl arginine; CE, capillary electrophoresis; CE, collision energy; CLSI, The Clinical & Laboratory Standards Institute; CXP, collision cell exit potential; DDAH, dimethylaminohydrolase; DP, declustering potential; EP, enterance potential; FDA, Food and Drug Administration; GC–MS, gas chromatography–mass spectrometry; HPLC, high performance liquid chromatography; L-NMMA, L-N monomethyl arginine; LC-MS, liquid chromatography–mass spectrometry; LC-MS/MS, liquid chromatography tandem-mass spectrometry; MRM, multiple reaction monitoring; Methylarginines; NO, nitric oxide; NOS, nitric oxide synthase; PRMTs, protein arginine methyltransferases; SDMA, symmetric dimethyl arginine; Tandem mass spectrometry; Validation

DOI:  https://doi.org/10.1016/j.jmsacl.2021.02.002