bims-mascan 2023-07-30 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2023–07–30
nineteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics.
A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry.
Metabolomic and Lipidomic Analysis of the Colorectal Adenocarcinoma Cell Line HT29 in Hypoxia and Reoxygenation.
PI3K signaling-regulated metabolic reprogramming: From mechanism to application.
Comparison of Compound Identification Tools Using Data Dependent and Data Independent High-Resolution Mass Spectrometry Spectra.
MSBooster: improving peptide identification rates using deep learning-based features.
Arginine dependency is a therapeutically exploitable vulnerability in chronic myeloid leukaemic stem cells.
A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation.
Radiation therapy promotes unsaturated fatty acids to maintain survival of glioblastoma.
Disturbed Plasma Lipidomic Profiles in Females with Diffuse Large B-Cell Lymphoma: A Pilot Study.
An Untargeted Metabolomics Approach to Study the Variation between Wild and Cultivated Soybeans.
The vitamin B5/coenzyme A axis: A target for immunomodulation?
Tissue-based absolute quantification using large-scale TMT and LFQ experiments.
Revealing the polar lipidome, pigment profiles, and antioxidant activity of the giant unicellular green alga, Acetabularia acetabulum.
ipaPy2: Integrated Probabilistic Annotation (IPA) 2.0-an improved Bayesian-based method for the annotation of LC-MS/MS untargeted metabolomics data.
A Cell Cycle-Dependent Ferroptosis Sensitivity Switch Governed by EMP2.
Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives.
Therapeutically targeting essential metabolites to improve immunometabolism manipulation after liver transplantation for hepatocellular carcinoma.
Differentiation in TCM patterns of chronic obstructive pulmonary disease by comprehensive metabolomic and lipidomic characterization.

Metabolites. 2023 Jul 13. pii: 844. [Epub ahead of print]13(7):

Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics.

Antonia Fecke, Nay Min Min Thaw Saw, Dipali Kale, Siva Swapna Kasarla, Albert Sickmann, Prasad Phapale.

  Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabolite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound's individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a "quantitative chromatogram library" with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications.

Keywords:  LC-MS; metabolite quantification; metabolomics; quantitative spectral library; relative response factor

DOI:  https://doi.org/10.3390/metabo13070844
Mol Cell Proteomics. 2023 Jul 20. pii: S1535-9476(23)00134-2. [Epub ahead of print] 100623

A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry.

Fangfei Zhang, Weigang Ge, Lingling Huang, Dan Li, Lijuan Liu, Zhen Dong, Luang Xu, Xuan Ding, Cheng Zhang, Yingying Sun, Jun A, Jinlong Gao, Tiannan Guo.

  Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-MS technology.

Keywords:  DIA-NN; EncyclopeDIA; OpenSWATH; Skyline; Spectronaut; data-independent acquisition; mass spectrometry; proteomics

DOI:  https://doi.org/10.1016/j.mcpro.2023.100623
Metabolites. 2023 Jul 23. pii: 875. [Epub ahead of print]13(7):

Metabolomic and Lipidomic Analysis of the Colorectal Adenocarcinoma Cell Line HT29 in Hypoxia and Reoxygenation.

Juan Carlos Alarcon Barrera, Alejandro Ondo-Mendez, Martin Giera, Sarantos Kostidis.

  The poor availability of oxygen and nutrients in malignant tumors drives the activation of various molecular responses and metabolic reprogramming in cancer cells. Hypoxic tumor regions often exhibit resistance to chemotherapy and radiotherapy. One approach to enhance cancer therapy is to indirectly increase tumor oxygen availability through targeted metabolic reprogramming. Thus, understanding the underlying metabolic changes occurring during hypoxia and reoxygenation is crucial for improving therapy efficacy. In this study, we utilized the HT29 colorectal adenocarcinoma cell line as a hypoxia-reoxygenation model to investigate central carbon and lipid metabolism. Through quantitative NMR spectroscopy and flow injection analysis - differential mobility spectroscopy-tandem mass spectrometry (FIA-DMS-MS/MS) analysis, we observed alterations in components of mitochondrial metabolism, redox status, specific lipid classes, and structural characteristics of lipids during hypoxia and up to 24 h of reoxygenation. These findings contribute to our understanding of the metabolic changes occurring during reoxygenation and provide the basis for functional studies aimed at metabolic pathways in cancer cells.

Keywords:  colorectal adenocarcinoma; hypoxia; lipidomics; metabolomics; reoxygenation

DOI:  https://doi.org/10.3390/metabo13070875
Biochim Biophys Acta Rev Cancer. 2023 Jul 24. pii: S0304-419X(23)00101-4. [Epub ahead of print] 188952

PI3K signaling-regulated metabolic reprogramming: From mechanism to application.

Wen Ya Su, Lu Yao Tian, Lan Pin Guo, Lu Qi Huang, Wen Yuan Gao.

  Oncogenic signaling involved in tumor metabolic reprogramming. Tumorigenesis was not only determined by the mutations or deletion of oncogenes but also accompanied by the reprogramming of cellular metabolism. Metabolic alterations play a crucial regulatory role in the development and progression of tumors. Oncogenic PI3K/AKT signaling mediates the metabolic switch in cancer cells and immune cells in the tumor microenvironment. PI3K/AKT and its downstream effector branch off and connect to multiple steps of metabolism, such as glucose, lipids, and amino acids. Thus, PI3K inhibitor could effectively regulate metabolic pathway and impede the oncogenic process and some key metabolic proteins or critical enzymes also constitute biomarkers for tumor diagnosis and treatment. In the current review, we summarize the significant effect of PI3K/AKT signaling toward tumor metabolism, it enables us to obtain the better understanding for this interaction and develop more effective therapeutic strategies targeting cancer cell metabolism.

Keywords:  Amino acid metabolism; Fatty acid metabolism; Glycolysis; Metabolic reprogramming; PI3K/AKT

DOI:  https://doi.org/10.1016/j.bbcan.2023.188952
Metabolites. 2023 Jun 21. pii: 777. [Epub ahead of print]13(7):

Comparison of Compound Identification Tools Using Data Dependent and Data Independent High-Resolution Mass Spectrometry Spectra.

Rosalie Nijssen, Marco H Blokland, Robin S Wegh, Erik de Lange, Stefan P J van Leeuwen, Bjorn J A Berendsen, Milou G M van de Schans.

  Liquid chromatography combined with high-resolution mass spectrometry (LC-HRMS) is a frequently applied technique for suspect screening (SS) and non-target screening (NTS) in metabolomics and environmental toxicology. However, correctly identifying compounds based on SS or NTS approaches remains challenging, especially when using data-independent acquisition (DIA). This study assessed the performance of four HRMS-spectra identification tools to annotate in-house generated data-dependent acquisition (DDA) and DIA HRMS spectra of 32 pesticides, veterinary drugs, and their metabolites. The identification tools were challenged with a diversity of compounds, including isomeric compounds. The identification power was evaluated in solvent standards and spiked feed extract. In DDA spectra, the mass spectral library mzCloud provided the highest success rate, with 84% and 88% of the compounds correctly identified in the top three in solvent standard and spiked feed extract, respectively. The in silico tools MSfinder, CFM-ID, and Chemdistiller also performed well in DDA data, with identification success rates above 75% for both solvent standard and spiked feed extract. MSfinder provided the highest identification success rates using DIA spectra with 72% and 75% (solvent standard and spiked feed extract, respectively), and CFM-ID performed almost similarly in solvent standard and slightly less in spiked feed extract (72% and 63%). The identification success rates for Chemdistiller (66% and 38%) and mzCloud (66% and 31%) were lower, especially in spiked feed extract. The difference in success rates between DDA and DIA is most likely caused by the higher complexity of the DIA spectra, making direct spectral matching more complex. However, this study demonstrates that DIA spectra can be used for compound annotation in certain software tools, although the success rate is lower than for DDA spectra.

Keywords:  annotation; data dependent acquisition; data independent acquisition; high-resolution mass spectrometry; identification; metabolites; pesticides; veterinary drugs

DOI:  https://doi.org/10.3390/metabo13070777
Nat Commun. 2023 Jul 27. 14(1): 4539

MSBooster: improving peptide identification rates using deep learning-based features.

Kevin L Yang, Fengchao Yu, Guo Ci Teo, Kai Li, Vadim Demichev, Markus Ralser, Alexey I Nesvizhskii.

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

DOI: https://doi.org/10.1038/s41467-023-40129-9
EMBO Rep. 2023 Jul 25. e56279

Arginine dependency is a therapeutically exploitable vulnerability in chronic myeloid leukaemic stem cells.

Kevin M Rattigan, Martha M Zarou, Zuzana Brabcova, Bodhayan Prasad, Désirée Zerbst, Daniele Sarnello, Eric R Kalkman, Angela Ianniciello, Mary T Scott, Karen Dunn, Engy Shokry, David Sumpton, Mhairi Copland, Saverio Tardito, Johan Vande Voorde, Francis Mussai, Paul Cheng, G Vignir Helgason.

  To fuel accelerated proliferation, leukaemic cells undergo metabolic deregulation, which can result in specific nutrient dependencies. Here, we perform an amino acid drop-out screen and apply pre-clinical models of chronic phase chronic myeloid leukaemia (CML) to identify arginine as a nutrient essential for primary human CML cells. Analysis of the Microarray Innovations in Leukaemia (MILE) dataset uncovers reduced ASS1 levels in CML compared to most other leukaemia types. Stable isotope tracing reveals repressed activity of all urea cycle enzymes in patient-derived CML CD34+ cells, rendering them arginine auxotrophic. Thus, arginine deprivation completely blocks proliferation of CML CD34+ cells and induces significantly higher levels of apoptosis when compared to arginine-deprived cell lines. Similarly, primary CML cells, but not normal CD34+ samples, are particularly sensitive to treatment with the arginine-depleting enzyme, BCT-100, which induces apoptosis and reduces clonogenicity. Moreover, BCT-100 is highly efficacious in a patient-derived xenograft model, causing > 90% reduction in the number of human leukaemic stem cells (LSCs). These findings indicate arginine depletion to be a promising and novel strategy to eradicate therapy resistant LSCs.

Keywords:  amino acids; leukaemic stem cells; metabolism; therapy resistance

DOI:  https://doi.org/10.15252/embr.202256279
Anal Chem. 2023 Jul 26.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation.

Xinxin Wang, Chao Li, Zaifang Li, Yanpeng Qi, Xiuqiong Zhang, Xinjie Zhao, Chunxia Zhao, Xiaohui Lin, Xin Lu, Guowang Xu.

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.

DOI: https://doi.org/10.1021/acs.analchem.3c00849
Cancer Lett. 2023 Jul 25. pii: S0304-3835(23)00280-X. [Epub ahead of print] 216329

Radiation therapy promotes unsaturated fatty acids to maintain survival of glioblastoma.

Mara De Martino, Camille Daviaud, Hanna E Minns, Artur Lazarian, Anja Wacker, Ana Paula Costa, Nabeel Attarwala, Qiuying Chen, Seung-Won Choi, Raùl Rabadàn, Laura Beth J McIntire, Robyn D Gartrell, James M Kelly, Evagelia C Laiakis, Claire Vanpouille-Box.

  Radiation therapy (RT) is essential for the management of glioblastoma (GBM). However, GBM frequently relapses within the irradiated margins, thus suggesting that RT might stimulate mechanisms of resistance that limits its efficacy. GBM is recognized for its metabolic plasticity, but whether RT-induced resistance relies on metabolic adaptation remains unclear. Here, we show in vitro and in vivo that irradiated GBM tumors switch their metabolic program to accumulate lipids, especially unsaturated fatty acids. This resulted in an increased formation of lipid droplets to prevent endoplasmic reticulum (ER) stress. The reduction of lipid accumulation with genetic suppression and pharmacological inhibition of the fatty acid synthase (FASN), one of the main lipogenic enzymes, leads to mitochondrial dysfunction and increased apoptosis of irradiated GBM cells. Combination of FASN inhibition with focal RT improved the median survival of GBM-bearing mice. Supporting the translational value of these findings, retrospective analysis of the GLASS consortium dataset of matched GBM patients revealed an enrichment in lipid metabolism signature in recurrent GBM compared to primary. Overall, these results demonstrate that RT drives GBM resistance by generating a lipogenic environment permissive to GBM survival. Targeting lipid metabolism might be required to develop more effective anti-GBM strategies.

Keywords:  Apoptosis; ER stress; Fatty acid synthase; Glioblastoma; Lipid droplets; Lipid metabolism; Mass spectrometry imaging; Prostaglandin E2; Radiation therapy; Survival; Unsaturated fatty acids

DOI:  https://doi.org/10.1016/j.canlet.2023.216329
Cancers (Basel). 2023 Jul 18. pii: 3653. [Epub ahead of print]15(14):

Disturbed Plasma Lipidomic Profiles in Females with Diffuse Large B-Cell Lymphoma: A Pilot Study.

Romana Masnikosa, David Pirić, Julia Maria Post, Zorica Cvetković, Snježana Petrović, Marija Paunović, Vesna Vučić, Laura Bindila.

  Lipidome dysregulation is a hallmark of cancer and inflammation. The global plasma lipidome and sub-lipidome of inflammatory pathways have not been reported in diffuse large B-cell lymphoma (DLBCL). In a pilot study of plasma lipid variation in female DLBCL patients and BMI-matched disease-free controls, we performed targeted lipidomics using LC-MRM to quantify lipid mediators of inflammation and immunity, and those known or hypothesised to be involved in cancer progression: sphingolipids, resolvin D1, arachidonic acid (AA)-derived oxylipins, such as hydroxyeicosatetraenoic acids (HETEs) and dihydroxyeicosatrienoic acids, along with their membrane structural precursors. We report on the role of the eicosanoids in the separation of DLBCL from controls, along with lysophosphatidylinositol LPI 20:4, implying notable changes in lipid metabolic and/or signalling pathways, particularly pertaining to AA lipoxygenase pathway and glycerophospholipid remodelling in the cell membrane. We suggest here the set of S1P, SM 36:1, SM 34:1 and PI 34:1 as DLBCL lipid signatures which could serve as a basis for the prospective validation in larger DLBCL cohorts. Additionally, untargeted lipidomics indicates a substantial change in the overall lipid metabolism in DLBCL. The plasma lipid profiling of DLBCL patients helps to better understand the specific lipid dysregulations and pathways in this cancer.

Keywords:  DLBCL; HETEs; eicosanoids; glycerophospholipids; plasma 4D lipidomics; sphingolipids; sphingosine 1-phosphate; targeted LC-MS; tims-Tof

DOI:  https://doi.org/10.3390/cancers15143653
Molecules. 2023 Jul 19. pii: 5507. [Epub ahead of print]28(14):

An Untargeted Metabolomics Approach to Study the Variation between Wild and Cultivated Soybeans.

Fakir Shahidullah Tareq, Raghavendhar R Kotha, Savithiry Natarajan, Jianghao Sun, Devanand L Luthria.

  The differential metabolite profiles of four wild and ten cultivated soybeans genotypes were explored using an untargeted metabolomics approach. Ground soybean seed samples were extracted with methanol and water, and metabolic features were obtained using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) in both positive and negative ion modes. The UHPLC-HRMS analysis of the two different extracts resulted in the putative identification of 98 metabolites belonging to several classes of phytochemicals, including isoflavones, organic acids, lipids, sugars, amino acids, saponins, and other compounds. The metabolic profile was significantly impacted by the polarity of the extraction solvent. Multivariate analysis showed a clear difference between wild and cultivated soybean cultivars. Unsupervised and supervised learning algorithms were applied to mine the generated data and to pinpoint metabolites differentiating wild and cultivated soybeans. The key identified metabolites differentiating wild and cultivated soybeans were isoflavonoids, free amino acids, and fatty acids. Catechin analogs, cynaroside, hydroxylated unsaturated fatty acid derivatives, amino acid, and uridine diphosphate-N-acetylglucosamine were upregulated in the methanol extract of wild soybeans. In contrast, isoflavonoids and other minor compounds were downregulated in the same soybean extract. This metabolic information will benefit breeders and biotechnology professionals to develop value-added soybeans with improved quality traits.

Keywords:  metabolomics; multivariate analysis; soybean cultivars; ultra-high-performance liquid chromatography-tandem mass spectrometry

DOI:  https://doi.org/10.3390/molecules28145507
Eur J Immunol. 2023 Jul 22. e2350435

The vitamin B5/coenzyme A axis: A target for immunomodulation?

Miallot Richard, Millet Virginie, Galland Franck, Naquet Philippe.

  Coenzyme A (CoA) serves as a vital cofactor in numerous enzymatic reactions involved in energy production, lipid metabolism, and synthesis of essential molecules. Dysregulation of CoA-dependent metabolic pathways can contribute to chronic diseases, such as inflammatory diseases, obesity, diabetes, cancer, and cardiovascular disorders. Additionally, CoA influences immune cell activation by modulating the metabolism of these cells, thereby affecting their proliferation, differentiation, and effector functions. Targeting CoA metabolism presents a promising avenue for therapeutic intervention, as it can potentially restore metabolic balance, mitigate chronic inflammation, and enhance immune cell function. This might ultimately improve the management and outcomes for these diseases. This review will more specifically focus on the contribution of pathways regulating the availability of the CoA precursor Vitamin B5/pantothenate in vivo and modulating the development of Th17-mediated inflammation, CD8-dependent anti-tumor immunity but also tissue repair processes in chronic inflammatory or degenerative diseases.

Keywords:  Coenzyme A; Inflammation; Tissue repair; Tumor immunity; Vitamin B5

DOI:  https://doi.org/10.1002/eji.202350435
Proteomics. 2023 Jul 24. e2300188

Tissue-based absolute quantification using large-scale TMT and LFQ experiments.

Hong Wang, Chengxin Dai, Julianus Pfeuffer, Timo Sachsenberg, Aniel Sanchez, Mingze Bai, Yasset Perez-Riverol.

  Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.

Keywords:  LFQ; TMT; absolute protein expression; big data; proteomics data reanalysis; public data

DOI:  https://doi.org/10.1002/pmic.202300188
J Phycol. 2023 Jul 23.

Revealing the polar lipidome, pigment profiles, and antioxidant activity of the giant unicellular green alga, Acetabularia acetabulum.

Felisa Rey, Paulo Cartaxana, Sónia Cruz, Tânia Melo, M Rosário Domingues.

  Marine algae are one of the most important sources of high-value compounds such as polar lipids, omega-3 fatty acids, photosynthetic pigments, or secondary metabolites with interesting features for different niche markets. Acetabularia acetabulum is a macroscopic green single-celled alga, with a single nucleus hosted in the rhizoid. This alga is one of the most studied dasycladalean species and represents an important model system in cell biology studies. However, its lipidome and pigment profile have been overlooked. Total lipid extracts were analyzed using hydrophilic interaction liquid chromatography-high resolution mass spectrometry (HILIC-HRMS), tandem mass spectrometry (MS/MS), and high-performance liquid chromatography (HPLC). The antioxidant capacity of lipid extracts was tested using DPPH and ABTS assays. Lipidomics identified 16 polar lipid classes, corresponding to glycolipids, betaine lipids, phospholipids, and sphingolipids, with a total of 191 lipid species, some of them recognized by their bioactivities. The most abundant polar lipids were glycolipids. Lipid classes less studied in algae were identified, such as diacylglyceryl-carboxyhydroxymethylcholine (DGCC) or hexosylceramide (HexCer). The pigment profile of A. acetabulum comprised carotenoids (17.19%), namely cis-neoxanthin, violaxanthin, lutein and β,β-carotene, and chlorophylls a and b (82.81%). A. acetabulum lipid extracts showed high antioxidant activity promoting a 50% inhibition (IC50 ) with concentrations of 57.91 ± 1.20 μg · mL-1 (438.18 ± 8.95 μmol Trolox · g-1 lipid) in DPPH and 20.55 ± 0.60 μg · mL-1 in ABTS assays (918.56 ± 27.55 μmol Trolox · g-1 lipid). This study demonstrates the potential of A. acetabulum as a source of natural bioactive molecules and antioxidant compounds.

Keywords:  Dasycladales; LC-MS; Lipidomics; betaine lipids; bioactivity; carotenoids; chlorophylls; glycolipids; phospholipids

DOI:  https://doi.org/10.1111/jpy.13367
Bioinformatics. 2023 Jul 01. pii: btad455. [Epub ahead of print]39(7):

ipaPy2: Integrated Probabilistic Annotation (IPA) 2.0-an improved Bayesian-based method for the annotation of LC-MS/MS untargeted metabolomics data.

Francesco Del Carratore, William Eagles, Juraj Borka, Rainer Breitling.

SUMMARY: The Integrated Probabilistic Annotation (IPA) is an automated annotation method for LC-MS-based untargeted metabolomics experiments that provides statistically rigorous estimates of the probabilities associated with each annotation. Here, we introduce ipaPy2, a substantially improved and completely refactored Python implementation of the IPA method. The revised method is now able to integrate tandem MS fragmentation data, which increases the accuracy of the identifications. Moreover, ipaPy2 provides a much more user-friendly interface, and isotope peaks are no longer treated as individual features but integrated into isotope fingerprints, greatly speeding up the calculations. The method has also been fully integrated with the mzMatch pipeline, so that the results of the annotation can be explored through the newly developed PeakMLViewerPy tool available at https://github.com/UoMMIB/PeakMLViewerPy.
AVAILABILITY AND IMPLEMENTATION: The source code, extensive documentation, and tutorials are freely available on GitHub at https://github.com/francescodc87/ipaPy2.

DOI: https://doi.org/10.1093/bioinformatics/btad455
bioRxiv. 2023 Jul 19. pii: 2023.07.19.549715. [Epub ahead of print]

A Cell Cycle-Dependent Ferroptosis Sensitivity Switch Governed by EMP2.

Jason Rodencal, Nathan Kim, Veronica L Li, Andrew He, Mike Lange, Jianping He, Amy Tarangelo, Zachary T Schafer, James A Olzmann, Julien Sage, Jonathan Z Long, Scott J Dixon.

Ferroptosis is a non-apoptotic form of cell death characterized by iron-dependent lipid peroxidation. Ferroptosis can be induced by system x c - cystine/glutamate antiporter inhibition or by direct inhibition of the phospholipid hydroperoxidase glutathione peroxidase 4 (GPX4). The regulation of ferroptosis in response to system x c - inhibition versus direct GPX4 inhibition may be distinct. Here, we show that cell cycle arrest enhances sensitivity to ferroptosis triggered by GPX4 inhibition but not system x c - inhibition. Arrested cells have increased levels of oxidizable polyunsaturated fatty acid-containing phospholipids, which drives sensitivity to GPX4 inhibition. Epithelial membrane protein 2 (EMP2) expression is reduced upon cell cycle arrest and is sufficient to enhance ferroptosis in response to direct GPX4 inhibition. An orally bioavailable GPX4 inhibitor increased markers of ferroptotic lipid peroxidation in vivo in combination with a cell cycle arresting agent. Thus, responses to different ferroptosis-inducing stimuli can be regulated by cell cycle state.

DOI: https://doi.org/10.1101/2023.07.19.549715
Metabolomics. 2023 Jul 23. 19(8): 67

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives.

Zohreh Mirveis, Orla Howe, Paul Cahill, Nitin Patil, Hugh J Byrne.

   BACKGROUND: Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.
AIM: This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.
KEY SCIENTIFIC CONCEPTS: Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

Keywords:  Chemometrics; Computational modelling; Flux analysis; Glutaminolysis; Metabolic pathways analysis; Vibrational spectroscopy

DOI:  https://doi.org/10.1007/s11306-023-02031-9
Front Immunol. 2023 ;14 1211126

Therapeutically targeting essential metabolites to improve immunometabolism manipulation after liver transplantation for hepatocellular carcinoma.

Wenhui Zhang, Yu Zhao, Qiang He, Ren Lang.

  Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy worldwide and is associated with a poor prognosis. Sophisticated molecular mechanisms and biological characteristics need to be explored to gain a better understanding of HCC. The role of metabolites in cancer immunometabolism has been widely recognized as a hallmark of cancer in the tumor microenvironment (TME). Recent studies have focused on metabolites that are derived from carbohydrate, lipid, and protein metabolism, because alterations in these may contribute to HCC progression, ischemia-reperfusion (IR) injury during liver transplantation (LT), and post-LT rejection. Immune cells play a central role in the HCC microenvironment and the duration of IR or rejection. They shape immune responses through metabolite modifications and by engaging in complex crosstalk with tumor cells. A growing number of publications suggest that immune cell functions in the TME are closely linked to metabolic changes. In this review, we summarize recent findings on the primary metabolites in the TME and post-LT metabolism and relate these studies to HCC development, IR injury, and post-LT rejection. Our understanding of aberrant metabolism and metabolite targeting based on regulatory metabolic pathways may provide a novel strategy to enhance immunometabolism manipulation by reprogramming cell metabolism.

Keywords:  hepatocellular carcinoma (HCC); immunometabolism; ischemia-reperfusion (IR) injury; lipid metabolism; liver transplantation (LT); succinate

DOI:  https://doi.org/10.3389/fimmu.2023.1211126
Front Immunol. 2023 ;14 1208480

Differentiation in TCM patterns of chronic obstructive pulmonary disease by comprehensive metabolomic and lipidomic characterization.

Jiansheng Li, Xinguang Liu, Yanmin Shi, Yang Xie, Jianya Yang, Yan Du, Ang Zhang, Jinyan Wu.

   Introduction: Chronic obstructive pulmonary disease (COPD) is a complex disease involving inflammation, cell senescence, and autoimmunity. Dialectical treatment for COPD with traditional Chinese medicine (TCM) has the advantage of fewer side effects, more effective suppression of inflammation, and improved immune function. However, the biological base of TCM pattern differentiation in COPD remains unclear.
Methods: Liquid Chromatography-Quadrupole-Orbitrap mass spectrometry (LC-Q-Orbitrap MS/MS) based metabolomics and lipidomics were used to analyze the serum samples from COPD patients of three TCM patterns in Lung Qi Deficiency (n=65), Lung-Kidney Qi Deficiency (n=54), Lung-Spleen Qi Deficiency (n=52), and healthy subjects (n=41). Three cross-comparisons were performed to characterize metabolic markers for different TCM patterns of COPD vs healthy subjects.
Results: We identified 28, 8, and 16 metabolites with differential abundance between three TCM patterns of COPD vs healthy subjects, respectively, the metabolic markers included cortisol, hypoxanthine, fatty acids, alkyl-/alkenyl-substituted phosphatidylethanolamine, and phosphatidylcholine, etc. Three panels of metabolic biomarkers specific to the above three TCM patterns yielded areas under the receiver operating characteristic curve of 0.992, 0.881, and 0.928, respectively, with sensitivity of 97.1%, 88.6%, and 91.4%, respectively, and specificity of 96.4%, 81.8%, and 83.9%, respectively.
Discussion: Combining metabolomics and lipidomics can more comprehensively and accurately trace metabolic markers. As a result, the differences in metabolism were proven to underlie different TCM patterns of COPD, which provided evidence to aid our understanding of the biological basis of dialectical treatment, and can also serve as biomarkers for more accurate diagnosis.

Keywords:  COPD; biomarker; lipidomics; metabolites; traditional Chinese medicine pattern

DOI:  https://doi.org/10.3389/fimmu.2023.1208480