bims-mascan 2022-01-09 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2022–01–09
23 papers selected by
Giovanny Rodríguez Blanco, University of Edinburgh

Palmitic acid: Enabling the tumor's nerves.
Proteomic Profiling of the Tumor Microenvironment.
The dysregulation of unsaturated fatty acid-based metabolomics in the MNNG-induced malignant transformation of Het-1A cells.
BoxCar and Library-Free Data-Independent Acquisition Substantially Improve the Depth, Range, and Completeness of Label-Free Quantitative Proteomics.
Diet, lipids, and antitumor immunity.
Metabolite discovery: Biochemistry's scientific driver.
Quantitative Chemoproteomic Profiling with Data-Independent Acquisition-Based Mass Spectrometry.
Immunometabolism in bladder cancer microenvironment.
Lipid Alterations in Systemic Sclerosis.
Insights and prospects for ion mobility-mass spectrometry in clinical chemistry.
Capillary Electrophoresis Coupled to Electrospray Ionization Tandem Mass Spectrometry for Ultra-Sensitive Proteomic Analysis of Limited Samples.
Quantification of succinic acid levels, linked to succinate dehydrogenase (SDH) dysfunctions, by an automated and fully validated liquid chromatography tandem mass spectrometry method suitable for multi-matrix applications.
Lipid analysis by ion mobility spectrometry combined with mass spectrometry: A brief update with a perspective on applications in the clinical laboratory.
Tandem Mass Spectrometry Molecular Networking as a Powerful and Efficient Tool for Drug Metabolism Studies.
Metabolic diversity within breast cancer brain-tropic cells determines metastatic fitness.
Glucose starvation induces a switch in the histone acetylome for activation of gluconeogenic and fat metabolism genes.
A review of chromatographic methods for bioactive tryptophan metabolites, kynurenine, kynurenic acid, quinolinic acid, and others, in biological fluids.
Subcellular metabolomics: Isolation, measurement, and applications.
MS Visualization and Interpretation Software (MS-VIS), a Tool for Visualizing Mass Spectra and Analyzing Mass Lists.
Supervised machine learning in the mass spectrometry laboratory: A tutorial.
STING orchestrates the crosstalk between polyunsaturated fatty acid metabolism and inflammatory responses.
HMDB 5.0: the Human Metabolome Database for 2022.
A Novel UPLC-MS Metabolomic Analysis-Based Strategy to Monitor the Course and Extent of iPSC Differentiation to Hepatocytes.

Cell Metab. 2022 Jan 04. pii: S1550-4131(21)00634-3. [Epub ahead of print]34(1): 7-9

Palmitic acid: Enabling the tumor's nerves.

H Furkan Alkan, Patricia Altea-Manzano, Sarah-Maria Fendt.

Diet can influence tumor aggressiveness. Recently in Nature, a study by Pascual et al. provided evidence that dietary palmitic acid induces an epigenetic memory by modulating particular histone methylation marks in cancer cells. This allows cancer cells to activate extracellular matrix secretion from Schwann cells of the tumor microenvironment, which ultimately potentiates metastasis initiation.

DOI: https://doi.org/10.1016/j.cmet.2021.12.015
Methods Mol Biol. 2022 ;2435 157-167

Proteomic Profiling of the Tumor Microenvironment.

Michela Capello, Hiroyuki Katayama, Samir M Hanash.

  The tumor microenvironment forms a complex pro-tumorigenic milieu constituted by extracellular matrix, surrounding stroma, infiltrating cell populations, and signaling molecules. Proteomic studies have the potential to reveal how individual cell populations within the tumor tissue modulate the microenvironment through protein secretion and consequently alter their protein expression and localization to adapt to this milieu. As a result, proteomic approaches have uncovered how these dynamic components communicate and promote tumor development and progression. The characterization of these mechanisms is relevant for the identification of clinically targetable pathways and for the development of diagnostic tools. Here we describe a method based on the isolation of individual cell compartments and the chromatographic fractionation of intact proteins, followed by enzymatic digestion of individual fractions, and mass-spectrometry analysis, for the profiling of tumor microenvironment cell populations.

Keywords:  Chromatographic fractionation; Mass spectrometry; Proteomics; SILAC; Tumor microenvironment

DOI:  https://doi.org/10.1007/978-1-0716-2014-4_11
Environ Sci Pollut Res Int. 2022 Jan 08.

The dysregulation of unsaturated fatty acid-based metabolomics in the MNNG-induced malignant transformation of Het-1A cells.

Hu Zhang, Qiwei Liu, Chao Zhao, Ying Zhang, Shizhi Wang, Ran Liu, Yuepu Pu, Lihong Yin.

  Studies have shown that environmental carcinogens exerted an important function in the high incidence of esophageal cancer (EC). Nitrosamines have been identified as important environmental carcinogens for EC. This study aimed to investigate the metabolic disturbances and new key toxicological markers in the malignant transformation process of normal esophageal epithelial cells (Het-1A) induced by MNNG (N-methyl-N'-nitro-N-nitrosoguanidine). Untargeted metabolomic and lipidomic profiling analysis by using ultra-high-performance liquid chromatography coupled with mass spectrometry (UHPLC-MS) were applied to explore the metabolic network alterations of Het-1A cells. The metabolomic results showed that significant alterations were observed in metabolic signatures between different generations (P5, P15, P25, P35) and the control cell group (P0). A total of 48 differential endogenous metabolites were screened and identified, mainly containing fatty acids, amino acids, and nucleotides. The differential metabolites were predominantly linked to the pathway of biosynthesis of unsaturated fatty acids metabolism. The cell lipidomic profiling revealed that the most differential lipids contained fatty acids (FAs), phosphatidylcholines (PC), phosphatidylethanolamines (PE), and phosphatidylserines (PS). The enrichment of the lipidomic pathway also confirmed that the lipid metabolism of biosynthesis of unsaturated fatty acids was the significant variation during the cell malignant transformation. Furthermore, we detected the expression of the upstream regulatory enzymes related to the unsaturated fatty acids to explore the regulation mechanism. The expression of stearoyl-CoA desaturase (SCD), ELOVL fatty acid elongase 1 (ELOVL1) promoted, and fatty acid desaturase 1 (FADS1) inhibited the key fatty acids of unsaturated fatty acids metabolism compared to the control cell group. Overall, our results revealed that lipid fatty acid metabolism was involved in the malignant transformation of Het-1A cells induced by MNNG and deepened the awareness of the carcinogenic mechanism of environmental exposure pollutants.

Keywords:  Esophageal cancer; Lipidomics; MNNG; Metabolomics; Regulatory enzyme

DOI:  https://doi.org/10.1007/s11356-021-17622-z
Anal Chem. 2022 Jan 03.

BoxCar and Library-Free Data-Independent Acquisition Substantially Improve the Depth, Range, and Completeness of Label-Free Quantitative Proteomics.

Devang Mehta, Sabine Scandola, R Glen Uhrig.

Data-dependent acquisition (DDA) methods are the current standard for quantitative proteomics in many biological systems. However, DDA preferentially measures highly abundant proteins and generates data that is plagued with missing values, requiring extensive imputation. Here, we demonstrate that library-free BoxCarDIA acquisition, combining MS1-level BoxCar acquisition with MS2-level data-independent acquisition (DIA) analysis, outperforms conventional DDA and other library-free DIA (directDIA) approaches. Using a combination of low- (HeLa cells) and high- (Arabidopsis thaliana cell culture) dynamic range sample types, we demonstrate that BoxCarDIA can achieve a 40% increase in protein quantification over DDA without offline fractionation or an increase in mass-spectrometer acquisition time. Further, we provide empirical evidence for substantial gains in dynamic range sampling that translates to deeper quantification of low-abundance protein classes under-represented in DDA and directDIA data. Unlike both DDA and directDIA, our new BoxCarDIA method does not require full MS1 scans while offering reproducible protein quantification between replicate injections and providing more robust biological inferences. Overall, our results advance the BoxCarDIA technique and establish it as the new method of choice for label-free quantitative proteomics across diverse sample types.

DOI: https://doi.org/10.1021/acs.analchem.1c03338
Cell Mol Immunol. 2022 Jan 05.

Diet, lipids, and antitumor immunity.

Hannah Prendeville, Lydia Lynch.

  Tumour growth and dissemination is largely dependent on nutrient availability. It has recently emerged that the tumour microenvironment is rich in a diverse array of lipids that increase in abundance with tumour progression and play a role in promoting tumour growth and metastasis. Here, we describe the pro-tumorigenic roles of lipid uptake, metabolism and synthesis and detail the therapeutic potential of targeting lipid metabolism in cancer. Additionally, we highlight new insights into the distinct immunosuppressive effects of lipids in the tumour microenvironment. Lipids threaten an anti-tumour environment whereby metabolic adaptation to lipid metabolism is linked to immune dysfunction. Finally, we describe the differential effects of commondietary lipids on cancer growth which may uncover a role for specific dietary regimens in association with traditional cancer therapies. Understanding the relationship between dietary lipids, tumour, and immune cells is important in the context of obesity which may reveal a possibility to harness the diet in the treatment of cancers.

Keywords:  Lipids; anti-tumour immunity; cancer; obesity; β-oxidation

DOI:  https://doi.org/10.1038/s41423-021-00781-x
Cell Metab. 2022 Jan 04. pii: S1550-4131(21)00533-7. [Epub ahead of print]34(1): 21-34

Metabolite discovery: Biochemistry's scientific driver.

Martin Giera, Oscar Yanes, Gary Siuzdak.

  Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase and the foundation of metabolism's rebirth in the 21st century; yet, characterizing newly observed metabolites has been an enduring obstacle. Crystallography and NMR spectroscopy have been of extraordinary importance, although their applicability in resolving metabolism's fine structure has been restricted by their intrinsic requirement of sufficient and sufficiently pure materials. Mass spectrometry has been a key technology, especially when coupled with high-performance separation technologies and emerging informatic and database solutions. Even more so, the collective of artificial intelligence technologies are rapidly evolving to help solve the metabolite characterization conundrum. This perspective describes this challenge, how it was historically addressed, and how metabolomics is evolving to address it today and in the future.

Keywords:  artificial intelligence; biochemistry; mass spectrometry; metabolites; nuclear magnetic resonance; structure; unknowns

DOI:  https://doi.org/10.1016/j.cmet.2021.11.005
J Am Chem Soc. 2022 Jan 05.

Quantitative Chemoproteomic Profiling with Data-Independent Acquisition-Based Mass Spectrometry.

Fan Yang, Guogeng Jia, Jiuzhou Guo, Yuan Liu, Chu Wang.

Activity-based protein profiling (ABPP) has emerged as a powerful and versatile tool to enable annotation of protein functions and discovery of targets of bioactive ligands in complex biological systems. It utilizes chemical probes to covalently label functional sites in proteins so that they can be enriched for mass spectrometry (MS)-based quantitative proteomics analysis. However, the semistochastic nature of data-dependent acquisition and high cost associated with isotopically encoded quantification reagents compromise the power of ABPP in multidimensional analysis and high-throughput screening, when a large number of samples need to be quantified in parallel. Here, we combine the data-independent acquisition (DIA) MS with ABPP to develop an efficient label-free quantitative chemical proteomic method, DIA-ABPP, with good reproducibility and high accuracy for high-throughput quantification. We demonstrated the power of DIA-ABPP for comprehensive profiling of functional cysteineome in three distinct applications, including dose-dependent quantification of cysteines' sensitivity toward a reactive metabolite, screening of ligandable cysteines with a covalent fragment library, and profiling of cysteinome fluctuation in circadian clock cycles. DIA-ABPP will open new opportunities for in-depth and multidimensional profiling of functional proteomes and interactions with bioactive small molecules in complex biological systems.

DOI: https://doi.org/10.1021/jacs.1c11053
Endocr Metab Immune Disord Drug Targets. 2022 Jan 04.

Immunometabolism in bladder cancer microenvironment.

Mohammad Javad Fattahi, Mohammad Reza Haghshenas, Abbas Ghaderi.

  The initiation and progression of bladder cancer (BC), is dependent on its tumor microenvironment (TME). On the other hand, cancer cells shape and train TME to support their development, respond to treatment and migration in an organism. Immune cells exert key roles in the BC microenvironment and have complex interactions with BC cells. These complicated interplays result in metabolic competition in the TME leading to nutrient deprivation, acidosis, hypoxia and metabolite accumulation, which impair immune cell function. Recent studies have demonstrated that immune cells functions are closely correlated with their metabolism. Immunometabolism describes the functional metabolic alterations that take place within immune cells and the role of these cells in directing metabolism and immune response in tissues or diseases such as cancer. Some molecules and their metabolites in the TME including glucose, fatty acids and amino acids can regulate the phenotype, function and metabolism of immune cells. Hence, here we describe some recent advances in immunometabolism and relate them to BC progression. A profound understanding of the metabolic reprogramming of BC cells and immune cells in the TME will offer novel opportunities for targeted therapies in future.

Keywords:  Bladder cancer; Immunometabolism; Metabolic Reprogramming; Tumor microenvironment

DOI:  https://doi.org/10.2174/1871530322666220104103905
Front Mol Biosci. 2021 ;8 761721

Lipid Alterations in Systemic Sclerosis.

Zuzanna Gogulska, Zaneta Smolenska, Jacek Turyn, Adriana Mika, Zbigniew Zdrojewski.

  Background: Systemic sclerosis (SSc) is an autoimmune disease with an elusive etiology and poor prognosis. Due to its diverse clinical presentation, a personalized approach is obligatory and needs to be based on a comprehensive biomarker panel. Therefore, particular metabolomic studies are necessary. Lipidomics addressed these issues and found disturbances in several crucial metabolic pathways. Aim of Review: The review aims to briefly summarize current knowledge related to lipid alterations in systemic sclerosis, highlight its importance, and encourage further research in this field. Key Scientific Concepts of Review: In this review, we summarized the studies on the lipidomic pattern, fatty acids, lipoproteins, cholesterol, eicosanoids, prostaglandins, leukotrienes, lysophospholipids, and sphingolipids in systemic sclerosis. Researchers demonstrated several alternate aspects of lipid metabolism. As we aimed to present our findings in a comprehensive view, we decided to divide our findings into three major groups: "serum lipoproteins," "fatty acids and derivatives," and "cellular membrane components," as we do believe they play a prominent role in SSc pathology.

Keywords:  biomarkers; lipidomics; lipids; metabolomics; systemic sclerosis

DOI:  https://doi.org/10.3389/fmolb.2021.761721
Expert Rev Proteomics. 2022 Jan 06.

Insights and prospects for ion mobility-mass spectrometry in clinical chemistry.

David C Koomen, Jody C May, John A McLean.

   INTRODUCTION: : Ion mobility-mass spectrometry is an emerging technology in the clinical setting for high throughput and high confidence molecular characterization from complex biological samples. Ion mobility spectrometry can provide isomer separations on the basis of molecular structure, the ability of which is increasing through technological developments that afford enhanced resolving power. Integrating multiple separation dimensions, such as liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) provide dramatic enhancements in the mitigation of molecular interferences for high accuracy clinical measurements.
AREAS COVERED: : Multidimensional separations with LC-IM-MS provide better selectivity and sensitivity in molecular analysis. Mass spectrometry imaging of tissues to inform spatial molecular distribution is improved by complementary ion mobility analyses. Biomarker identification in surgical environments is enhanced by intraoperative biochemical analysis with mass spectrometry and holds promise for integration with ion mobility spectrometry. New prospects in high resolving power ion mobility are enhancing analysis capabilities, such as distinguishing isomeric compounds.
EXPERT OPINION: : Ion mobility-mass spectrometry holds many prospects for the field of isomer identification, molecular imaging, and intraoperative tumor margin delineation in clinical settings. These advantages are afforded while maintaining fast analysis times and subsequently high throughput. High resolving power ion mobility will enhance these advantages further, in particular for analyses requiring high confidence isobaric selectivity and detection.

Keywords:  high-resolution ion mobility; ion mobility-mass spectrometry; isobars and isomers; mass spectrometry imaging; metabolomics; multidimensional separations

DOI:  https://doi.org/10.1080/14789450.2022.2026218
Anal Chem. 2022 Jan 04.

Capillary Electrophoresis Coupled to Electrospray Ionization Tandem Mass Spectrometry for Ultra-Sensitive Proteomic Analysis of Limited Samples.

Kendall R Johnson, Michal Greguš, James C Kostas, Alexander R Ivanov.

In this work, we developed an ultra-sensitive CE-MS/MS method for bottom-up proteomics analysis of limited samples, down to sub-nanogram levels of total protein. Analysis of 880 and 88 pg of the HeLa protein digest standard by CE-MS/MS yielded ∼1100 ± 46 and ∼160 ± 59 proteins, respectively, demonstrating higher protein and peptide identifications than the current state-of-the-art CE-MS/MS-based proteomic analyses with similar amounts of sample. To demonstrate potential applications of our ultra-sensitive CE-MS/MS method for the analysis of limited biological samples, we digested 500 and 1000 HeLa cells using a miniaturized in-solution digestion workflow. From 1-, 5-, and 10-cell equivalents injected from the resulted digests, we identified 744 ± 127, 1139 ± 24, and 1271 ± 6 proteins and 3353 ± 719, 5709 ± 513, and 8527 ± 114 peptide groups, respectively. Furthermore, we performed a comparative assessment of CE-MS/MS and two reversed-phased nano-liquid chromatography (RP-nLC-MS/MS) methods (monolithic and packed columns) for the analysis of a ∼10 ng HeLa protein digest standard. Our results demonstrate complementarity in the protein- and especially peptide-level identifications of the evaluated CE-MS- and RP-nLC-MS-based methods. The techniques were further assessed to detect post-translational modifications and highlight the strengths of the CE-MS/MS approach in identifying potentially important and biologically relevant modified peptides. With a migration window of ∼60 min, CE-MS/MS identified ∼2000 ± 53 proteins on average from a single injection of ∼8.8 ng of the HeLa protein digest standard. Additionally, an average of 232 ± 10 phosphopeptides and 377 ± 14 N-terminal acetylated peptides were identified in CE-MS/MS analyses at this sample amount, corresponding to 2- and 1.5-fold more identifications for each respective modification found by nLC-MS/MS methods.

DOI: https://doi.org/10.1021/acs.analchem.1c02929
J Chromatogr B Analyt Technol Biomed Life Sci. 2021 Dec 16. pii: S1570-0232(21)00566-3. [Epub ahead of print]1189 123085

Quantification of succinic acid levels, linked to succinate dehydrogenase (SDH) dysfunctions, by an automated and fully validated liquid chromatography tandem mass spectrometry method suitable for multi-matrix applications.

Constance Lamy, Clémence Mansard, Louis Blondel, Lionel Mercier, Angelo Paci, Sophie Broutin.

  The hallmarks of cancer include metabolism with deregulating cellular energetics. Dysfunctions in succinate dehydrogenase (SDH) metabolic enzyme activity, leading to an abnormal accumulation of succinic acid has been described in solid tumors but also in inflammation and ischemia reperfusion injury. Succinic acid is a potential biomarker of SDH related pathologies for diagnostic, evaluation of treatment response and follow-up of the disease. We developed a liquid chromatography tandem mass spectrometry (LC-MS/MS) method allowing a rapid, accurate and precise quantification of succinic acid levels in clinical (serum, urine) and preclinical (cellular pellets, supernatants) samples. 13C4 succinic acid disodium salt was used as internal standard and added to samples before a solid phase extraction (SPE) on Phenomenex STRATATM XL-A (200 mg - 3 mL) 33 µm cartridges. This method is automated by a Freedom EVO® platform from TECAN and succinic acid is separated on a C18 column combined to a Xevo® TQ-S micro Waters mass spectrometer with electrospray ionization (ESI) source. This biomedical analysis allows standard curves to be linear over the range 1.0-135.5 µM with r2 values > 0.999 and low matrix effects (<9.1 %). This method, which is validated according updated European Medicine Agency (EMA) guidelines, is accurate between-run (<11.0 %) and within-run (<7.8 %), precise between-run (<14.4 CV %) and within-run (<3.7 CV %), and is suitable for clinical and preclinical applications.

Keywords:  Biomarker; Endogenous compounds; Liquid chromatography tandem mass spectrometry; Oncometabolite; Succinate dehydrogenase dysfunctions; Succinic acid

DOI:  https://doi.org/10.1016/j.jchromb.2021.123085
J Mass Spectrom Adv Clin Lab. 2022 Jan;23 7-13

Lipid analysis by ion mobility spectrometry combined with mass spectrometry: A brief update with a perspective on applications in the clinical laboratory.

Joshua A Dubland.

  Ion mobility spectrometry (IMS) is an analytical technique where ions are separated in the gas phase based on their mobility through a buffer gas in the presence of an electric field. An ion passing through an IMS device has a characteristic collisional cross section (CCS) value that depends on the buffer gas used. IMS can be coupled with mass spectrometry (MS), which characterizes an ion based on a mass-to-charge ratio (m/z), to increase analytical specificity and provide further physicochemical information. In particular, IMS-MS is of ever-increasing interest for the analysis of lipids, which can be problematic to accurately identify and quantify in bodily fluids by liquid chromatography (LC) with MS alone due to the presence of isomers, isobars, and structurally similar analogs. IMS provides an additional layer of separation when combined with front-end LC approaches, thereby, enhancing peak capacity and analytical specificity. CCS (and also ion mobility drift time) can be plotted against m/z ion intensity and/or LC retention time in order to generate in-depth molecular profiles of a sample. Utilization of IMS-MS for routine clinical laboratory testing remains relatively unexplored, but areas do exist for potential implementation. A brief update is provided here on lipid analysis using IMS-MS with a perspective on some applications in the clinical laboratory.

Keywords:  CCS, collisional cross section; CV, compensation voltage; CVD, cardiovascular disease; Clinical analysis; DG, diacylglycerol; DMS, differential mobility spectrometry; DTIMS, drift tube ion mobility spectrometry; EV, elution voltage; FAIMS, field asymmetric waveform ion mobility spectrometry; FIA, flow injection analysis; FTICR, fourier-transform ion cyclotron resonance; HDL, high-density-lipoprotein; HRMS, high-resolution mass spectrometry; IMS, ion mobility spectrometry; IMS-MS, ion mobility spectrometry-mass spectrometry; Ion mobility spectrometry; LC, liquid chromatography; LDL, low-density-lipoprotein; LPC, lysophosphatidylcholine; Lipids; MALDI, matrix-assisted laser desorption/ionization; MS, mass spectrometry; Mass spectrometry; NBS, newborn screening; PC, glycerophosphocholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; RF, radio frequency; SLIM, structures for loss less ion manipulations; SM, sphingomyelin; SV, separation voltage; TG, triglyceride; TIMS, trapped ion mobility spectrometry; TOF, time-of-flight; TWIMS, traveling wave ion mobility spectrometry; VLDL, very-low-density lipoprotein; m/z, mass-to-charge ratio

DOI:  https://doi.org/10.1016/j.jmsacl.2021.12.005
Anal Chem. 2022 Jan 05.

Tandem Mass Spectrometry Molecular Networking as a Powerful and Efficient Tool for Drug Metabolism Studies.

Jun Sang Yu, Louis-Félix Nothias, Mingxun Wang, Dong Hyun Kim, Pieter C Dorrestein, Kyo Bin Kang, Hye Hyun Yoo.

Molecular networking (MN) has become a popular data analysis method for untargeted mass spectrometry (MS)/MS-based metabolomics. Recently, MN has been suggested as a powerful tool for drug metabolite identification, but its effectiveness for drug metabolism studies has not yet been benchmarked against existing strategies. In this study, we compared the performance of MN, mass defect filtering, Agilent MassHunter Metabolite ID, and Agilent Mass Profiler Professional workflows to annotate metabolites of sildenafil generated in an in vitro liver microsome-based metabolism study. Totally, 28 previously known metabolites with 15 additional unknown isomers and 25 unknown metabolites were found in this study. The comparison demonstrated that MN exhibited performances comparable or superior to those of the existing tools in terms of the number of detected metabolites (27 known metabolites and 22 unknown metabolites), ratio of false positives, and the amount of time and effort required for human labor-based postprocessing, which provided evidence of the efficiency of MN as a drug metabolite identification tool.

DOI: https://doi.org/10.1021/acs.analchem.1c04925
Cell Metab. 2022 Jan 04. pii: S1550-4131(21)00620-3. [Epub ahead of print]34(1): 90-105.e7

Metabolic diversity within breast cancer brain-tropic cells determines metastatic fitness.

Pravat Kumar Parida, Mauricio Marquez-Palencia, Vidhya Nair, Akash K Kaushik, Kangsan Kim, Jessica Sudderth, Eduardo Quesada-Diaz, Ambar Cajigas, Vamsidhara Vemireddy, Paula I Gonzalez-Ericsson, Melinda E Sanders, Bret C Mobley, Kenneth Huffman, Sunati Sahoo, Prasanna Alluri, Cheryl Lewis, Yan Peng, Robert M Bachoo, Carlos L Arteaga, Ariella B Hanker, Ralph J DeBerardinis, Srinivas Malladi.

  HER2+ breast cancer patients are presented with either synchronous (S-BM), latent (Lat), or metachronous (M-BM) brain metastases. However, the basis for disparate metastatic fitness among disseminated tumor cells of similar oncotype within a distal organ remains unknown. Here, employing brain metastatic models, we show that metabolic diversity and plasticity within brain-tropic cells determine metastatic fitness. Lactate secreted by aggressive metastatic cells or lactate supplementation to mice bearing Lat cells limits innate immunosurveillance and triggers overt metastasis. Attenuating lactate metabolism in S-BM impedes metastasis, while M-BM adapt and survive as residual disease. In contrast to S-BM, Lat and M-BM survive in equilibrium with innate immunosurveillance, oxidize glutamine, and maintain cellular redox homeostasis through the anionic amino acid transporter xCT. Moreover, xCT expression is significantly higher in matched M-BM brain metastatic samples compared to primary tumors from HER2+ breast cancer patients. Inhibiting xCT function attenuates residual disease and recurrence in these preclinical models.

Keywords:  HER2; breast cancer brain metastasis; immune surveillance; late recurrences; metabolism; metastasis; metastatic dormancy; metastatic latency; redox homeostasis; relapse

DOI:  https://doi.org/10.1016/j.cmet.2021.12.001
Mol Cell. 2022 Jan 06. pii: S1097-2765(21)01077-7. [Epub ahead of print]82(1): 60-74.e5

Glucose starvation induces a switch in the histone acetylome for activation of gluconeogenic and fat metabolism genes.

Wen-Chuan Hsieh, Benjamin M Sutter, Holly Ruess, Spencer D Barnes, Venkat S Malladi, Benjamin P Tu.

  Acetyl-CoA is a key intermediate situated at the intersection of many metabolic pathways. The reliance of histone acetylation on acetyl-CoA enables the coordination of gene expression with metabolic state. Abundant acetyl-CoA has been linked to the activation of genes involved in cell growth or tumorigenesis through histone acetylation. However, the role of histone acetylation in transcription under low levels of acetyl-CoA remains poorly understood. Here, we use a yeast starvation model to observe the dramatic alteration in the global occupancy of histone acetylation following carbon starvation; the location of histone acetylation marks shifts from growth-promoting genes to gluconeogenic and fat metabolism genes. This reallocation is mediated by both the histone deacetylase Rpd3p and the acetyltransferase Gcn5p, a component of the SAGA transcriptional coactivator. Our findings reveal an unexpected switch in the specificity of histone acetylation to promote pathways that generate acetyl-CoA for oxidation when acetyl-CoA is limiting.

Keywords:  Gcn5p; Rpd3p; SAGA; acetyl-CoA; environmental stress response; fat metabolism; gluconeogenesis; glucose starvation; histone acetylation; transcription

DOI:  https://doi.org/10.1016/j.molcel.2021.12.015
Biomed Chromatogr. 2022 Jan 02. e5308

A review of chromatographic methods for bioactive tryptophan metabolites, kynurenine, kynurenic acid, quinolinic acid, and others, in biological fluids.

Takeshi Fukushima, Maho Umino, Tatsuya Sakamoto, Mayu Onozato.

  sKynurenine (KYN) is synthesized from an essential amino acid, tryptophan by tryptophan 2,3-dioxygenase or indoleamine 2,3-dioxygenase via N-formyl- KYN in vivo. Subsequently, KYN acts as a precursor of some neuroactive metabolites such as kynurenic acid, quinolinic acid, and an important enzyme co-factor, nicotine adenine dinucleotide. These metabolites of tryptophan are a part of the "kynurenine pathway." In addition, KYN functions as an endogenous ligand for the aryl hydrocarbon receptor, which acts as a transcription factor. The levels of tryptophan metabolites are important for the assessment of the stage of neurological disorders, and hence, have garnered significant interest for clinical diagnosis. In this review, the detection of kynurenine, kynurenic acid, quinolinic acid, and other tryptophan metabolites performed via chromatographic methods such as HPLC using UV absorbance, fluorescence, and chromatographic-mass spectrometric detection is summarized.

Keywords:  Kynurenic acid; Kynurenine; Kynurenine pathway; Quinolinic acid; Tryptophan

DOI:  https://doi.org/10.1002/bmc.5308
J Pharm Biomed Anal. 2021 Dec 29. pii: S0731-7085(21)00668-3. [Epub ahead of print]210 114557

Subcellular metabolomics: Isolation, measurement, and applications.

Siyuan Qin, Yuxin Zhang, Yuan Tian, Fengguo Xu, Pei Zhang.

  Metabolomics, a technique that profiles global small molecules in biological samples, has been a pivotal tool for disease diagnosis and mechanism research. The sample type in metabolomics covers a wide range, including a variety of body fluids, tissues, and cells. However, little attention was paid to the smaller, relatively independent partition systems in cells, namely the organelles. The organelles are specific compartments/places where diverse metabolic activities are happening in an orderly manner. Metabolic disorders of organelles were found to occur in various pathological conditions such as inherited metabolic diseases, diabetes, cancer, and neurodegenerative diseases. However, at the cellular level, the metabolic outcomes of organelles and cytoplasm are superimposed interactively, making it difficult to describe the changes in subcellular compartments. Therefore, characterizing the metabolic pool in the compartmentalized system is of great significance for understanding the role of organelles in physiological functions and diseases. So far, there are very few research articles or reviews related to subcellular metabolomics. In this review, subcellular fractionation and metabolite analysis methods, as well as the application of subcellular metabolomics in the physiological and pathological studies are systematically reviewed, as a practical reference to promote the continued advancement in subcellular metabolomics.

Keywords:  Chemical derivatization; Mass spectrometry; Metabolic profiling; Organelles; Subcellular isolation; Subcellular metabolomics

DOI:  https://doi.org/10.1016/j.jpba.2021.114557
Rapid Commun Mass Spectrom. 2022 Jan 07. e9253

MS Visualization and Interpretation Software (MS-VIS), a Tool for Visualizing Mass Spectra and Analyzing Mass Lists.

Bjoern C Froehlich, Hardeep Kevin Gill, Anuj Joshi, David R Goodlett.

Generating figures of mass spectra fit for publication is often very time consuming. Often, software for analysis of mass spectra has very limited options for customizing the figure for publication. We developed R scripts for the visualization and labelling of mass spectra, but we found that requiring researchers to use R created a significant barrier-of-entry. Hence, we developed a web-hosted version, hosted at https://www.mass-spectrum.com/to make these scripts available to all users. This tool allows for broad customization of graphical parameters such as figure resolution and margins as well as axis customization and various colouring/sizing line width options. The peak labelling function allows selective display of information such as m/z, intensity and S/N ratio as well as custom text labels. Additionally, our tool allows extracting peak information for user defined m/z values from large numbers of mass lists. This makes it possible for researchers to quickly examine the peaks of interest in their data set without the need to manually browse the data. With this tool, we hope to save researcher's time in making figures for publication purposes. This website provides an easy tool for plotting and labelling mass spectra to generate publication quality figures.

DOI: https://doi.org/10.1002/rcm.9253
J Mass Spectrom Adv Clin Lab. 2022 Jan;23 1-6

Supervised machine learning in the mass spectrometry laboratory: A tutorial.

Edward S Lee, Thomas J S Durant.

  As the demand for laboratory testing by mass spectrometry increases, so does the need for automated methods for data analysis. Clinical mass spectrometry (MS) data is particularly well-suited for machine learning (ML) methods, which deal nicely with structured and discrete data elements. The alignment of these two fields offers a promising synergy that can be used to optimize workflows, improve result quality, and enhance our understanding of high-dimensional datasets and their inherent relationship with disease. In recent years, there has been an increasing number of publications that examine the capabilities of ML-based software in the context of chromatography and MS. However, given the historically distant nature between the fields of clinical chemistry and computer science, there is an opportunity to improve technological literacy of ML-based software within the clinical laboratory scientist community. To this end, we present a basic overview of ML and a tutorial of an ML-based experiment using a previously published MS dataset. The purpose of this paper is to describe the fundamental principles of supervised ML, outline the steps that are classically involved in an ML-based experiment, and discuss the purpose of good ML practice in the context of a binary MS classification problem.

Keywords:  Amino acid; Artificial intelligence; CART, Classification and Regression Trees; ML, Machine Learning; MS, Mass Spectrometry; Mass spectrometry; NLL, Negative Log Loss; PAA, Plasma Amino Acid; PR, Precision-Recall; PRAUC, Area Under the Precision-Recall Curve; RL, Reinforcement Learning; ROC, Receiver Operator Curve; SCF, Supplemental Code File; Supervised machine learning; XGBT, Extreme Gradient Boosted Trees; Xgboost

DOI:  https://doi.org/10.1016/j.jmsacl.2021.12.001
Cell Metab. 2022 Jan 04. pii: S1550-4131(21)00626-4. [Epub ahead of print]34(1): 125-139.e8

STING orchestrates the crosstalk between polyunsaturated fatty acid metabolism and inflammatory responses.

Isabelle K Vila, Hanane Chamma, Alizée Steer, Mathilde Saccas, Clara Taffoni, Evgenia Turtoi, Line S Reinert, Saqib Hussain, Johanna Marines, Lei Jin, Xavier Bonnefont, Mathieu Hubert, Olivier Schwartz, Soren R Paludan, Gaetan Van Simaeys, Gilles Doumont, Bijan Sobhian, Dimitrios Vlachakis, Andrei Turtoi, Nadine Laguette.

  Concerted alteration of immune and metabolic homeostasis underlies several inflammation-related pathologies, ranging from metabolic syndrome to infectious diseases. Here, we explored the coordination of nucleic acid-dependent inflammatory responses and metabolic homeostasis. We reveal that the STING (stimulator of interferon genes) protein regulates metabolic homeostasis through inhibition of the fatty acid desaturase 2 (FADS2) rate-limiting enzyme in polyunsaturated fatty acid (PUFA) desaturation. STING ablation and agonist-mediated degradation increased FADS2-associated desaturase activity and led to accumulation of PUFA derivatives that drive thermogenesis. STING agonists directly activated FADS2-dependent desaturation, promoting metabolic alterations. PUFAs in turn inhibited STING, thereby regulating antiviral responses and contributing to resolving STING-associated inflammation. Thus, we have unveiled a negative regulatory feedback loop between STING and FADS2 that fine-tunes inflammatory responses. Our results highlight the role of metabolic alterations in human pathologies associated with aberrant STING activation and STING-targeting therapies.

Keywords:  FADS2; STING; cGAS; cytosolic DNA; delta-6 Desaturase; inflammation; interferon responses; metabolism; nucleic acid immunity; polyunsaturated fatty acids

DOI:  https://doi.org/10.1016/j.cmet.2021.12.007
Nucleic Acids Res. 2022 Jan 07. 50(D1): D622-D631

HMDB 5.0: the Human Metabolome Database for 2022.

David S Wishart, AnChi Guo, Eponine Oler, Fei Wang, Afia Anjum, Harrison Peters, Raynard Dizon, Zinat Sayeeda, Siyang Tian, Brian L Lee, Mark Berjanskii, Robert Mah, Mai Yamamoto, Juan Jovel, Claudia Torres-Calzada, Mickel Hiebert-Giesbrecht, Vicki W Lui, Dorna Varshavi, Dorsa Varshavi, Dana Allen, David Arndt, Nitya Khetarpal, Aadhavya Sivakumaran, Karxena Harford, Selena Sanford, Kristen Yee, Xuan Cao, Zachary Budinski, Jaanus Liigand, Lun Zhang, Jiamin Zheng, Rupasri Mandal, Naama Karu, Maija Dambrova, Helgi B Schiöth, Russell Greiner, Vasuk Gautam.

The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicted NMR spectra, more accurately predicted MS spectra, predicted retention indices and predicted collision cross section data and (v) enhancements to the HMDB's search functions to facilitate better compound identification. Many other minor improvements and updates to the content, the interface, and general performance of the HMDB website have also been made. Overall, we believe these upgrades and updates should greatly enhance the HMDB's ease of use and its potential applications not only in human metabolomics but also in exposomics, lipidomics, nutritional science, biochemistry and clinical chemistry.

DOI: https://doi.org/10.1093/nar/gkab1062
J Proteome Res. 2022 Jan 04.

A Novel UPLC-MS Metabolomic Analysis-Based Strategy to Monitor the Course and Extent of iPSC Differentiation to Hepatocytes.

Marta Moreno-Torres, Manoj Kumar, Guillem García-Llorens, Guillermo Quintás, Tine Tricot, Ruben Boon, Laia Tolosa, Burak Toprakhisar, Francois Chesnais, Catherine Verfaillie, José V Castell.

  Typical protocols to differentiate induced pluripotent stem cells (iPSCs) from hepatocyte-like cells (HLCs) imply complex strategies that include transfection with key hepatic transcription factors and the addition to culture media of nutrients, growth factors, and cytokines. A main constraint to evaluate the hepatic phenotype achieved arises from the way the grade of differentiation is determined. Currently, it relies on the assessment of the expression of a limited number of hepatic gene transcripts, less frequently by assessing certain hepatic metabolic functions, and rarely by the global metabolic performance of differentiated cells. We envisaged a new strategy to assess the extent of differentiation achieved, based on the analysis of the cellular metabolome along the differentiation process and its quantitative comparison with that of primary human hepatocytes (PHHs). To validate our approach, we examined the changes in the metabolome of three iPSC progenies (transfected with/without key transcription factors), cultured in three differentiation media, and compared them to PHHs. Results revealed consistent metabolome changes along differentiation and evidenced the factors that more strongly promote changes in the metabolome. The integrated dissimilarities between the PHHs and HLCs retrieved metabolomes were used as a numerical reference for quantifying the degree of iPSCs differentiation. This newly developed metabolome-analysis approach evidenced its utility in assisting us to select a cell's source, culture conditions, and differentiation media, to achieve better-differentiated HLCs.

Keywords:  UPLC-MS; iPSC differentiation; metabolic pathways; metabolomics; primary human hepatocytes

DOI:  https://doi.org/10.1021/acs.jproteome.1c00779