bims-mascan 2024-01-21 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2024–01–21
fifteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics.
Unraveling the potential of segment scan mass spectral acquisition for chemical isotope labeling LC-MS-based metabolome analysis: Performance assessment across different types of biological samples.
IDSL_MINT: a deep learning framework to predict molecular fingerprints from mass spectra.
Triplex glycan quantification by metabolic labeling with isotopically labeled glucose in yeast.
timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery.
Untargeted metabolomics of bladder tissue using liquid chromatography and quadrupole time-of-flight mass spectrometry for cancer biomarker detection.
HILIC/MS quantitation of low-abundant phospholipids and sphingolipids in human plasma and serum: Dysregulation in pancreatic cancer.
iSanXoT: A standalone application for the integrative analysis of mass spectrometry-based quantitative proteomics data.
Gas chromatography-mass spectrometry-based quantitative method using tert-butyldimethylsilyl derivatization for plasma levels of free amino acids and related metabolites in Japanese Black cattle.
Distinct plasma molecular profiles between early-onset and late-onset colorectal cancer patients revealed by metabolic and lipidomic analyses.
Synthesis and quantification of short-chain fatty acid esters of hydroxy fatty acids in rat intestinal contents and fecal samples by LC-MS/MS.
Magnetic Bead-Based Workflow for Sensitive and Streamlined Cell Surface Proteomics.
Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking.
The extracellular matrix supports breast cancer cell growth under amino acid starvation by promoting tyrosine catabolism.
Rapid and Bifunctional Chemoselective Metabolome Analysis of Liver Patient Samples Using the Reagent 4-Nitrophenyl-2H-azirine.

Anal Chem. 2024 Jan 18.

MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics.

Laura Goracci, Paolo Tiberi, Stefano Di Bona, Stefano Bonciarelli, Giovanna Ilaria Passeri, Marta Piroddi, Simone Moretti, Claudia Volpi, Ismael Zamora, Gabriele Cruciani.

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.

DOI: https://doi.org/10.1021/acs.analchem.3c03620
Anal Chim Acta. 2024 Feb 01. pii: S0003-2670(23)01358-2. [Epub ahead of print]1288 342137

Unraveling the potential of segment scan mass spectral acquisition for chemical isotope labeling LC-MS-based metabolome analysis: Performance assessment across different types of biological samples.

Chu-Fan Wang, Liang Li.

   BACKGROUND: Chemical isotope labeling (CIL) LC-MS is a powerful tool for metabolome analysis with high metabolomic coverage and quantification accuracy. In CIL LC-MS, the overall metabolite detection efficiency using Orbitrap MS can be further improved by employing a segment scan method where the full m/z range is divided into multiple segments for spectral acquisition with a significant increase in the in-spectrum dynamic range. Considering the metabolic complexity in different types of biological samples (e.g., feces, urine, serum/plasma, cell/tissue extracts, saliva, etc.), we report the development and evaluation of the segment scan method for metabolome analysis of different sample types.
RESULTS: It was found that sample complexity significantly influenced the performance of the segment scan method. In metabolically complex samples such as feces and urine, the method yielded a substantial increase (up to 94 %) in detected peak pairs or metabolites, compared to conventional full scan. Conversely, less complex samples like saliva exhibited more modest gains (approximately 25 %). Based on the observations, a 120-m/z segment scan method was determined as a routine approach for CIL LC-Orbitrap-MS-based metabolomics with good compatibility with different types of biological samples. For this method, a further investigation on relative quantification accuracy was done. The peak area ratios of 12C-/13-labeled metabolites were slightly reduced with 72%-84 % of peak pairs falling within the ±25 % range of the anticipated peak ratio of 1.0 among different samples, as opposed to 81%-90 % in the full scan, which was attributed to the inclusion of more low-abundance peak pairs within the narrow MS segments. However, the overall peak ratio measurement precision was not significantly affected by the segment scan.
SIGNIFICANCE AND NOVELTY: The segment scan method was found to be useful for CIL LC-Orbitrap-MS-based metabolome analysis of different types of samples with significant improvement in metabolite detectability (25-94 % increase), compared to the conventional full scan method.

Keywords:  Chemical isotope labeling; Metabolic complexity; Metabolomics; Orbitrap MS; Segment scan

DOI:  https://doi.org/10.1016/j.aca.2023.342137
J Cheminform. 2024 Jan 18. 16(1): 8

IDSL_MINT: a deep learning framework to predict molecular fingerprints from mass spectra.

Sadjad Fakouri Baygi, Dinesh Kumar Barupal.

  The majority of tandem mass spectrometry (MS/MS) spectra in untargeted metabolomics and exposomics studies lack any annotation. Our deep learning framework, Integrated Data Science Laboratory for Metabolomics and Exposomics-Mass INTerpreter (IDSL_MINT) can translate MS/MS spectra into molecular fingerprint descriptors. IDSL_MINT allows users to leverage the power of the transformer model for mass spectrometry data, similar to the large language models. Models are trained on user-provided reference MS/MS libraries via any customizable molecular fingerprint descriptors. IDSL_MINT was benchmarked using the LipidMaps database and improved the annotation rate of a test study for MS/MS spectra that were not originally annotated using existing mass spectral libraries. IDSL_MINT may improve the overall annotation rates in untargeted metabolomics and exposomics studies. The IDSL_MINT framework and tutorials are available in the GitHub repository at https://github.com/idslme/IDSL_MINT .Scientific contribution statement.Structural annotation of MS/MS spectra from untargeted metabolomics and exposomics datasets is a major bottleneck in gaining new biological insights. Machine learning models to convert spectra into molecular fingerprints can help in the annotation process. Here, we present IDSL_MINT, a new, easy-to-use and customizable deep-learning framework to train and utilize new models to predict molecular fingerprints from spectra for the compound annotation workflows.

Keywords:  Deep learning; LipidMaps; Lipidomics; Mass spectrometry; Metabolomics; Molecular fingerprint descriptor; PyTorch; Transformer

DOI:  https://doi.org/10.1186/s13321-024-00804-5
Anal Chim Acta. 2024 Feb 01. pii: S0003-2670(23)01335-1. [Epub ahead of print]1288 342114

Triplex glycan quantification by metabolic labeling with isotopically labeled glucose in yeast.

Thao Thi Pham, Ji-Yeon Kim, Sami T Tuomivaara, Yong-Ill Lee, Seonghun Kim, Lance Wells, Jae-Min Lim.

  Mass spectrometry-based approaches encompass a powerful collection of tools for the analysis biological molecules, including glycans and glycoconjugates. Unlike most traditional bioanalytical methods focusing on these molecules, mass spectrometry is especially suited for multiplexing, by utilizing stable-isotope labeling. Indeed, stable isotope-based multiplexing can be regarded as the gold-standard approach in reducing noise and uncertainty in quantitative mass spectrometry and quantitative analyses generally. The increasing sophistication and depth of biological questions being asked continue to challenge the practitioners of mass spectrometry method development. To understand the biological relevance of glycans, many stable isotope labeling-based mass spectrometry methods have been developed. Based on the duplex MILPIG (metabolic isotope labeling of polysaccharides with isotopic glucose), we establish here a novel triplex isotope labeling method using baker's yeast as the model system. Two differentially isotope-labeled glucoses (medium: 1-13C1 and heavy: 1,2-13C2), in addition to natural abundance glucose (light), were successfully used to label each monosaccharide ring in N-linked glycans in three different cell culture conditions, that, after sample mixing, resulted in a predictable triplet spectrum amenable for relative quantitation. We demonstrate excellent accuracy and precision of relative quantitation for a 1:1:1 mixture of glycans labeled in such a fashion. In addition, we applied triplex MILPIG to interrogate differential N-glycan profiles in tunicamycin-treated and control yeast cells and show that different N-glycans respond differently to tunicamycin.

Keywords:  Glycans; Glycomics; MILPIG; Mass spectrometry; Triplex quantification

DOI:  https://doi.org/10.1016/j.aca.2023.342114
J Proteome Res. 2024 Jan 15.

timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery.

Dijana Vitko, Wan-Fang Chou, Sara Nouri Golmaei, Joon-Yong Lee, Chinmay Belthangady, John Blume, Jessica K Chan, Guillermo Flores-Campuzano, Yuntao Hu, Manway Liu, Mark A Marispini, Megan G Mora, Saividya Ramaswamy, Purva Ranjan, Preston B Williams, Robert J X Zawada, Philip Ma, Bruce E Wilcox.

  Mass spectrometry (MS) is a valuable tool for plasma proteome profiling and disease biomarker discovery. However, wide-ranging plasma protein concentrations, along with technical and biological variabilities, present significant challenges for deep and reproducible protein quantitation. Here, we evaluated the qualitative and quantitative performance of timsTOF HT and timsTOF Pro 2 mass spectrometers for analysis of neat (unfractionated) and Proteograph-processed plasma across a wide range of peptide loading masses and liquid chromatography (LC) gradients. We observed up to a 76% increase in total plasma peptide precursors identified and a >2-fold boost in quantifiable plasma peptide precursors (CV < 20%) with timsTOF HT compared to Pro 2. In an exploratory analysis of 20 late-stage lung cancer and 20 control plasma samples, which were expected to exhibit distinct proteomes, an approximate 50% increase in total and statistically significant plasma peptide precursors (q < 0.05) was observed with timsTOF HT compared to Pro 2. Our data demonstrate the superior performance of timsTOF HT for identifying and quantifying differences between biologically diverse samples, allowing for improved disease biomarker discovery in large cohort studies. Moreover, researchers can leverage data sets from this study to optimize their liquid chromatography-mass spectrometry (LC-MS) workflows for plasma protein profiling and biomarker discovery. (ProteomeXchange identifier: PXD047854 and PXD047839).

Keywords:  biomarkers; liquid chromatography; mass spectrometry; plasma; proteomics; timsTOF

DOI:  https://doi.org/10.1021/acs.jproteome.3c00646
J Pharm Biomed Anal. 2024 Jan 08. pii: S0731-7085(24)00006-2. [Epub ahead of print]240 115966

Untargeted metabolomics of bladder tissue using liquid chromatography and quadrupole time-of-flight mass spectrometry for cancer biomarker detection.

Joanna Nizioł, Krzysztof Ossoliński, Aneta Płaza-Altamer, Artur Kołodziej, Anna Ossolińska, Tadeusz Ossoliński, Zuzanna Krupa, Tomasz Ruman.

  Bladder cancer (BC) ranks among the most common cancers globally, with an increasing occurrence, particularly in developed nations. Utilizing tissue metabolomics presents a promising strategy for identifying potential biomarkers for cancer detection. In this study, we utilized ultra-high-performance liquid chromatography coupled with ultra-high-resolution mass spectrometry (UHPLC-UHRMS), incorporating both C18-silica and HILIC columns, to comprehensively analyze both polar and non-polar metabolite profiles in tissue samples from 99 patients with bladder cancer. By utilizing an untargeted approach with external validation, we identified twenty-five tissue metabolites that hold promise as potential indicators of BC. Furthermore, twenty-five characteristic tissue metabolites that exhibit discriminatory potential across bladder cancer tumor grades, as well as thirty-nine metabolites that display correlations with tumor stages were presented. Receiver operating characteristics analysis demonstrated high predictive power for all types of metabolomics data, with area under the curve (AUC) values exceeding 0.966. Notably, this study represents the first report in which human bladder normal tissues adjacent to cancerous tissues were analyzed using UHPLC-UHRMS. These findings suggest that the metabolite markers identified in this investigation could serve as valuable tools for the detection and monitoring of bladder cancer stages and grades.

Keywords:  Biomarkers; Bladder cancer; Human tissue; LC-MS; Metabolomics; UHPLC-UHRMS

DOI:  https://doi.org/10.1016/j.jpba.2024.115966
Anal Chim Acta. 2024 Feb 01. pii: S0003-2670(23)01365-X. [Epub ahead of print]1288 342144

HILIC/MS quantitation of low-abundant phospholipids and sphingolipids in human plasma and serum: Dysregulation in pancreatic cancer.

Ondřej Peterka, Alessandro Maccelli, Robert Jirásko, Zuzana Vaňková, Jakub Idkowiak, Roman Hrstka, Denise Wolrab, Michal Holčapek.

  A new hydrophilic interaction liquid chromatography - mass spectrometry method is developed for low-abundant phospholipids and sphingolipids in human plasma and serum. The optimized method involves the Cogent Silica type C hydride column, the simple sample preparation by protein precipitation, and the removal of highly abundant lipid classes using the postcolumn valve directed to waste during two elution windows. The method allows a highly confident and sensitive identification of low-abundant lipid classes in human plasma (246 lipid species from 24 lipid subclasses) based on mass accuracy and retention dependencies in both polarity modes. The method is validated for quantitation using two internal standards (if available) for each lipid class and applied to human plasma and serum samples obtained from patients with pancreatic ductal adenocarcinoma (PDAC), healthy controls, and NIST SRM 1950. Multivariate data analysis followed by various statistical projection methods is used to determine the most dysregulated lipids. Significant downregulation is observed for lysophospholipids with fatty acyl composition 16:0, 18:0, 18:1, and 18:2. Distinct trends are observed for phosphatidylethanolamines (PE) in relation to the bonding type of fatty acyls, where most PE with acyl bonds are upregulated, while ether/plasmenyl PE are downregulated. For the sphingolipid category, sphingolipids with very long N-acyl chains are downregulated, while sphingolipids with shorter N-acyl chains were upregulated in PDAC. These changes are consistently observed for various classes of sphingolipids, ranging from ceramides to glycosphingolipids, indicating a possible metabolic disorder in ceramide biosynthesis caused by PDAC.

Keywords:  Human plasma; Human serum; Hydrophilic interaction liquid chromatography; Lipidomics; Liquid chromatography; Mass spectrometry; Pancreatic cancer

DOI:  https://doi.org/10.1016/j.aca.2023.342144
Comput Struct Biotechnol J. 2024 Dec;23 452-459

iSanXoT: A standalone application for the integrative analysis of mass spectrometry-based quantitative proteomics data.

Jose Manuel Rodríguez, Inmaculada Jorge, Ana Martinez-Val, Rafael Barrero-Rodríguez, Ricardo Magni, Estefanía Núñez, Andrea Laguillo, Cristina A Devesa, Juan A López, Emilio Camafeita, Jesús Vázquez.

  Many bioinformatics tools are available for the quantitative analysis of proteomics experiments. Most of these tools use a dedicated statistical model to derive absolute quantitative protein values from mass spectrometry (MS) data. Here, we present iSanXoT, a standalone application that processes relative abundances between MS signals and then integrates them sequentially to upper levels using the previously published Generic Integration Algorithm (GIA). iSanXoT offers unique capabilities that complement conventional quantitative software applications, including statistical weighting and independent modeling of error distributions in each integration, aggregation of technical or biological replicates, quantification of posttranslational modifications, and analysis of coordinated protein behavior. iSanXoT is a standalone, user-friendly application that accepts output from popular proteomics pipelines and enables unrestricted creation of quantification workflows and fully customizable reports that can be reused across projects or shared among users. Numerous publications attest the successful application of diverse integrative workflows constructed using the GIA for the analysis of high-throughput quantitative proteomics experiments. iSanXoT has been tested with the main operating systems. Download links for the corresponding distributions are available at https://github.com/CNIC-Proteomics/iSanXoT/releases.

Keywords:  Generic integration algorithm; Mass spectrometry; Protein coordination; Proteomics pipeline; Quantitative proteomics; WSPP model

DOI:  https://doi.org/10.1016/j.csbj.2023.12.034
Anim Sci J. 2024 Jan-Dec;95(1):95(1): e13896

Gas chromatography-mass spectrometry-based quantitative method using tert-butyldimethylsilyl derivatization for plasma levels of free amino acids and related metabolites in Japanese Black cattle.

Hanwool Do, Emi Yoshida, Tatsunori Masaki, Shigeru Oosaki, Mitsuhiro Sakase, Eiji Iwamoto, Shozo Tomonaga.

  The quantification of amino acid and related metabolite levels is important for evaluating amino acid metabolism and function in animals. However, a useful quantitative method is not enough. In this study, we developed and validated tert-butyldimethylsilyl derivatization method using gas chromatography-mass spectrometry to quantify plasma levels of free amino acids and related metabolites in Japanese Black cattle. Of the 51 metabolites examined, 24, including 20 amino acids, one amine, and three keto acids, could be quantified. Compared with the trimethylsilyl derivatization method using gas chromatography-mass spectrometry, which has been used for untargeted metabolomic analysis, the present method had higher analytical reliability. This method is advantageous for assessing branched-chain amino acid (BCAA) metabolism because it enables the quantification of not only BCAA levels (valine, leucine, and isoleucine) but also their bioactive metabolite keto acid levels (2-ketoisovaleric acid, 2-ketoisocaproic acid, and 2-keto-3-methylvaleric acid) in the plasma. In addition, this method can quantify the plasma levels of not only tryptophan but also its bioactive metabolites kynurenine and serotonin. These results suggest that this quantitative method has the potential to further our understanding of amino acid metabolic processes and their functions in Japanese Black cattle.

Keywords:  Japanese Black cattle; amino acid metabolism; plasma; quantitative analysis

DOI:  https://doi.org/10.1111/asj.13896
J Pharm Biomed Anal. 2024 Jan 12. pii: S0731-7085(24)00018-9. [Epub ahead of print]241 115978

Distinct plasma molecular profiles between early-onset and late-onset colorectal cancer patients revealed by metabolic and lipidomic analyses.

Shiqi Zhang, Rui Xu, Ming Hu, Fouad Choueiry, Ning Jin, Jieli Li, Xiaokui Mo, Jiangjiang Zhu.

  Colorectal cancer (CRC) incidence in younger adults has been steadily rising, warranting an in-depth investigation into the distinctions between early-onset CRC (EOCRC, < 50 years) and late-onset CRC (LOCRC, ≥ 50 years). Despite extensive study of clinical, pathological, and molecular traits, differentiating EOCRC from LOCRC and identifying potential biomarkers remain elusive. We analyzed plasma samples from healthy individuals, EOCRC, and LOCRC patients using liquid-chromatography mass spectrometry (LC/MS)-based metabolomics and lipidomics. Distinct polar metabolite and lipid profiles with significant metabolites altered in CRC group (e.g., choline and DG 40:4) were identified. Notably, EOCRC exhibited distinct polar metabolomic and differential lipidomic profiles compared to LOCRC, with polar metabolites like aminoadipate and uridine contributing significantly to the difference, and originating from pathways such as lysine biosynthesis and nucleotide metabolism. Furthermore, gene set enrichment analysis (GSEA) using independent TCGA gene expression data identified pathways significantly enriched in either EOCRC or LOCRC. Integrating gene expression and metabolomics data revealed numerous associations differentiating EOCRC and LOCRC. Our multi-omics integration underscores critical molecular distinctions, offers insights into the EOCRC development mechanisms and potential plasma biomarkers for diagnosis.

Keywords:  Colorectal cancer; Early-onset; Late-onset; Mass spectrometry; Metabolomics

DOI:  https://doi.org/10.1016/j.jpba.2024.115978
Anal Chim Acta. 2024 Feb 01. pii: S0003-2670(23)01366-1. [Epub ahead of print]1288 342145

Synthesis and quantification of short-chain fatty acid esters of hydroxy fatty acids in rat intestinal contents and fecal samples by LC-MS/MS.

Siddabasave Gowda B Gowda, Fengjue Hou, Divyavani Gowda, Hitoshi Chiba, Kentaro Kawakami, Satoru Fukiya, Atsushi Yokota, Shu-Ping Hui.

  Short-chain fatty acid esters of hydroxy fatty acids (SFAHFAs) are a new class of endogenous lipids belonging to the fatty acid esters of the hydroxy fatty acid family. We previously uncovered their chemical structure and discussed their potential biological significance. We anticipate an increased need for SFAHFA measurements as markers of metabolic and inflammatory health. In this study, we synthesized sixty isomeric SFAHFAs by combining 12 hydroxy fatty acids (C16-C24) and five short-chain fatty acids (C2-C6) including a labelled internal standard. SFAHFA enrichment was achieved by solid-phase extraction and established a sensitive method for their quantitation by targeted LC-MS/MS. The method was applied to profile SFAHFAs in intestinal contents and fecal samples collected from rats fed a high-fat diet (HFD). The results demonstrated a significant decrease in SFAHFAs in the intestinal contents of the HFD group compared with the control group. The fecal time course (0-8 weeks) profile of SFAHFAs showed significant downregulation of acetic and propanoic acid esters in just 2 weeks after HFD administration. This study offers the first synthesis and quantitation method for SFAHFAs, demonstrating their potential use in elucidating SFAHFA sources, their role in various diseases, and potential biochemical signalling pathways.

Keywords:  Chemical synthesis; Gut microbial lipids; Intestinal contents; Liquid chromatography; Mass spectrometry; SFAHFAs

DOI:  https://doi.org/10.1016/j.aca.2023.342145
J Proteome Res. 2024 Jan 16.

Magnetic Bead-Based Workflow for Sensitive and Streamlined Cell Surface Proteomics.

Dylan Z Dieters-Castator, Paolo Manzanillo, Han-Yin Yang, Rucha V Modak, Matthew J Rardin, Bradford W Gibson.

  Cell surface proteins represent an important class of molecules for therapeutic targeting and cellular phenotyping. However, their enrichment and detection via mass spectrometry-based proteomics remains challenging due to low abundance, post-translational modifications, hydrophobic regions, and processing requirements. To improve their identification, we optimized a Cell-Surface Capture (CSC) workflow that incorporates magnetic bead-based processing. Using this approach, we evaluated labeling conditions (biotin tags and catalysts), enrichment specificity (streptavidin beads), missed cleavages (lysis buffers), nonenzymatic deamidation (digestion and deglycosylation buffers), and data acquisition methods (DDA, DIA, and TMT). Our findings support the use of alkoxyamine-PEG4-biotin plus 5-methoxy-anthranilic acid, SDS/urea-based lysis buffers, single-pot solid-phased-enhanced sample-preparation (SP3), and streptavidin magnetic beads for maximal surfaceome coverage. Notably, with semiautomated processing, sample handling was simplified and between ∼600 and 900 cell surface N-glycoproteins were identified from only 25-200 μg of HeLa protein. CSC also revealed significant differences between in vitro monolayer cultures and in vivo tumor xenografts of murine CT26 colon adenocarcinoma samples that may aid in target identification for drug development. Overall, the improved efficiency of the magnetic-based CSC workflow identified both previously reported and novel N-glycosites with less material and high reproducibility that should help advance the field of surfaceomics by providing insight in cellular phenotypes not previously documented.

Keywords:  N-glycopeptide enrichment; cell surface capture; mass spectrometry; plasma membrane N-glycoprotein; surfaceomics

DOI:  https://doi.org/10.1021/acs.jproteome.3c00432
Anal Chem. 2024 Jan 19.

Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking.

Xinxin Wang, Xiaoshan Sun, Fubo Wang, Chunmeng Wei, Fujian Zheng, Xiuqiong Zhang, Xinjie Zhao, Chunxia Zhao, Xin Lu, Guowang Xu.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases. Molecular networking (MN) shows great promise in large-scale metabolome annotation, but enhancing the correlation between spectral and structural similarity is essential to fully exploring the benefits of MN annotation. In this study, a novel approach was proposed to enhance metabolite annotation in untargeted metabolomics using EIEIO and MN. MS/MS spectra were acquired in EIEIO and collision-induced dissociation (CID) modes for over 400 reference metabolites. The study revealed a stronger correlation between the EIEIO spectra and metabolite structure. Moreover, the EIEIO spectral network outperformed the CID spectral network in capturing structural analogues. The annotation performance of the structural similarity network for untargeted LC-MS/MS was evaluated. For the spiked NIST SRM 1950 human plasma, the annotation coverage and accuracy were 72.94 and 74.19%, respectively. A total of 2337 metabolite features were successfully annotated in NIST SRM 1950 human plasma, which was twice that of LC-CID MS/MS. Finally, the developed method was applied to investigate prostate cancer. A total of 87 significantly differential metabolites were annotated. This study combining EIEIO and MN makes a valuable contribution to improving metabolome annotation.

DOI: https://doi.org/10.1021/acs.analchem.3c03443
PLoS Biol. 2024 Jan;22(1): e3002406

The extracellular matrix supports breast cancer cell growth under amino acid starvation by promoting tyrosine catabolism.

Mona Nazemi, Bian Yanes, Montserrat Llanses Martinez, Heather J Walker, Khoa Pham, Mark O Collins, Frederic Bard, Elena Rainero.

Breast tumours are embedded in a collagen I-rich extracellular matrix (ECM) network, where nutrients are scarce due to limited blood flow and elevated tumour growth. Metabolic adaptation is required for cancer cells to endure these conditions. Here, we demonstrated that the presence of ECM supported the growth of invasive breast cancer cells, but not non-transformed mammary epithelial cells, under amino acid starvation, through a mechanism that required macropinocytosis-dependent ECM uptake. Importantly, we showed that this behaviour was acquired during carcinoma progression. ECM internalisation, followed by lysosomal degradation, contributed to the up-regulation of the intracellular levels of several amino acids, most notably tyrosine and phenylalanine. This resulted in elevated tyrosine catabolism on ECM under starvation, leading to increased fumarate levels, potentially feeding into the tricarboxylic acid (TCA) cycle. Interestingly, this pathway was required for ECM-dependent cell growth and invasive cell migration under amino acid starvation, as the knockdown of p-hydroxyphenylpyruvate hydroxylase-like protein (HPDL), the third enzyme of the pathway, opposed cell growth and motility on ECM in both 2D and 3D systems, without affecting cell proliferation on plastic. Finally, high HPDL expression correlated with poor prognosis in breast cancer patients. Collectively, our results highlight that the ECM in the tumour microenvironment (TME) represents an alternative source of nutrients to support cancer cell growth by regulating phenylalanine and tyrosine metabolism.

DOI: https://doi.org/10.1371/journal.pbio.3002406
Angew Chem Int Ed Engl. 2024 Jan 18. e202318579

Rapid and Bifunctional Chemoselective Metabolome Analysis of Liver Patient Samples Using the Reagent 4-Nitrophenyl-2H-azirine.

Weifeng Lin, Lars Gerullat, Peder R Braadland, Anaïs Fournier, Johannes R Hov, Daniel Globisch.

  Primary sclerosing cholangitis (PSC) is a chronic inflammatory disease of the bile ducts that has been associated with diverse metabolic carboxylic acids. Mass spectrometric techniques are the method of choice for their analysis. However, the broad investigation of this metabolite class remains challenging. Derivatization of carboxylic acids represents a strategy to overcome these limitations but available methods suffer from diverse analytical challenges. Herein, we have designed a novel strategy introducing 4-nitrophenyl-2H-azirine as a new chemoselective moiety for the first time for carboxylic acid metabolites. This moiety was selected as it rapidly forms a stable amide bond and also generates a new ketone, which can be analyzed by our recently developed quant-SCHEMA method specific for carbonyl metabolites. Optimization of this new method revealed a high reproducibility and robustness, which was utilized to validate 102 metabolic carboxylic acids using authentic synthetic standard conjugates in human plasma samples including nine metabolites that were newly detected. Using this sequential analysis of the carbonyl- and carboxylic acid-metabolomes revealed alterations of the ketogenesis pathway, which demonstrates the vast benefit of our unique methodology. We anticipate that the developed azirine moiety with rapid functional group transformation will find broad application in diverse chemical biology research fields.

Keywords:  2H-Azirine; Bioorganic chemistry; Chemical Biology; Chemical metabolomics; Metabolic carboxylic acids

DOI:  https://doi.org/10.1002/anie.202318579