bims-mascan 2024-05-26 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2024–05–26
twenty-two papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Lipid availability influences ferroptosis sensitivity in cancer cells by regulating polyunsaturated fatty acid trafficking.
Fully automated workflow for integrated sample digestion and Evotip loading enabling high-throughput clinical proteomics.
Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery.
An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing.
FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Data Sets Built on FragPipe.
Improved drug target deconvolution with PISA-DIA using an extended, overlapping temperature gradient.
Analytical methods for stable isotope labeling to elucidate rapid auxin kinetics in Arabidopsis thaliana.
Reproducible mass spectrometry data processing and compound annotation in MZmine 3.
Maximizing Glycoproteomics Results through an Integrated Parallel Accumulation Serial Fragmentation Workflow.
Targeting lipid reprogramming in the tumor microenvironment by traditional Chinese medicines as a potential cancer treatment.
Mass Spectrometric Analysis of Purine Intermediary Metabolism Indicates Cyanide Induces Purine Catabolism in Rabbits.
Direct Infusion Mass Spectrometry to Rapidly Map Metabolic Flux of Substrates Labeled with Stable Isotopes.
New analytical methods focusing on polar metabolite analysis in mass spectrometry and NMR-based metabolomics.
Glycosaminoglycan-mediated lipoprotein uptake protects cancer cells from ferroptosis.
GCMS-ID: a webserver for identifying compounds from gas chromatography mass spectrometry experiments.
In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level.
The OGT-c-Myc-PDK2 axis rewires the TCA cycle and promotes colorectal tumor growth.
Data processing for high-throughput mass spectrometry in drug discovery.
Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules.
Assessment of Untargeted Metabolomics by Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry to Define Breast Cancer Liquid Biopsy-Based Biomarkers in Plasma Samples.
Mitochondrial respiratory function is preserved under cysteine starvation via glutathione catabolism in NSCLC.
The Metabolic and Lipidomic Fingerprint of Torin1 Exposure in Mouse Embryonic Fibroblasts Using Untargeted Metabolomics.

bioRxiv. 2024 May 09. pii: 2024.05.06.592780. [Epub ahead of print]

Lipid availability influences ferroptosis sensitivity in cancer cells by regulating polyunsaturated fatty acid trafficking.

Kelly H Sokol, Cameron J Lee, Thomas J Rogers, Althea Waldhart, Abigail E Ellis, Sahithi Madireddy, Samuel R Daniels, Xinyu Ye, Mary Olesnavich, Amy Johnson, Benjamin R Furness, Ryan D Sheldon, Evan C Lien.

Ferroptosis is a form of cell death caused by lipid peroxidation that is emerging as a target for cancer therapy, highlighting the need to identify factors that govern ferroptosis susceptibility. Lipid peroxidation occurs primarily on phospholipids containing polyunsaturated fatty acids (PUFAs). Here, we show that even though extracellular lipid limitation reduces cellular PUFA levels, lipid-starved cancer cells are paradoxically more sensitive to ferroptosis. Using mass spectrometry-based lipidomics with stable isotope fatty acid labeling, we show that lipid limitation induces a fatty acid trafficking pathway in which PUFAs are liberated from triglycerides to synthesize highly unsaturated PUFAs such as arachidonic acid and adrenic acid. These PUFAs then accumulate in phospholipids, particularly ether phospholipids, to promote ferroptosis sensitivity. Therefore, PUFA levels within cancer cells do not necessarily correlate with ferroptosis susceptibility. Rather, how cancer cells respond to extracellular lipid levels by trafficking PUFAs into proper phospholipid pools dictates their sensitivity to ferroptosis.

DOI: https://doi.org/10.1101/2024.05.06.592780
Mol Cell Proteomics. 2024 May 20. pii: S1535-9476(24)00080-X. [Epub ahead of print] 100790

Fully automated workflow for integrated sample digestion and Evotip loading enabling high-throughput clinical proteomics.

Anders H Kverneland, Florian Harking, Joel Mario Vej-Nielsen, Magnus Huusfeldt, Dorte B Bekker-Jensen, Inge Marie Svane, Nicolai Bache, Jesper V Olsen.

  Protein identification and quantification is an important tool for biomarker discovery. With the increased sensitivity and speed of modern mass spectrometers, sample-preparation remains a bottleneck for studying large cohorts. To address this issue, we prepared and evaluated a simple and efficient workflow on the Opentrons OT-2 (OT-2) robot that combines sample digestion, cleanup and loading on Evotips in a fully automated manner, allowing the processing of up to 192 samples in 6 hours. Analysis of 192 automated HeLa cell sample preparations consistently identified ∼8000 protein groups and ∼130,000 peptide precursors with an 11.5 minute active LC gradient with the Evosep One and narrow-window data-independent acquisition with the Orbitrap Astral mass spectrometer providing a throughput of 100-samples-per-day. Our results demonstrate a highly sensitive workflow yielding both reproducibility and stability at low sample inputs. The workflow is optimized for minimal sample starting amount to reduce the costs for reagents needed for sample preparation, which is critical when analyzing large biological cohorts. Building on the digesting workflow, we incorporated an automated phosphopeptide enrichment step using magnetic Ti-IMAC beads. This allows for a fully automated proteome and phosphoproteome sample preparation in a single step with high sensitivity. Using the integrated digestion and Evotip loading workflow, we evaluated the effects of cancer immune therapy on the plasma proteome in metastatic melanoma patients.

Keywords:  Mass spectrometry; biomarker discovery; cancer immune therapy; plasma proteomics; workflow automation

DOI:  https://doi.org/10.1016/j.mcpro.2024.100790
Metabolites. 2024 May 11. pii: 280. [Epub ahead of print]14(5):

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery.

Masoumeh Dorrani, Jifang Zhao, Nihel Bekhti, Alessia Trimigno, Sangil Min, Jongwon Ha, Ahram Han, Elizabeth O'Day, Jurre J Kamphorst.

  Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

Keywords:  HILIC; U-13C-metabolite internal standard; biomarkers; clinical metabolomics; hydrophilic interaction liquid chromatography; triple quadrupole mass spectrometry

DOI:  https://doi.org/10.3390/metabo14050280
Nat Protoc. 2024 May 20.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing.

Devon Kohler, Mateusz Staniak, Fengchao Yu, Alexey I Nesvizhskii, Olga Vitek.

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

DOI: https://doi.org/10.1038/s41596-024-01000-3
J Proteome Res. 2024 May 24.

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Data Sets Built on FragPipe.

Edgar Manriquez-Sandoval, Joy Brewer, Gabriela Lule, Samanta Lopez, Stephen D Fried.

  Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a data merging scheme and a protein-centric multiple hypothesis correction scheme, enabling processed LiP-MS data sets to be more robust and less redundant. These improvements strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.

Keywords:  bioinformatics; computational tools; data analysis workflow; limited proteolysis; structural proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.3c00887
Proteomics. 2024 May 20. e2300644

Improved drug target deconvolution with PISA-DIA using an extended, overlapping temperature gradient.

Samantha J Emery-Corbin, Jumana M Yousef, Subash Adhikari, Fransisca Sumardy, Duong Nhu, Mark F van Delft, Guillaume Lessene, Jerzy Dziekan, Andrew I Webb, Laura F Dagley.

  Thermal proteome profiling (TPP) is a powerful tool for drug target deconvolution. Recently, data-independent acquisition mass spectrometry (DIA-MS) approaches have demonstrated significant improvements to depth and missingness in proteome data, but traditional TPP (a.k.a. CEllular Thermal Shift Assay "CETSA") workflows typically employ multiplexing reagents reliant on data-dependent acquisition (DDA). Herein, we introduce a new experimental design for the Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA). We highlight the proteome coverage and sensitivity achieved by using multiple overlapping thermal gradients alongside DIA-MS, which maximizes efficiencies in PISA sample concatenation and safeguards against missing protein targets that exist at high melting temperatures. We demonstrate our extended PISA-DIA design has superior proteome coverage as compared to using tandem-mass tags (TMT) necessitating DDA-MS analysis. Importantly, we demonstrate our PISA-DIA approach has the quantitative and statistical rigor using A-1331852, a specific inhibitor of BCL-xL. Due to the high melt temperature of this protein target, we utilized our extended multiple gradient PISA-DIA workflow to identify BCL-xL. We assert our novel overlapping gradient PISA-DIA-MS approach is ideal for unbiased drug target deconvolution, spanning a large temperature range whilst minimizing target dropout between gradients, increasing the likelihood of resolving the protein targets of novel compounds.

Keywords:  Proteome Integral Solubility Alteration; data independent acquisition (DIA); mass spectrometry; protein stability; proteomics; target engagement; thermal proteome profiling

DOI:  https://doi.org/10.1002/pmic.202300644
PLoS One. 2024 ;19(5): e0303992

Analytical methods for stable isotope labeling to elucidate rapid auxin kinetics in Arabidopsis thaliana.

Qian Tang, Molly Tillmann, Jerry D Cohen.

The phytohormone auxin plays a critical role in plant growth and development. Despite significant progress in elucidating metabolic pathways of the primary bioactive auxin, indole-3-acetic acid (IAA), over the past few decades, key components such as intermediates and enzymes have not been fully characterized, and the dynamic regulation of IAA metabolism in response to environmental signals has not been completely revealed. In this study, we established a protocol employing a highly sensitive liquid chromatography-mass spectrometry (LC-MS) instrumentation and a rapid stable isotope labeling approach. We treated Arabidopsis seedlings with two stable isotope labeled precursors ([13C6]anthranilate and [13C8, 15N1]indole) and monitored the label incorporation into proposed indolic compounds involved in IAA biosynthetic pathways. This Stable Isotope Labeled Kinetics (SILK) method allowed us to trace the turnover rates of IAA pathway precursors and product concurrently with a time scale of seconds to minutes. By measuring the entire pathways over time and using different isotopic tracer techniques, we demonstrated that these methods offer more detailed information about this complex interacting network of IAA biosynthesis, and should prove to be useful for studying auxin metabolic network in vivo in a variety of plant tissues and under different environmental conditions.

DOI: https://doi.org/10.1371/journal.pone.0303992
Nat Protoc. 2024 May 20.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3.

Steffen Heuckeroth, Tito Damiani, Aleksandr Smirnov, Olena Mokshyna, Corinna Brungs, Ansgar Korf, Joshua David Smith, Paolo Stincone, Nicola Dreolin, Louis-Félix Nothias, Tuulia Hyötyläinen, Matej Orešič, Uwe Karst, Pieter C Dorrestein, Daniel Petras, Xiuxia Du, Justin J J van der Hooft, Robin Schmid, Tomáš Pluskal.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

DOI: https://doi.org/10.1038/s41596-024-00996-y
Anal Chem. 2024 May 22.

Maximizing Glycoproteomics Results through an Integrated Parallel Accumulation Serial Fragmentation Workflow.

Melissa Baerenfaenger, Merel A Post, Fokje Zijlstra, Alain J van Gool, Dirk J Lefeber, Hans J C T Wessels.

Glycoproteins play important roles in numerous physiological processes and are often implicated in disease. Analysis of site-specific protein glycobiology through glycoproteomics has evolved rapidly in recent years thanks to hardware and software innovations. Particularly, the introduction of parallel accumulation serial fragmentation (PASEF) on hybrid trapped ion mobility time-of-flight mass spectrometry instruments combined deep proteome sequencing with separation of (near-)isobaric precursor ions or converging isotope envelopes through ion mobility separation. However, the reported use of PASEF in integrated glycoproteomics workflows to comprehensively capture the glycoproteome is still limited. To this end, we developed an integrated methodology using timsTOF Pro 2 to enhance N-glycopeptide identifications in complex mixtures. We systematically optimized the ion optics tuning, collision energies, mobility isolation width, and the use of dopant-enriched nitrogen gas (DEN). Thus, we obtained a marked increase in unique glycopeptide identification rates compared to standard proteomics settings, showcasing our results on a large set of glycopeptides. With short liquid chromatography gradients of 30 min, we increased the number of unique N-glycopeptide identifications in human plasma samples from around 100 identifications under standard proteomics conditions to up to 1500 with our optimized glycoproteomics approach, highlighting the need for tailored optimizations to obtain comprehensive data.

DOI: https://doi.org/10.1021/acs.analchem.3c05874
Heliyon. 2024 May 15. 10(9): e30807

Targeting lipid reprogramming in the tumor microenvironment by traditional Chinese medicines as a potential cancer treatment.

Qian Zuo, Yingchao Wu, Yuyu Hu, Cui Shao, Yuqi Liang, Liushan Chen, Qianqian Guo, Ping Huang, Qianjun Chen.

  In the last ten years, there has been a notable rise in the study of metabolic abnormalities in cancer cells. However, compared to glucose or glutamine metabolism, less attention has been paid to the importance of lipid metabolism in tumorigenesis. Recent developments in lipidomics technologies have allowed for detailed analysis of lipid profiles within cancer cells and other cellular players present within the tumor microenvironment (TME). Traditional Chinese medicine (TCM) and its bioactive components have a long history of use in cancer treatments and are also being studied for their potential role in regulating metabolic reprogramming within TME. This review focuses on four core abnormalities altered by lipid reprogramming in cancer cells: de novo synthesis and exogenous uptake of fatty acids (FAs), upregulated fatty acid oxidation (FAO), cholesterol accumulation, which offer benefits for tumor growth and metastasis. The review also discusses how altered lipid metabolism impacts infiltrating immune cell function and phenotype as these interactions between cancer-stromal become more pronounced during tumor progression. Finally, recent literature is highlighted regarding how cancer cells can be metabolically reprogrammed by specific Chinese herbal components with potential therapeutic benefits related to lipid metabolic and signaling pathways.

Keywords:  Fatty acids; Lipid droplet; Lipid reprogramming; Traditional Chinese medicines; Tumor microenvironment; de novo lipogenesis

DOI:  https://doi.org/10.1016/j.heliyon.2024.e30807
Metabolites. 2024 May 10. pii: 279. [Epub ahead of print]14(5):

Mass Spectrometric Analysis of Purine Intermediary Metabolism Indicates Cyanide Induces Purine Catabolism in Rabbits.

Jordan Morningstar, Jangwoen Lee, Sari Mahon, Matthew Brenner, Anjali K Nath.

  Purines are the building blocks of DNA/RNA, energy substrates, and cofactors. Purine metabolites, including ATP, GTP, NADH, and coenzyme A, are essential molecules in diverse biological processes such as energy metabolism, signal transduction, and enzyme activity. When purine levels increase, excess purines are either recycled to synthesize purine metabolites or catabolized to the end product uric acid. Purine catabolism increases during states of low oxygen tension (hypoxia and ischemia), but this metabolic pathway is incompletely understood in the context of histotoxic hypoxia (i.e., inhibition of oxygen utilization despite normal oxygen tension). In rabbits exposed to cyanide-a classical histotoxic hypoxia agent-we demonstrated significant increases in several concordant metabolites in the purine catabolic pathway (including plasma levels of uric acid, xanthosine, xanthine, hypoxanthine, and inosine) via mass spectrometry-based metabolite profiling. Pharmacological inhibition of the purine catabolic pathway with oxypurinol mitigated the deleterious effects of cyanide on skeletal muscle cytochrome c oxidase redox state, measured by non-invasive diffuse optical spectroscopy. Finally, plasma uric acid levels correlated strongly with those of lactic acid, an established clinical biomarker of cyanide exposure, in addition to a tissue biomarker of cyanide exposure (skeletal muscle cytochrome c oxidase redox state). Cumulatively, these findings not only shed light on the in vivo role(s) of cyanide but also have implications in the field of medical countermeasure (MCM) development.

Keywords:  allopurinol; biomarker; cyanide; cytochrome c oxidase (Complex IV); histotoxic hypoxia; mass spectrometry; nucleoside/nucleotide metabolism; preclinical animal model; purine; uric acid

DOI:  https://doi.org/10.3390/metabo14050279
Metabolites. 2024 Apr 25. pii: 246. [Epub ahead of print]14(5):

Direct Infusion Mass Spectrometry to Rapidly Map Metabolic Flux of Substrates Labeled with Stable Isotopes.

Nils W F Meijer, Susan Zwakenberg, Johan Gerrits, Denise Westland, Arif I Ardisasmita, Sabine A Fuchs, Nanda M Verhoeven-Duif, Judith J M Jans, Fried J T Zwartkruis.

  Direct infusion-high-resolution mass spectrometry (DI-HRMS) allows for rapid profiling of complex mixtures of metabolites in blood, cerebrospinal fluid, tissue samples and cultured cells. Here, we present a DI-HRMS method suitable for the rapid determination of metabolic fluxes of isotopically labeled substrates in cultured cells and organoids. We adapted an automated annotation pipeline by selecting labeled adducts that best represent the majority of 13C and/or 15N-labeled glycolytic and tricarboxylic acid cycle intermediates as well as a number of their derivatives. Furthermore, valine, leucine and several of their degradation products were included. We show that DI-HRMS can determine anticipated and unanticipated alterations in metabolic fluxes along these pathways that result from the genetic alteration of single metabolic enzymes, including pyruvate dehydrogenase (PDHA1) and glutaminase (GLS). In addition, it can precisely pinpoint metabolic adaptations to the loss of methylmalonyl-CoA mutase in patient-derived liver organoids. Our results highlight the power of DI-HRMS in combination with stable isotopically labeled compounds as an efficient screening method for fluxomics.

Keywords:  TCA cycle; direct infusion–high-resolution mass spectrometry; glutaminolysis; glycolysis; isotope tracing; organoids; patient material

DOI:  https://doi.org/10.3390/metabo14050246
Curr Opin Chem Biol. 2024 May 20. pii: S1367-5931(24)00042-5. [Epub ahead of print]80 102466

New analytical methods focusing on polar metabolite analysis in mass spectrometry and NMR-based metabolomics.

James McCullagh, Fay Probert.

  Following in the footsteps of genomics and proteomics, metabolomics has revolutionised the way we investigate and understand biological systems. Rapid development in the last 25 years has been driven largely by technical innovations in mass spectrometry and nuclear magnetic resonance spectroscopy. However, despite the modest size of metabolomes relative to proteomes and genomes, methodological capabilities for robust, comprehensive metabolite analysis remain a major challenge. Therefore, development of new methods and techniques remains vital for progress in the field. Here, we review developments in LC-MS, GC-MS and NMR methods in the last few years that have enhanced quantitative and comprehensive metabolome coverage, highlighting the techniques involved, their technical capabilities, relative performance, and potential impact.

Keywords:  HILIC-MS; Hyphenated methods; IC-MS; Mass spectrometry; Methods; Mixed mode; NMR; Pure-shift NMR; Quantification; Ultrafast NMR

DOI:  https://doi.org/10.1016/j.cbpa.2024.102466
bioRxiv. 2024 May 13. pii: 2024.05.13.593939. [Epub ahead of print]

Glycosaminoglycan-mediated lipoprotein uptake protects cancer cells from ferroptosis.

Dylan Calhoon, Lingjie Sang, Divya Bezwada, Nathaniel Kim, Amrita Basu, Sheng-Chieh Hsu, Anastasia Pimentel, Bailey Brooks, Konnor La, Ana Paulina Serrano, Daniel L Cassidy, Ling Cai, Vanina Toffessi-Tcheuyap, Vitaly Margulis, Feng Cai, James Brugarolas, Ryan J Weiss, Ralph J DeBerardinis, Kıvanç Birsoy, Javier Garcia-Bermudez.

Lipids are essential for tumours because of their structural, energetic, and signaling roles. While many cancer cells upregulate lipid synthesis, growing evidence suggests that tumours simultaneously intensify the uptake of circulating lipids carried by lipoproteins. Which mechanisms promote the uptake of extracellular lipids, and how this pool of lipids contributes to cancer progression, are poorly understood. Here, using functional genetic screens, we find that lipoprotein uptake confers resistance to lipid peroxidation and ferroptotic cell death. Lipoprotein supplementation robustly inhibits ferroptosis across numerous cancer types. Mechanistically, cancer cells take up lipoproteins through a pathway dependent on sulfated glycosaminoglycans (GAGs) linked to cell-surface proteoglycans. Tumour GAGs are a major determinant of the uptake of both low and high density lipoproteins. Impairment of glycosaminoglycan synthesis or acute degradation of surface GAGs decreases the uptake of lipoproteins, sensitizes cells to ferroptosis and reduces tumour growth in mice. We also find that human clear cell renal cell carcinomas, a distinctively lipid-rich tumour type, display elevated levels of lipoprotein-derived antioxidants and the GAG chondroitin sulfate than non-malignant human kidney. Altogether, our work identifies lipoprotein uptake as an essential anti-ferroptotic mechanism for cancer cells to overcome lipid oxidative stress in vivo, and reveals GAG biosynthesis as an unexpected mediator of this process.

DOI: https://doi.org/10.1101/2024.05.13.593939
Nucleic Acids Res. 2024 May 23. pii: gkae425. [Epub ahead of print]

GCMS-ID: a webserver for identifying compounds from gas chromatography mass spectrometry experiments.

Julia Wakoli, Afia Anjum, Tanvir Sajed, Eponine Oler, Fei Wang, Vasuk Gautam, Marcia LeVatte, David S Wishart.

GCMS-ID (Gas Chromatography Mass Spectrometry compound IDentifier) is a webserver designed to enable the identification of compounds from GC-MS experiments. GC-MS instruments produce both electron impact mass spectra (EI-MS) and retention index (RI) data for as few as one, to as many as hundreds of different compounds. Matching the measured EI-MS, RI or EI-MS + RI data to experimentally collected EI-MS and/or RI reference libraries allows facile compound identification. However, the number of available experimental RI and EI-MS reference spectra, especially for metabolomics or exposomics-related studies, is disappointingly small. Using machine learning to accurately predict the EI-MS spectra and/or RIs for millions of metabolomics and/or exposomics-relevant compounds could (partially) solve this spectral matching problem. This computational approach to compound identification is called in silico metabolomics. GCMS-ID brings this concept of in silico metabolomics closer to reality by intelligently integrating two of our previously published webservers: CFM-EI and RIpred. CFM-EI is an EI-MS spectral prediction webserver, and RIpred is a Kovats RI prediction webserver. We have found that GCMS-ID can accurately identify compounds from experimental RI, EI-MS or RI + EI-MS data through matching to its own large library of >1 million predicted RI/EI-MS values generated for metabolomics/exposomics-relevant compounds. GCMS-ID can also predict the RI or EI-MS spectrum from a user-submitted structure or annotate a user-submitted EI-MS spectrum. GCMS-ID is freely available at https://gcms-id.ca/.

DOI: https://doi.org/10.1093/nar/gkae425
Nat Commun. 2024 May 23. 15(1): 4387

In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level.

Shaojie Qin, Yi Zhang, Mingying Shi, Daiyu Miao, Jiansen Lu, Lu Wen, Yu Bai.

Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.

DOI: https://doi.org/10.1038/s41467-024-48865-2
Cell Death Differ. 2024 May 22.

The OGT-c-Myc-PDK2 axis rewires the TCA cycle and promotes colorectal tumor growth.

Huijuan Wang, Jie Sun, Haofan Sun, Yifei Wang, Bingyi Lin, Liming Wu, Weijie Qin, Qiang Zhu, Wen Yi.

Deregulated glucose metabolism termed the "Warburg effect" is a fundamental feature of cancers, including the colorectal cancer. This is typically characterized with an increased rate of glycolysis, and a concomitant reduced rate of the tricarboxylic acid (TCA) cycle metabolism as compared to the normal cells. How the TCA cycle is manipulated in cancer cells remains unknown. Here, we show that O-linked N-acetylglucosamine (O-GlcNAc) regulates the TCA cycle in colorectal cancer cells. Depletion of OGT, the sole transferase of O-GlcNAc, significantly increases the TCA cycle metabolism in colorectal cancer cells. Mechanistically, OGT-catalyzed O-GlcNAc modification of c-Myc at serine 415 (S415) increases c-Myc stability, which transcriptionally upregulates the expression of pyruvate dehydrogenase kinase 2 (PDK2). PDK2 phosphorylates pyruvate dehydrogenase (PDH) to inhibit the activity of mitochondrial pyruvate dehydrogenase complex, which reduces mitochondrial pyruvate metabolism, suppresses reactive oxygen species production, and promotes xenograft tumor growth. Furthermore, c-Myc S415 glycosylation levels positively correlate with PDK2 expression levels in clinical colorectal tumor tissues. This study highlights the OGT-c-Myc-PDK2 axis as a key mechanism linking oncoprotein activation with deregulated glucose metabolism in colorectal cancer.

DOI: https://doi.org/10.1038/s41418-024-01315-4
Expert Opin Drug Discov. 2024 May 24. 1-11

Data processing for high-throughput mass spectrometry in drug discovery.

Chang Liu, Hui Zhang.

   INTRODUCTION: High-throughput mass spectrometry that could deliver > 10 times faster sample readout speed than traditional LC-based platforms has emerged as a powerful analytical technique, enabling the rapid analysis of complex biological samples. This increased speed of MS data acquisition has brought a critical demand for automatic data processing capabilities that should match or surpass the speed of data acquisition. Those data processing capabilities should serve the different requirements of drug discovery workflows.
AREAS COVERED: This paper introduced the key steps of the automatic data processing workflows for high-throughput MS technologies. Specific examples and requirements are detailed for different drug discovery applications.
EXPERT OPINION: The demand for automatic data processing in high-throughput mass spectrometry is driven by the need to keep pace with the accelerated speed of data acquisition. The seamless integration of processing capabilities with LIMS, efficient data review mechanisms, and the exploration of future features such as real-time feedback, automatic method optimization, and AI model training is crucial for advancing the drug discovery field. As technology continues to evolve, the synergy between high-throughput mass spectrometry and intelligent data processing will undoubtedly play a pivotal role in shaping the future of high-throughput drug discovery applications.

Keywords:  High-throughput analysis; data processing; data review and visualization; drug discovery; mass spectrometry

DOI:  https://doi.org/10.1080/17460441.2024.2354871
Anal Chem. 2024 May 24.

Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules.

Sara M de Cripan, Trisha Arora, Adrià Olomí, Núria Canela, Gary Siuzdak, Xavier Domingo-Almenara.

The application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-section (CCS) prediction, but those have been trained with different and relatively small data sets covering a few thousands of small molecules, which hampers their systematic comparison. Here, we compared four existing ML-based CCS prediction models and their capacity to predict CCS values using the recently introduced METLIN-CCS data set. We also compared them with simple linear models and with ML models that used fingerprints as regressors. We analyzed the role of structural diversity of the data on which the ML models are trained with and explored the practical application of these models for metabolite annotation using CCS values. Results showed a limited capability of the existing models to achieve the necessary accuracy to be adopted for routine metabolomics analysis. We showed that for a particular molecule, this accuracy could only be improved when models were trained with a large number of structurally similar counterparts. Therefore, we suggest that current annotation capabilities will only be significantly altered with models trained with heterogeneous data sets composed of large homogeneous hubs of structurally similar molecules to those being predicted.

DOI: https://doi.org/10.1021/acs.analchem.4c00630
Int J Mol Sci. 2024 May 07. pii: 5098. [Epub ahead of print]25(10):

Assessment of Untargeted Metabolomics by Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry to Define Breast Cancer Liquid Biopsy-Based Biomarkers in Plasma Samples.

Carmen González Olmedo, Leticia Díaz Beltrán, Verónica Madrid García, José Luis Palacios Ferrer, Alicia Cano Jiménez, Rocío Urbano Cubero, José Pérez Del Palacio, Caridad Díaz, Francisca Vicente, Pedro Sánchez Rovira.

  An early diagnosis of cancer is fundamental not only in regard to reducing its mortality rate but also in terms of counteracting the progression of the tumor in the initial stages. Breast cancer (BC) is the most common tumor pathology in women and the second deathliest cancer worldwide, although its survival rate is increasing thanks to improvements in screening programs. However, the most common techniques to detect a breast tumor tend to be time-consuming, unspecific or invasive. Herein, the use of untargeted hydrophilic interaction liquid chromatography-mass spectrometry analysis appears as an analytical technique with potential use for the early detection of biomarkers in liquid biopsies from BC patients. In this research, plasma samples from 134 BC patients were compared with 136 from healthy controls (HC), and multivariate statistical analyses showed a clear separation between four BC phenotypes (LA, LB, HER2, and TN) and the HC group. As a result, we identified two candidate biomarkers that discriminated between the groups under study with a VIP > 1 and an AUC of 0.958. Thus, targeting the specific aberrant metabolic pathways in future studies may allow for better molecular stratification or early detection of the disease.

Keywords:  breast cancer; early diagnosis; hydrophilic interaction liquid chromatography; liquid biopsy; mass spectrometry; metabolomics

DOI:  https://doi.org/10.3390/ijms25105098
Nat Commun. 2024 May 18. 15(1): 4244

Mitochondrial respiratory function is preserved under cysteine starvation via glutathione catabolism in NSCLC.

Nathan P Ward, Sang Jun Yoon, Tyce Flynn, Amanda M Sherwood, Maddison A Olley, Juliana Madej, Gina M DeNicola.

Cysteine metabolism occurs across cellular compartments to support diverse biological functions and prevent the induction of ferroptosis. Though the disruption of cytosolic cysteine metabolism is implicated in this form of cell death, it is unknown whether the substantial cysteine metabolism resident within the mitochondria is similarly pertinent to ferroptosis. Here, we show that despite the rapid depletion of intracellular cysteine upon loss of extracellular cystine, cysteine-dependent synthesis of Fe-S clusters persists in the mitochondria of lung cancer cells. This promotes a retention of respiratory function and a maintenance of the mitochondrial redox state. Under these limiting conditions, we find that glutathione catabolism by CHAC1 supports the mitochondrial cysteine pool to sustain the function of the Fe-S proteins critical to oxidative metabolism. We find that disrupting Fe-S cluster synthesis under cysteine restriction protects against the induction of ferroptosis, suggesting that the preservation of mitochondrial function is antagonistic to survival under starved conditions. Overall, our findings implicate mitochondrial cysteine metabolism in the induction of ferroptosis and reveal a mechanism of mitochondrial resilience in response to nutrient stress.

DOI: https://doi.org/10.1038/s41467-024-48695-2
Metabolites. 2024 Apr 25. pii: 248. [Epub ahead of print]14(5):

The Metabolic and Lipidomic Fingerprint of Torin1 Exposure in Mouse Embryonic Fibroblasts Using Untargeted Metabolomics.

Rani Robeyns, Angela Sisto, Elias Iturrospe, Katyeny Manuela da Silva, Maria van de Lavoir, Vincent Timmerman, Adrian Covaci, Sigrid Stroobants, Alexander L N van Nuijs.

  Torin1, a selective kinase inhibitor targeting the mammalian target of rapamycin (mTOR), remains widely used in autophagy research due to its potent autophagy-inducing abilities, regardless of its unspecific properties. Recognizing the impact of mTOR inhibition on metabolism, our objective was to develop a reliable and thorough untargeted metabolomics workflow to study torin1-induced metabolic changes in mouse embryonic fibroblast (MEF) cells. Crucially, our quality assurance and quality control (QA/QC) protocols were designed to increase confidence in the reported findings by reducing the likelihood of false positives, including a validation experiment replicating all experimental steps from sample preparation to data analysis. This study investigated the metabolic fingerprint of torin1 exposure by using liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics platforms. Our workflow identified 67 altered metabolites after torin1 exposure, combining univariate and multivariate statistics and the implementation of a validation experiment. In particular, intracellular ceramides, diglycerides, phosphatidylcholines, phosphatidylethanolamines, glutathione, and 5'-methylthioadenosine were downregulated. Lyso-phosphatidylcholines, lyso-phosphatidylethanolamines, glycerophosphocholine, triglycerides, inosine, and hypoxanthine were upregulated. Further biochemical pathway analyses provided deeper insights into the reported changes. Ultimately, our study provides a valuable workflow that can be implemented for future investigations into the effects of other compounds, including more specific autophagy modulators.

Keywords:  autophagy; high-resolution mass spectrometry; lipids; mTOR; metabolism

DOI:  https://doi.org/10.3390/metabo14050248