bims-mascan 2023-05-28 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2023–05–28
35 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Accurate label-free quantification by directLFQ to compare unlimited numbers of proteomes.
High-throughput Saccharomyces cerevisiae cultivation method for credentialing-based untargeted metabolomics.
GC/MS-Based Analysis of Fatty Acids and Amino Acids in H460 Cells Treated with Short-Chain and Polyunsaturated Fatty Acids: A Highly Sensitive Approach.
Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics.
MS-based proteomics of body fluids: The end of the beginning.
Electron transport chain inhibition increases cellular dependence on purine transport and salvage.
Chemical isotope labeling assisted liquid chromatography-mass spectrometry method for simultaneous analysis of central carbon metabolism intermediates.
High-Throughput Analysis of Underivatized Amino Acids and Acylcarnitines in Infant Serum: A Micromethod Based on Stable Isotope Dilution Targeted HILIC-ESI-MS/MS.
Mass spectrometry-based phosphoproteomics in clinical applications.
Aberrant Lipid Metabolism in Cancer: Current Status and Emerging Therapeutic Perspectives.
Spatial single cell metabolomics: Current challenges and future developments.
Augmented region of interest for untargeted metabolomics mass spectrometry (AriumMS) of multi-platform-based CE-MS and LC-MS data.
Cannabis sativa L. modulates altered metabolic pathways involved in key metabolisms in human breast cancer (MCF-7) cells: A metabolomics study.
Targeting Cancer Metabolism to Improve Outcomes with Immune Checkpoint Inhibitors.
Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping.
Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples.
Scrutinizing different ionization responses of polar lipids in a reversed-phase gradient by implementing a counter-gradient.
Short-Term Stability of Serum and Liver Extracts for Untargeted Metabolomics and Lipidomics.
The role of Extracellular Vesicles in glycolytic and lipid metabolic reprogramming of cancer cells: Consequences for drug resistance.
MicroRNAs as regulators of tumor metabolism.
Therapeutic strategies targeting metabolic characteristics of cancer cells.
Automated Proteomics Workflows for High-Throughput Library Generation and Biomarker Detection Using Data-Independent Acquisition.
Targeting Oncometabolites in Peritoneal Cancers: Preclinical Insights and Therapeutic Strategies.
Stearoyl-CoA Desaturase 1 as a Therapeutic Biomarker: Focusing on Cancer Stem Cells.
Lipidomic Characterization of Oocytes at Single-Cell Level Using Nanoflow Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry.
Quantitative Aspects of the Human Cell Proteome.
A miniaturized comprehensive approach for total lipidome analysis and vitamin D metabolite quantification in human serum.
Anaplerotic nutrient stress drives synergy of angiogenesis inhibitors with therapeutics targeting tumor metabolism.
MeRgeION: a Multifunctional R Pipeline for Small Molecule LC-MS/MS Data Processing, Searching, and Organizing.
GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma.
Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.
Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective.
Proteomic Signature of Extracellular Vesicles Associated with Colorectal Cancer.
Untargeted metabolomics analysis assisted by signal selection for comprehensively identifying metabolites of new psychoactive substances: 4-MeO-α-PVP as an example.
Peptide CoA conjugates for in situ proteomics profiling of acetyltransferase activities.

Mol Cell Proteomics. 2023 May 22. pii: S1535-9476(23)00092-0. [Epub ahead of print] 100581

Accurate label-free quantification by directLFQ to compare unlimited numbers of proteomes.

Constantin Ammar, Julia Patricia Schessner, Sander Willems, André C Michaelis, Matthias Mann.

  Recent advances in mass spectrometry (MS)-based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks in the bioinformatics pipeline. Whereas peptide identification is already scalable, most label-free quantification (LFQ) algorithms scale quadratic or cubic with the sample numbers, which may even preclude the analysis of large-scale data. Here we introduce directLFQ, a ratio-based approach for sample normalization and the calculation of protein intensities. It estimates quantities via aligning samples and ion traces by shifting them on top of each other in logarithmic space. Importantly, directLFQ scales linearly with the number of samples, allowing analyses of large studies to finish in minutes instead of days or months. We quantify 10,000 proteomes in 10 minutes and 100,000 proteomes in less than two hours - a thousand-fold faster than some implementations of the popular LFQ algorithm MaxLFQ. In-depth characterization of directLFQ reveals excellent normalization properties and benchmark results, comparing favorably to MaxLFQ for both data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, directFQ provides normalized peptide intensity estimates for peptide-level comparisons. It is an important part of an overall quantitative proteomic pipeline that also needs to include high sensitive statistical analysis leading to proteoform resolution. Available as an open-source Python package and a GUI with a one-click installer, it can be used in the AlphaPept ecosystem as well as downstream of most common computational proteomics pipelines.

Keywords:  Proteomics; algorithms; label-free; protein intensity; quantification

DOI:  https://doi.org/10.1016/j.mcpro.2023.100581
Anal Bioanal Chem. 2023 May 22.

High-throughput Saccharomyces cerevisiae cultivation method for credentialing-based untargeted metabolomics.

Lorenzo Favilli, Corey M Griffith, Emma L Schymanski, Carole L Linster.

  Identifying metabolites in model organisms is critical for many areas of biology, including unravelling disease aetiology or elucidating functions of putative enzymes. Even now, hundreds of predicted metabolic genes in Saccharomyces cerevisiae remain uncharacterized, indicating that our understanding of metabolism is far from complete even in well-characterized organisms. While untargeted high-resolution mass spectrometry (HRMS) enables the detection of thousands of features per analysis, many of these have a non-biological origin. Stable isotope labelling (SIL) approaches can serve as credentialing strategies to distinguish biologically relevant features from background signals, but implementing these experiments at large scale remains challenging. Here, we developed a SIL-based approach for high-throughput untargeted metabolomics in S. cerevisiae, including deep-48 well format-based cultivation and metabolite extraction, building on the peak annotation and verification engine (PAVE) tool. Aqueous and nonpolar extracts were analysed using HILIC and RP liquid chromatography, respectively, coupled to Orbitrap Q Exactive HF mass spectrometry. Of the approximately 37,000 total detected features, only 3-7% of the features were credentialed and used for data analysis with open-source software such as MS-DIAL, MetFrag, Shinyscreen, SIRIUS CSI:FingerID, and MetaboAnalyst, leading to the successful annotation of 198 metabolites using MS2 database matching. Comparable metabolic profiles were observed for wild-type and sdh1Δ yeast strains grown in deep-48 well plates versus the classical shake flask format, including the expected increase in intracellular succinate concentration in the sdh1Δ strain. The described approach enables high-throughput yeast cultivation and credentialing-based untargeted metabolomics, providing a means to efficiently perform molecular phenotypic screens and help complete metabolic networks.

Keywords:  High-throughput sample generation; Liquid chromatography; Metabolomics; Saccharomyces cerevisiae; Stable isotope labelling; Untargeted high-resolution mass spectrometry

DOI:  https://doi.org/10.1007/s00216-023-04724-5
Nutrients. 2023 May 17. pii: 2342. [Epub ahead of print]15(10):

GC/MS-Based Analysis of Fatty Acids and Amino Acids in H460 Cells Treated with Short-Chain and Polyunsaturated Fatty Acids: A Highly Sensitive Approach.

Tianxiao Zhou, Kaige Yang, Yinjie Ma, Jin Huang, Wenchang Fu, Chao Yan, Xinyan Li, Yan Wang.

  The important metabolic characteristics of cancer cells include increased fat production and changes in amino acid metabolism. Based on the category of tumor, tumor cells are capable of synthesizing as much as 95% of saturated and monounsaturated fatty acids through de novo synthesis, even in the presence of sufficient dietary lipid intake. This fat transformation starts early when cell cancerization and further spread along with the tumor cells grow more malignant. In addition, local catabolism of tryptophan, a common feature, can weaken anti-tumor immunity in primary tumor lesions and TDLN. Arginine catabolism is likewise related with the inhibition of anti-tumor immunity. Due to the crucial role of amino acids in tumor growth, increasing tryptophan along with arginine catabolism will promote tumor growth. However, immune cells also require amino acids to expand and distinguish into effector cells that can kill tumor cells. Therefore, it is necessary to have a deeper understanding of the metabolism of amino acids and fatty acids within cells. In this study, we established a method for the simultaneous analysis of 64 metabolites consisting of fatty acids and amino acids, covering biosynthesis of unsaturated fatty acids, aminoacyl-tRNA biosynthesis, and fatty acid biosynthesis using the Agilent GC-MS system. We selected linoleic acid, linolenic acid, sodium acetate, and sodium butyrate to treat H460 cells to validate the current method. The differential metabolites observed in the four fatty acid groups in comparison with the control group indicate the metabolic effects of various fatty acids on H460 cells. These differential metabolites could potentially become biomarkers for the early diagnosis of lung cancer.

Keywords:  GC-MS; H460 lung cancer cell; amino acids; fatty acids; targeted metabolomics

DOI:  https://doi.org/10.3390/nu15102342
Metabolites. 2023 May 10. pii: 648. [Epub ahead of print]13(5):

Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics.

Bashar Amer, Rahul R Deshpande, Susan S Bird.

  Untargeted and targeted approaches are the traditional metabolomics workflows acquired for a wider understanding of the metabolome under focus. Both approaches have their strengths and weaknesses. The untargeted, for example, is maximizing the detection and accurate identification of thousands of metabolites, while the targeted is maximizing the linear dynamic range and quantification sensitivity. These workflows, however, are acquired separately, so researchers compromise either a low-accuracy overview of total molecular changes (i.e., untargeted analysis) or a detailed yet blinkered snapshot of a selected group of metabolites (i.e., targeted analysis) by selecting one of the workflows over the other. In this review, we present a novel single injection simultaneous quantitation and discovery (SQUAD) metabolomics that combines targeted and untargeted workflows. It is used to identify and accurately quantify a targeted set of metabolites. It also allows data retro-mining to look for global metabolic changes that were not part of the original focus. This offers a way to strike the balance between targeted and untargeted approaches in one single experiment and address the two approaches' limitations. This simultaneous acquisition of hypothesis-led and discovery-led datasets allows scientists to gain more knowledge about biological systems in a single experiment.

Keywords:  metabolomics; simultaneous quantitation and discovery (SQUAD); targeted metabolomics; untargeted metabolomics

DOI:  https://doi.org/10.3390/metabo13050648
Mol Cell Proteomics. 2023 May 18. pii: S1535-9476(23)00088-9. [Epub ahead of print] 100577

MS-based proteomics of body fluids: The end of the beginning.

Jakob M Bader, Vincent Albrecht, Matthias Mann.

Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification and quantification making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In a previous review we described technological and conceptual limitations that had held back success (Geyer et al., 2017). We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. They are also required for machine learning or deep learning. Shorter gradients, new scan modes and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multi-protein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter into regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.

DOI: https://doi.org/10.1016/j.mcpro.2023.100577
bioRxiv. 2023 May 11. pii: 2023.05.11.540429. [Epub ahead of print]

Electron transport chain inhibition increases cellular dependence on purine transport and salvage.

Zheng Wu, Divya Bezwada, Robert C Harris, Chunxiao Pan, Phong T Nguyen, Brandon Faubert, Ling Cai, Feng Cai, Hieu S Vu, Hongli Chen, Misty Martin- Sandoval, Duyen Do, Wen Gu, Yuannyu Zhang, Bookyung Ko, Bailey Brooks, Sherwin Kelekar, Yuanyuan Zhang, Lauren G Zacharias, K Celeste Oaxaca, Thomas P Mathews, Javier Garcia-Bermudez, Min Ni, Ralph J DeBerardinis.

Cancer cells reprogram their metabolism to support cell growth and proliferation in harsh environments. While many studies have documented the importance of mitochondrial oxidative phosphorylation (OXPHOS) in tumor growth, some cancer cells experience conditions of reduced OXPHOS in vivo and induce alternative metabolic pathways to compensate. To assess how human cells respond to mitochondrial dysfunction, we performed metabolomics in fibroblasts and plasma from patients with inborn errors of mitochondrial metabolism, and in cancer cells subjected to inhibition of the electron transport chain (ETC). All these analyses revealed extensive perturbations in purine-related metabolites; in non-small cell lung cancer (NSCLC) cells, ETC blockade led to purine metabolite accumulation arising from a reduced cytosolic NAD + /NADH ratio (NADH reductive stress). Stable isotope tracing demonstrated that ETC deficiency suppressed de novo purine nucleotide synthesis while enhancing purine salvage. Analysis of NSCLC patients infused with [U- 13 C]glucose revealed that tumors with markers of low oxidative mitochondrial metabolism exhibited high expression of the purine salvage enzyme HPRT1 and abundant levels of the HPRT1 product inosine monophosphate (IMP). ETC blockade also induced production of ribose-5' phosphate (R5P) by the pentose phosphate pathway (PPP) and import of purine nucleobases. Blocking either HPRT1 or nucleoside transporters sensitized cancer cells to ETC inhibition, and overexpressing nucleoside transporters was sufficient to drive growth of NSCLC xenografts. Collectively, this study mechanistically delineates how cells compensate for suppressed purine metabolism in response to ETC blockade, and uncovers a new metabolic vulnerability in tumors experiencing NADH excess.

DOI: https://doi.org/10.1101/2023.05.11.540429
J Chromatogr A. 2023 May 18. pii: S0021-9673(23)00309-6. [Epub ahead of print]1702 464083

Chemical isotope labeling assisted liquid chromatography-mass spectrometry method for simultaneous analysis of central carbon metabolism intermediates.

Sha Li, Yao-Yu Chen, Tian-Tian Ye, Quan-Fei Zhu, Yu-Qi Feng.

  Central carbon metabolism pathway (CCM) is one of the most important metabolic pathways in all living organisms and play crucial function in aspect of organism life. However, the simultaneous detection of CCM intermediates remains challenging. Here, we developed a chemical isotope labeling combined with LC-MS method for simultaneous determination of CCM intermediates with high coverage and accuracy. By chemical derivatization with 2-(diazo-methyl)-N-methyl-N-phenyl-benzamide (2-DMBA) and d5-2-DMBA, all CCM intermediates obtain better separation and accurate quantification at a single LC-MS run. The obtained limits of detection of CCM intermediates ranged from 5 to 36 pg/mL. Using this method, we achieved simultaneous and accurate quantification of 22 CCM intermediates in different biological samples. Take account of the high detection sensitivity of the developed method, this method was further applied to the quantification of CCM intermediates at single-cell level. Finally, 21 CCM intermediates were detected in 1000 HEK-293T cells and 9 CCM intermediates were detected in mouse kidney glomeruli optical slice samples (10∼100 cells).

Keywords:  Central carbon metabolism; Chemical isotope labeling; LC−MS

DOI:  https://doi.org/10.1016/j.chroma.2023.464083
J Agric Food Chem. 2023 May 26.

High-Throughput Analysis of Underivatized Amino Acids and Acylcarnitines in Infant Serum: A Micromethod Based on Stable Isotope Dilution Targeted HILIC-ESI-MS/MS.

Susanne I Wudy, Verena K Mittermeier-Klessinger, Andreas Dunkel, Karin Kleigrewe, Regina Ensenauer, Corinna Dawid, Thomas F Hofmann.

  Amino acids and acylcarnitines are important biomarkers of the body's energy state and can be used as diagnostic markers of certain inborn errors of metabolism. Few multianalyte methods for high-throughput analysis in serum exist for these compounds, but micromethods suitable for use in young children and infants are lacking. Therefore, we developed a quantitative high-throughput multianalyte hydrophilic interaction liquid chromatography-tandem mass spectrometry method preceded by a derivatization-free sample preparation using minimum amounts of serum (25 μL). Isotopically labeled standards were utilized for quantification. Forty amino acids and amino acid derivatives and 22 acylcarnitines were detected by applying a multiple reaction monitoring mode within a 20 min run. The method was comprehensively validated, comprising linearity, accuracy, (intraday/interday) precision, and quantitation limits, of which the latter ranged from 0.25 to 50 nM for acylcarnitines and from 0.005 to 1 μM for amino acids and their derivatives. Application of the method to 145 serum samples of three- to four-month-old healthy infants showed excellent reproducibility for multiday analyses and enabled simultaneous amino acid and acylcarnitine profiling in this age group.

Keywords:  LC-MS; acylcarnitines; amino acids; infant; serum; stable isotope; underivatized

DOI:  https://doi.org/10.1021/acs.jafc.3c00962
Trends Analyt Chem. 2023 Jun;pii: 117066. [Epub ahead of print]163

Mass spectrometry-based phosphoproteomics in clinical applications.

Xiaofeng Wu, Yi-Kai Liu, Anton B Iliuk, W Andy Tao.

  Protein phosphorylation is an essential post-translational modification that regulates many aspects of cellular physiology, and dysregulation of pivotal phosphorylation events is often responsible for disease onset and progression. Clinical analysis on disease-relevant phosphoproteins, while quite challenging, provides unique information for precision medicine and targeted therapy. Among various approaches, mass spectrometry (MS)-centered characterization features discovery-driven, high-throughput and in-depth identification of phosphorylation events. This review highlights advances in sample preparation and instrument in MS-based phosphoproteomics and recent clinical applications. We emphasize the preeminent data-independent acquisition method in MS as one of the most promising future directions and biofluid-derived extracellular vesicles as an intriguing source of the phosphoproteome for liquid biopsy.

Keywords:  Data-independent acquisition; Extracellular vesicle; Mass spectrometry; Phosphoproteomics; Tissue/liquid biopsy

DOI:  https://doi.org/10.1016/j.trac.2023.117066
Curr Top Med Chem. 2023 May 22.

Aberrant Lipid Metabolism in Cancer: Current Status and Emerging Therapeutic Perspectives.

Rasha Irshad, Sazi Tabassum, Mohammad Husain.

  It is now an undisputed fact that cancer cells undergo metabolic reprogramming to support their malignant phenotype, and it is one of the crucial hallmarks which enables cancer cells to facilitate their survival under variable conditions ranging from lack of nutrients to conditions, such as hypoxia. Recent developments in technologies, such as lipidomics and machine learning, have underlined the critical effects of altered lipid metabolism in tumorigenesis. The cancer cells show elevated de novo fatty acid synthesis, an increased capacity to scavenge lipids from their environment, and enhanced fatty acid oxidation to fulfill their need for uncontrolled cellular proliferation, immune evasion, tumor formation, angiogenesis, metastasis, and invasion. Besides, important genes/ proteins involved in lipid metabolism have been proposed as prognostic indicators in a variety of cancer types linked to tumor survival and/or recurrence. Consequently, several approaches are being explored to regulate this metabolic dysregulation to subvert its tumorigenic properties in different types of cancers. The present review details the significance of lipid metabolism in cancer progression, the critical enzymes involved therein, and their regulation. Moreover, the current findings of the interplay between the oncogenic pathways and the lipid metabolic enzymes are elucidated briefly. The therapeutic implications of modulating these aberrations for the advancement of anti-cancer therapies are also discussed. Although the understanding of altered lipid metabolism in cancer initiation and progression is still in its infancy and somewhat obscure, its in-depth comprehension will open promising therapeutic opportunities for the development of novel and promising strategies for cancer treatment and management.

Keywords:  Cancer metabolism; beta-oxidation; fatty acid synthesis; lipid metabolism inhibitors; lipid signaling; lipid uptake

DOI:  https://doi.org/10.2174/1568026623666230522103321
Curr Opin Chem Biol. 2023 May 22. pii: S1367-5931(23)00065-0. [Epub ahead of print]75 102327

Spatial single cell metabolomics: Current challenges and future developments.

Kyle D G Saunders, Holly-May Lewis, Dany Jv Beste, Olivier Cexus, Melanie J Bailey.

  Single cell metabolomics is a rapidly advancing field of bio-analytical chemistry which aims to observe cellular biology with the greatest detail possible. Mass spectrometry imaging and selective cell sampling (e.g. using nanocapillaries) are two common approaches within the field. Recent achievements such as observation of cell-cell interactions, lipids determining cell states and rapid phenotypic identification demonstrate the efficacy of these approaches and the momentum of the field. However, single cell metabolomics can only continue with the same impetus if the universal challenges to the field are met, such as the lack of strategies for standardisation and quantification, and lack of specificity/sensitivity. Mass spectrometry imaging and selective cell sampling come with unique advantages and challenges which, in many cases are complementary to each other. We propose here that the challenges specific to each approach could be ameliorated with collaboration between the two communities driving these approaches.

Keywords:  Lipidomics; Mass spectrometry; Mass spectrometry imaging; Selective cell sampling; Single cell metabolomics

DOI:  https://doi.org/10.1016/j.cbpa.2023.102327
Anal Bioanal Chem. 2023 May 25.

Augmented region of interest for untargeted metabolomics mass spectrometry (AriumMS) of multi-platform-based CE-MS and LC-MS data.

Lukas Naumann, Adrian Haun, Alisa Höchsmann, Michael Mohr, Martin Novák, Dirk Flottmann, Christian Neusüß.

  In mass spectrometry (MS)-based metabolomics, there is a great need to combine different analytical separation techniques to cover metabolites of different polarities and apply appropriate multi-platform data processing. Here, we introduce AriumMS (augmented region of interest for untargeted metabolomics mass spectrometry) as a reliable toolbox for multi-platform metabolomics. AriumMS offers augmented data analysis of several separation techniques utilizing a region-of-interest algorithm. To demonstrate the capabilities of AriumMS, five datasets were combined. This includes three newly developed capillary electrophoresis (CE)-Orbitrap MS methods using the recently introduced nanoCEasy CE-MS interface and two hydrophilic interaction liquid chromatography (HILIC)-Orbitrap MS methods. AriumMS provides a novel mid-level data fusion approach for multi-platform data analysis to simplify and speed up multi-platform data processing and evaluation. The key feature of AriumMS lies in the optimized data processing strategy, including parallel processing of datasets and flexible parameterization for processing of individual separation methods with different peak characteristics. As a case study, Saccharomyces cerevisiae (yeast) was treated with a growth inhibitor, and AriumMS successfully differentiated the metabolome based on the augmented multi-platform CE-MS and HILIC-MS investigation. As a result, AriumMS is proposed as a powerful tool to improve the accuracy and selectivity of metabolome analysis through the integration of several HILIC-MS/CE-MS techniques.

Keywords:  Augmented data evaluation; Capillary electrophoresis; Hydrophilic interaction liquid chromatography; Mid-level data fusion; Multi-platform metabolomics; nanoCEasy

DOI:  https://doi.org/10.1007/s00216-023-04715-6
Heliyon. 2023 May;9(5): e16156

Cannabis sativa L. modulates altered metabolic pathways involved in key metabolisms in human breast cancer (MCF-7) cells: A metabolomics study.

Ochuko L Erukainure, Omolola R Oyenihi, James F Amaku, Chika I Chukwuma, Adeline Lum Nde, Veronica F Salau, Motlalepula G Matsabisa.

  The present study investigated the ability of Cannabis sativa leaves infusion (CSI) to modulate major metabolisms implicated in cancer cells survival, as well as to induce cell death in human breast cancer (MCF-7) cells. MCF-7 cell lines were treated with CSI for 48 h, doxorubicin served as the standard anticancer drug, while untreated MCF-7 cells served as the control. CSI caused 21.2% inhibition of cell growth at the highest dose. Liquid chromatography-mass spectroscopy (LC-MS) profiling of the control cells revealed the presence of carbohydrate, vitamins, oxidative, lipids, nucleotides, and amino acids metabolites. Treatment with CSI caused a 91% depletion of these metabolites, while concomitantly generating selenomethionine, l-cystine, deoxyadenosine triphosphate, cyclic AMP, selenocystathionine, inosine triphosphate, adenosine phosphosulfate, 5'-methylthioadenosine, uric acid, malonic semialdehyde, 2-methylguanosine, ganglioside GD2 and malonic acid. Metabolomics analysis via pathway enrichment of the metabolites revealed the activation of key metabolic pathways relevant to glucose, lipid, amino acid, vitamin, and nucleotide metabolisms. CSI caused a total inactivation of glucose, vitamin, and nucleotide metabolisms, while inactivating key lipid and amino acid metabolic pathways linked to cancer cell survival. Flow cytometry analysis revealed an induction of apoptosis and necrosis in MCF-7 cells treated with CSI. High-performance liquid chromatography (HPLC) analysis of CSI revealed the presence of cannabidiol, rutin, cinnamic acid, and ferulic. These results portray the antiproliferative potentials of CSI as an alternative therapy for the treatment and management of breast cancer as depicted by its modulation of glucose, lipid, amino acid, vitamin, and nucleotide metabolisms, while concomitantly inducing cell death in MCF-7 cells.

Keywords:  Apoptosis; Breast cancer; Cancer metabolism; Cannabis sativa L.; Metabolomics

DOI:  https://doi.org/10.1016/j.heliyon.2023.e16156
J Immunother Precis Oncol. 2023 May;6(2): 91-102

Targeting Cancer Metabolism to Improve Outcomes with Immune Checkpoint Inhibitors.

Zainab Fatima, Abdulrahman Abonofal, Bettzy Stephen.

  Immune checkpoint inhibitors have revolutionized the treatment paradigm of several cancers. However, not all patients respond to treatment. Tumor cells reprogram metabolic pathways to facilitate growth and proliferation. This shift in metabolic pathways creates fierce competition with immune cells for nutrients in the tumor microenvironment and generates by-products harmful for immune cell differentiation and growth. In this review, we discuss these metabolic alterations and the current therapeutic strategies to mitigate these alterations to metabolic pathways that can be used in combination with checkpoint blockade to offer a new path forward in cancer management.

Keywords:  adenosine pathway; amino acid metabolism; glucose metabolism; immune checkpoint inhibitors; lipid metabolism

DOI:  https://doi.org/10.36401/JIPO-22-27
Anal Chem. 2023 May 23.

Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping.

P Vangeenderhuysen, J Van Arnhem, B Pomian, M De Graeve, L De Commer, G Falony, J Raes, A Zhernakova, J Fu, L Y Hemeryck, L Vanhaecke.

In recent years, feces has surfaced as the matrix of choice for investigating the gut microbiome-health axis because of its non-invasive sampling and the unique reflection it offers of an individual's lifestyle. In cohort studies where the number of samples required is large, but availability is scarce, a clear need exists for high-throughput analyses. Such analyses should combine a wide physicochemical range of molecules with a minimal amount of sample and resources and downstream data processing workflows that are as automated and time efficient as possible. We present a dual fecal extraction and ultra high performance liquid chromatography-high resolution-quadrupole-orbitrap-mass spectrometry (UHPLC-HR-Q-Orbitrap-MS)-based workflow that enables widely targeted and untargeted metabolome and lipidome analysis. A total of 836 in-house standards were analyzed, of which 360 metabolites and 132 lipids were consequently detected in feces. Their targeted profiling was validated successfully with respect to repeatability (78% CV < 20%), reproducibility (82% CV < 20%), and linearity (81% R2 > 0.9), while also enabling holistic untargeted fingerprinting (15,319 features, CV < 30%). To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. The latter was benchmarked toward vendor-specific targeted and untargeted software and our isotopologue parameter optimization/XCMS-based untargeted pipeline in LifeLines Deep cohort samples (n = 97). TaPEx clearly outperformed the untargeted approaches (81.3 vs 56.7-66.0% compounds detected). Finally, our novel dual fecal metabolomics-lipidomics-TaPEx method was successfully applied to Flemish Gut Flora Project cohort (n = 292) samples, leading to a sample-to-result time reduction of 60%.

DOI: https://doi.org/10.1021/acs.analchem.2c05371
Metabolites. 2023 May 16. pii: 665. [Epub ahead of print]13(5):

Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples.

Andre Märtens, Johannes Holle, Brit Mollenhauer, Andre Wegner, Jennifer Kirwan, Karsten Hiller.

  Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.

Keywords:  analytical variation; batch effects; metabolomics; quality control

DOI:  https://doi.org/10.3390/metabo13050665
Anal Chim Acta. 2023 Jul 18. pii: S0003-2670(23)00495-6. [Epub ahead of print]1265 341274

Scrutinizing different ionization responses of polar lipids in a reversed-phase gradient by implementing a counter-gradient.

Felina Hildebrand, Harald Schoeny, Evelyn Rampler, Gunda Koellensperger.

  Lipidomics studies strive for a comprehensive identification and quantification of lipids. While reversed phase (RP) liquid chromatography (LC) coupled to high resolution mass spectrometry (MS) offers unrivalled selectivity and thus is the preferred method for lipid identification, accurate lipid quantification remains challenging. The widely adopted one-point lipid class specific quantification (one internal standard per lipid class) suffers from the fact that ionization of internal standard and target lipid occurs under different solvent composition as a consequence of chromatographic separation. To address this issue, we established a dual flow injection and chromatography setup that allows to control solvent conditions during ionization enabling isocratic ionization while running a RP gradient through the use of a counter-gradient. Using this dual LC pump platform, we investigated the impact of solvent conditions within a RP gradient on ionization response and arising quantification biases. Our results confirmed that changing solvent composition significantly influences ionization response. Quantification of human plasma (SRM 1950) lipids under gradient and isocratic ionization conditions further confirmed these findings as significant differences between the two conditions were found for the majority of lipids. While the quantity of sphingomyelins with >40 C atoms was consistently overestimated under gradient ionization, isocratic ionization improved their recovery compared to consensus values. However, the limitation of consensus values was demonstrated as overall only small changes in z-score were observed because of high uncertainties of the consensus values. Furthermore, we observed a trueness bias between gradient and isocratic ionization when quantifying a panel of lipid species standards which is highly dependent on lipid class and ionization mode. Uncertainty calculations under consideration of the trueness bias as RP gradient uncertainty revealed that especially ceramides with >40 C atoms had a high bias leading to total combined uncertainties of up to 54%. The assumption of isocratic ionization significantly decreases total measurement uncertainty and highlights the importance of studying the trueness bias introduced by a RP gradient to reduce quantification uncertainty.

Keywords:  Human plasma; Ionization response; Lipidomics; Mass spectrometry; Quantification; Reversed-phase liquid chromatography

DOI:  https://doi.org/10.1016/j.aca.2023.341274
Antioxidants (Basel). 2023 Apr 24. pii: 986. [Epub ahead of print]12(5):

Short-Term Stability of Serum and Liver Extracts for Untargeted Metabolomics and Lipidomics.

Jiri Hricko, Lucie Rudl Kulhava, Michaela Paucova, Michaela Novakova, Ondrej Kuda, Oliver Fiehn, Tomas Cajka.

  Thermal reactions can significantly alter the metabolomic and lipidomic content of biofluids and tissues during storage. In this study, we investigated the stability of polar metabolites and complex lipids in dry human serum and mouse liver extracts over a three-day period under various temperature conditions. Specifically, we tested temperatures of -80 °C (freezer), -24 °C (freezer), -0.5 °C (polystyrene box with gel-based ice packs), +5 °C (refrigerator), +23 °C (laboratory, room temperature), and +30 °C (thermostat) to simulate the time between sample extraction and analysis, shipping dry extracts to different labs as an alternative to dry ice, and document the impact of higher temperatures on sample integrity. The extracts were analyzed using five fast liquid chromatography-mass spectrometry (LC-MS) methods to screen polar metabolites and complex lipids, and over 600 metabolites were annotated in serum and liver extracts. We found that storing dry extracts at -24 °C and partially at -0.5 °C provided comparable results to -80 °C (reference condition). However, increasing the storage temperatures led to significant changes in oxidized triacylglycerols, phospholipids, and fatty acids within three days. Polar metabolites were mainly affected at storage temperatures of +23 °C and +30 °C.

Keywords:  LC-MS; lipidomics; liquid chromatography; liver; mass spectrometry; metabolomics; oxidation; serum; shipping; stability; tissue

DOI:  https://doi.org/10.3390/antiox12050986
Cytokine Growth Factor Rev. 2023 May 14. pii: S1359-6101(23)00021-7. [Epub ahead of print]

The role of Extracellular Vesicles in glycolytic and lipid metabolic reprogramming of cancer cells: Consequences for drug resistance.

Bárbara Polónia, Cristina P R Xavier, Joanna Kopecka, Chiara Riganti, M Helena Vasconcelos.

  In order to adapt to a higher proliferative rate and an increased demand for energy sources, cancer cells rewire their metabolic pathways, a process currently recognized as a hallmark of cancer. Even though the metabolism of glucose is perhaps the most discussed metabolic shift in cancer, lipid metabolic alterations have been recently recognized as relevant players in the growth and proliferation of cancer cells. Importantly, some of these metabolic alterations are reported to induce a drug resistant phenotype in cancer cells. The acquisition of drug resistance traits severely hinders cancer treatment, being currently considered one of the major challenges of the oncological field. Evidence suggests that Extracellular Vesicles (EVs), which play a crucial role in intercellular communication, may act as facilitators of tumour progression, survival and drug resistance by modulating several aspects involved in the metabolism of cancer cells. This review aims to gather and discuss relevant data regarding metabolic reprograming in cancer, particularly involving the glycolytic and lipid alterations, focusing on its influence on drug resistance and highlighting the relevance of EVs as intercellular mediators of this process.

Keywords:  Cancer; Drug Resistance; Extracellular Vesicles; Intercellular Communication; Metabolic Reprogramming

DOI:  https://doi.org/10.1016/j.cytogfr.2023.05.001
Endocr Relat Cancer. 2023 May 01. pii: ERC-22-0267. [Epub ahead of print]

MicroRNAs as regulators of tumor metabolism.

Francesca Ruggieri, Katharina Jonas, Manuela Ferracin, Michael Dengler, Vanessa Jager, Martin Pichler.

Cancer cells reprogram their metabolism to support their growth. Since the discovery of the Warburg effect, several other metabolic alterations and metabolites have been described in cancer cells, including lactate, glutamine and lipid metabolism reprogramming. Together these alterations provide rapidly dividing tumor cells with metabolic intermediates needed for nucleotide, protein and fatty acid biosynthesis. MicroRNAs are a class of small non-coding RNAs involved in the regulation of virtually all biological pathways. Altered microRNA expression patterns are associated with the onset and development of several diseases, including cancer. Tumor suppressor microRNAs targeting molecules involved in tumor metabolism are frequently downregulated in cancers. Therefore, microRNAs can serve as potential tumor biomarkers and also represent interesting therapeutic targets. This review summarizes recent findings about microRNAs involved in the regulation of tumor metabolism.

DOI: https://doi.org/10.1530/ERC-22-0267
Crit Rev Oncol Hematol. 2023 May 24. pii: S1040-8428(23)00125-7. [Epub ahead of print] 104037

Therapeutic strategies targeting metabolic characteristics of cancer cells.

Rilan Bai, Ying Meng, Jiuwei Cui.

  Metabolic reprogramming is one of the important characteristics of cancer and is a key process leading to malignant proliferation, tumor development and treatment resistance. A variety of therapeutic drugs targeting metabolic reaction enzymes, transport receptors, and special metabolic processes have been developed. In this review, we investigate the characteristics of multiple metabolic changes in cancer cells, including glycolytic pathways, lipid metabolism, and glutamine metabolism changes, describe how these changes promote tumor development and tumor resistance, and summarize the progress and challenges of therapeutic strategies targeting various links of tumor metabolism in combination with current study data.

Keywords:  Cancer; Glycolysis; Metabolism; Oxidative Phosphorylation; Treatment Resistance

DOI:  https://doi.org/10.1016/j.critrevonc.2023.104037
J Proteome Res. 2023 May 23.

Automated Proteomics Workflows for High-Throughput Library Generation and Biomarker Detection Using Data-Independent Acquisition.

Selvam Paramasivan, Janna L Morrison, Mitchell C Lock, Jack R T Darby, Roberto A Barrero, Paul C Mills, Pawel Sadowski.

  Sequential window acquisition of all theoretical mass spectra-mass spectrometry underpinned by advanced bioinformatics offers a framework for comprehensive analysis of proteomes and the discovery of robust biomarkers. However, the lack of a generic sample preparation platform to tackle the heterogeneity of material collected from different sources may be a limiting factor to the broad application of this technique. We have developed universal and fully automated workflows using a robotic sample preparation platform, which enabled in-depth and reproducible proteome coverage and characterization of bovine and ovine specimens representing healthy animals and a model of myocardial infarction. High correlation (R2 = 0.85) between sheep proteomics and transcriptomics datasets validated the developments. The findings suggest that automated workflows can be employed for various clinical applications across different animal species and animal models of health and disease.

Keywords:  FASP; SWATH; automation; biomarker discovery; data-independent acquisition; filter-aided sample preparation; myocardial infarction

DOI:  https://doi.org/10.1021/acs.jproteome.3c00074
Metabolites. 2023 Apr 30. pii: 618. [Epub ahead of print]13(5):

Targeting Oncometabolites in Peritoneal Cancers: Preclinical Insights and Therapeutic Strategies.

Revathy Nadhan, Srishti Kashyap, Ji Hee Ha, Muralidharan Jayaraman, Yong Sang Song, Ciro Isidoro, Danny N Dhanasekaran.

  Peritoneal cancers present significant clinical challenges with poor prognosis. Understanding the role of cancer cell metabolism and cancer-promoting metabolites in peritoneal cancers can provide new insights into the mechanisms that drive tumor progression and can identify novel therapeutic targets and biomarkers for early detection, prognosis, and treatment response. Cancer cells dynamically reprogram their metabolism to facilitate tumor growth and overcome metabolic stress, with cancer-promoting metabolites such as kynurenines, lactate, and sphingosine-1-phosphate promoting cell proliferation, angiogenesis, and immune evasion. Targeting cancer-promoting metabolites could also lead to the development of effective combinatorial and adjuvant therapies involving metabolic inhibitors for the treatment of peritoneal cancers. With the observed metabolomic heterogeneity in cancer patients, defining peritoneal cancer metabolome and cancer-promoting metabolites holds great promise for improving outcomes for patients with peritoneal tumors and advancing the field of precision cancer medicine. This review provides an overview of the metabolic signatures of peritoneal cancer cells, explores the role of cancer-promoting metabolites as potential therapeutic targets, and discusses the implications for advancing precision cancer medicine in peritoneal cancers.

Keywords:  aerobic glycolysis; cancer metabolism; metabolite; metabolome; metabolomics; oncometabolite; peritoneal cancers; tumor microenvironment

DOI:  https://doi.org/10.3390/metabo13050618
Int J Mol Sci. 2023 May 18. pii: 8951. [Epub ahead of print]24(10):

Stearoyl-CoA Desaturase 1 as a Therapeutic Biomarker: Focusing on Cancer Stem Cells.

Jin-Young Min, Do-Hee Kim.

  The dysregulation of lipid metabolism and alterations in the ratio of monounsaturated fatty acids (MUFAs) to saturated fatty acids (SFAs) have been implicated in cancer progression and stemness. Stearoyl-CoA desaturase 1 (SCD1), an enzyme involved in lipid desaturation, is crucial in regulating this ratio and has been identified as an important regulator of cancer cell survival and progression. SCD1 converts SFAs into MUFAs and is important for maintaining membrane fluidity, cellular signaling, and gene expression. Many malignancies, including cancer stem cells, have been reported to exhibit high expression of SCD1. Therefore, targeting SCD1 may provide a novel therapeutic strategy for cancer treatment. In addition, the involvement of SCD1 in cancer stem cells has been observed in various types of cancer. Some natural products have the potential to inhibit SCD1 expression/activity, thereby suppressing cancer cell survival and self-renewal activity.

Keywords:  SCD1; cancer progression; cancer stem cells; lipid metabolism; phytochemicals

DOI:  https://doi.org/10.3390/ijms24108951
Molecules. 2023 May 19. pii: 4202. [Epub ahead of print]28(10):

Lipidomic Characterization of Oocytes at Single-Cell Level Using Nanoflow Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry.

Pujia Zhu, Guowei Bu, Ruifeng Hu, Xianqin Ruan, Rongrong Fu, Zhourui Zhang, Qiongqiong Wan, Xin Liu, Yiliang Miao, Suming Chen.

  Mass spectrometry (MS)-based lipidomic has become a powerful tool for studying lipids in biological systems. However, lipidome analysis at the single-cell level remains a challenge. Here, we report a highly sensitive lipidomic workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS)-MS. This approach enables the high-coverage identification of lipidome landscape at the single-oocyte level. By using the proposed method, comprehensive lipid changes in porcine oocytes during their maturation were revealed. The results provide valuable insights into the structural changes of lipid molecules during porcine oocyte maturation, highlighting the significance of sphingolipids and glycerophospholipids. This study offers a new approach to the single-cell lipidomic.

Keywords:  lipidomic; liquid chromatography; mass spectrometry; oocytes; single-cell

DOI:  https://doi.org/10.3390/molecules28104202
Int J Mol Sci. 2023 May 10. pii: 8524. [Epub ahead of print]24(10):

Quantitative Aspects of the Human Cell Proteome.

Stanislav Naryzhny.

  The number and identity of proteins and proteoforms presented in a single human cell (a cellular proteome) are fundamental biological questions. The answers can be found with sophisticated and sensitive proteomics methods, including advanced mass spectrometry (MS) coupled with separation by gel electrophoresis and chromatography. So far, bioinformatics and experimental approaches have been applied to quantitate the complexity of the human proteome. This review analyzed the quantitative information obtained from several large-scale panoramic experiments in which high-resolution mass spectrometry-based proteomics in combination with liquid chromatography or two-dimensional gel electrophoresis (2DE) were used to evaluate the cellular proteome. It is important that even though all these experiments were performed in different labs using different equipment and calculation algorithms, the main conclusion about the distribution of proteome components (proteins or proteoforms) was basically the same for all human tissues or cells. It follows Zipf's law and has a formula N = A/x, where N is the number of proteoforms, A is a coefficient, and x is the limit of proteoform detection in terms of abundance.

Keywords:  formula; human proteome; quantitation

DOI:  https://doi.org/10.3390/ijms24108524
Anal Bioanal Chem. 2023 May 25.

A miniaturized comprehensive approach for total lipidome analysis and vitamin D metabolite quantification in human serum.

Danilo Donnarumma, Alessia Arena, Emanuela Trovato, Francesca Rigano, Mariosimone Zoccali, Luigi Mondello.

  The balance between the different lipid molecules present in biological fluids accurately reflects the health state of the organism and can be used by medical personnel to finely tune therapy to a single patient, a process known as precision medicine. In this work, we developed a miniaturized workflow for the analysis of different lipid classes at the intact level, as well as their fatty acid constituents, starting from human serum. Fatty acids were identified by using flow-modulated comprehensive gas chromatography coupled to mass spectrometry (FM-GC × GC-MS), and their relative amount as well as the ratio of specific FA classes was determined by using FM-GC × GC with a flame ionization detector. Ultra-high-performance liquid chromatography coupled to tandem mass spectrometry was used for the simultaneous quantification of vitamin D metabolites and assessment of different intact lipid classes. An MRM method was developed for the quantification of five vitamin D metabolites (vitamin D2, vitamin D3, 25-hydroxyvitamin D2, 25-hydroxyvitamin D3, 24R,25-dihydroxyvitamin D3), and validated in terms of LoD, LoQ, accuracy, and precision, also using a certified reference material. At the same time, a combination of SCAN, precursor ion scan, and neutral loss scan, in both positive and negative modes, was used for the identification of 81 intact lipid species, such as phospholipids, cholesteryl esters, and triacylglycerols, in less than 25 min. In order to easily monitor the lipid composition and speed up the identification process, a two-dimensional map of the lipidome was generated, by plotting the molecular weight of the identified molecules versus their retention time. Moreover, a relative quantification was performed within each lipid class identified. The combination of untargeted and targeted data could provide useful information about the pathophysiological condition of the organism and evaluate, in a tailored manner, an efficient action.

Keywords:  Comprehensive two-dimensional gas chromatography; Fatty acids; Lipidomic; Precision medicine; UHPLC-MS/MS; Vitamin D

DOI:  https://doi.org/10.1007/s00216-023-04756-x
bioRxiv. 2023 May 09. pii: 2023.05.07.539744. [Epub ahead of print]

Anaplerotic nutrient stress drives synergy of angiogenesis inhibitors with therapeutics targeting tumor metabolism.

Sunada Khadka, Yu-Hsi Lin, Jeffrey Ackroyd, Yi-An Chen, Yanghui Sheng, Wubin Qian, Sheng Guo, Yining Chen, Eliot Behr, Yasaman Barekatain, Md Nasir Uddin, Kenisha Arthur, Victoria Yan, Wen-Hao Hsu, Edward Chang, Anton Poral, Theresa Tran, Surendra Chaurasia, Dimitra K Georgiou, John M Asara, Floris P Barthel, Steve W Millward, Ronald A DePinho, Florian L Muller.

Tumor angiogenesis is a cancer hallmark, and its therapeutic inhibition has provided meaningful, albeit limited, clinical benefit. While anti-angiogenesis inhibitors deprive the tumor of oxygen and essential nutrients, cancer cells activate metabolic adaptations to diminish therapeutic response. Despite these adaptations, angiogenesis inhibition incurs extensive metabolic stress, prompting us to consider such metabolic stress as an induced vulnerability to therapies targeting cancer metabolism. Metabolomic profiling of angiogenesis-inhibited intracranial xenografts showed universal decrease in tricarboxylic acid cycle intermediates, corroborating a state of anaplerotic nutrient deficit or stress. Accordingly, we show strong synergy between angiogenesis inhibitors (Avastin, Tivozanib) and inhibitors of glycolysis or oxidative phosphorylation through exacerbation of anaplerotic nutrient stress in intracranial orthotopic xenografted gliomas. Our findings were recapitulated in GBM xenografts that do not have genetically predisposed metabolic vulnerabilities at baseline. Thus, our findings cement the central importance of the tricarboxylic acid cycle as the nexus of metabolic vulnerabilities and suggest clinical path hypothesis combining angiogenesis inhibitors with pharmacological cancer interventions targeting tumor metabolism for GBM tumors.

DOI: https://doi.org/10.1101/2023.05.07.539744
Anal Chem. 2023 May 23.

MeRgeION: a Multifunctional R Pipeline for Small Molecule LC-MS/MS Data Processing, Searching, and Organizing.

Youzhong Liu, Yingjie Zhang, Tom Vennekens, Jennifer L Lippens, Luc Duijsens, Danh Bui-Thi, Kris Laukens, Thomas de Vijlder.

Small molecule structure elucidation using tandem mass spectrometry (MS/MS) plays a crucial role in life science, bioanalytical, and pharmaceutical research. There is a pressing need for increased throughput of compound identification and transformation of historical data into information-rich spectral databases. Meanwhile, molecular networking, a recent bioinformatic framework, provides global displays and system-level understanding of complex LC-MS/MS data sets. Herein we present meRgeION, a multifunctional, modular, and flexible R-based toolbox to streamline spectral database building, automated structural elucidation, and molecular networking. The toolbox offers diverse tuning parameters and the possibility to combine various algorithms in the same pipeline. As an open-source R package, meRgeION is ideally suited for building spectral databases and molecular networks from privacy-sensitive and preliminary data. Using meRgeION, we have created an integrated spectral database covering diverse pharmaceutical compounds that was successfully applied to annotate drug-related metabolites from a published nontargeted metabolomics data set as well as reveal the chemical space behind this complex data set through molecular networking. Moreover, the meRgeION-based processing workflow has demonstrated the usefulness of a spectral library search and molecular networking for pharmaceutical forced degradation studies. meRgeION is freely available at: https://github.com/daniellyz/meRgeION2.

DOI: https://doi.org/10.1021/acs.analchem.2c04343
PeerJ. 2023 ;11 e15302

GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma.

Ding Wang, Jing Zhu, Na Li, Hongyang Lu, Yun Gao, Lei Zhuang, Zhongjian Chen, Weimin Mao.

   Background: Malignant mesothelioma (MM) is a cancer caused mainly by asbestos exposure, and is aggressive and incurable. This study aimed to identify differential metabolites and metabolic pathways involved in the pathogenesis and diagnosis of malignant mesothelioma.
Methods: By using gas chromatography-mass spectrometry (GC-MS), this study examined the plasma metabolic profile of human malignant mesothelioma. We performed univariate and multivariate analyses and pathway analyses to identify differential metabolites, enriched metabolism pathways, and potential metabolic targets. The area under the receiver-operating curve (AUC) criterion was used to identify possible plasma biomarkers.
Results: Using samples from MM (n = 19) and healthy control (n = 22) participants, 20 metabolites were annotated. Seven metabolic pathways were disrupted, involving alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; arginine and proline metabolism; butanoate and histidine metabolism; beta-alanine metabolism; and pentose phosphate metabolic pathway. The AUC was used to identify potential plasma biomarkers. Using a threshold of AUC = 0.9, five metabolites were identified, including xanthurenic acid, (s)-3,4-hydroxybutyric acid, D-arabinose, gluconic acid, and beta-d-glucopyranuronic acid.
Conclusions: To the best of our knowledge, this is the first report of a plasma metabolomics analysis using GC-MS analyses of Asian MM patients. Our identification of these metabolic abnormalities is critical for identifying plasma biomarkers in patients with MM. However, additional research using a larger population is needed to validate our findings.

Keywords:  Biomarker; GC-MS; Malignant mesothelioma; Metabolomics

DOI:  https://doi.org/10.7717/peerj.15302
Proteomes. 2023 May 02. pii: 16. [Epub ahead of print]11(2):

Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery.

Neha Varshney, Abhinava K Mishra.

  Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.

Keywords:  cancer; deep learning; machine learning; personalized medicine; phosphoproteomics; post-translational modification

DOI:  https://doi.org/10.3390/proteomes11020016
Mol Cell Proteomics. 2023 May 18. pii: S1535-9476(23)00089-0. [Epub ahead of print] 100578

Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective.

Liujia Qian, Rui Sun, Zhangzhi Xue, Tiannan Guo.

  Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990, and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

Keywords:  clinical trials; early diagnosis; epithelial ovarian cancer; proteomics; therapeutic targets

DOI:  https://doi.org/10.1016/j.mcpro.2023.100578
Molecules. 2023 May 22. pii: 4227. [Epub ahead of print]28(10):

Proteomic Signature of Extracellular Vesicles Associated with Colorectal Cancer.

Natalia Soloveva, Svetlana Novikova, Tatiana Farafonova, Olga Tikhonova, Victor Zgoda.

  The proteins of extracellular vesicles (EVs) provide proteomic signatures that reflect molecular features of EV-producing cells, including cancer cells. Detection of cancer cell EV proteins is of great interest due to the development of novel predictive diagnostic approaches. Using targeted mass spectrometry with stable-isotope-labeled peptide standards (SIS), we measured in this study the levels of 34 EV-associated proteins in vesicles and whole lysate derived from the colorectal cancer (CRC) cell lines Caco-2, HT29 and HCT116. We also evaluated the abundance of 13 EV-associated proteins (FN1, TLN1, ITGB3, HSPA8, TUBA4A, CD9, CD63, HSPG2, ITGB1, GNAI2, TSG101, PACSIN2, and CDC42) in EVs isolated from blood plasma samples from 11 CRC patients and 20 healthy volunteers. Downregulation of TLN1, ITGB3, and TUBA4A with simultaneous upregulation of HSPG2 protein were observed in cancer samples compared to healthy controls. The proteomic cargo of the EVs associated with CRC represents a promising source of potential prognostic markers.

Keywords:  SRM; colorectal cancer; extracellular vesicles; mass spectrometry; proteomic signature; stable-isotope-labeled peptide standards

DOI:  https://doi.org/10.3390/molecules28104227
J Food Drug Anal. 2023 Mar 15. 31(1): 137-151

Untargeted metabolomics analysis assisted by signal selection for comprehensively identifying metabolites of new psychoactive substances: 4-MeO-α-PVP as an example.

Hsin-Yi Wu, Yuan-Chih Chen, Jing-Fang Hsu, Hsiang-Ting Lu, Yu-Yi Pan, Mi-Chia Ma, Pao-Chi Liao.

New psychoactive substances (NPS) have been rapidly emerged as legal alternatives to controlled drugs, which raised severe public health issue. The detection and monitoring of its intake by complete metabolic profiling is an urgent and vital task. Untargeted metabolomics approach has been applied for several NPS metabolites studies. Although the number of such works is relatively limited but with a rapidly growing need. The present study aimed to propose a procedure that includes liquid chromatography high-resolution mass spectrometry (LC-HRMS) analysis and a signal selection software, MetaboFinder, programed as a web tool. The comprehensive metabolites profile of one kind of NPS, 4-methoxy-α-pyrrolidinovalerophenone (4-MeO-α-PVP), was studied by using this workflow. In this study, two different concentrations of 4-MeO-α-PVP along with as blank sample were incubated with human liver S9 fraction for the conversion to their metabolites and followed by LC-MS analysis. After retention time alignment and feature identification, 4640 features were obtained and submitted to statistical analysis for signal selection by using MetaboFinder. A total of 50 features were considered as 4-MeO-α-PVP metabolite candidates showing significant changes (p < 0.00001 and fold change >2) between the two investigated groups. Targeted LC-MS/MS analysis was conducted focusing on these significantly expressed features. Assisted by chemical formula determination according to high mass accuracy and in silico MS2 fragmentation prediction, 19 chemical structure identifications were achieved. Among which, 8 metabolites have been reported derived from 4-MeO-α-PVP in a previous literature while 11 novel 4-MeO-α-PVP metabolites were identified by using our strategy. Further in vivo animal experiment confirmed that 18 compounds were 4-MeO-α-PVP metabolites, which demonstrated the feasibility of our strategy for screening the 4-MeO-α-PVP metabolites. We anticipate that this procedure may support and facilitate traditional metabolism studies and potentially being applied for routine NPS metabolites screening.

DOI: https://doi.org/10.38212/2224-6614.3447
Methods Enzymol. 2023 ;pii: S0076-6879(22)00386-X. [Epub ahead of print]684 209-252

Peptide CoA conjugates for in situ proteomics profiling of acetyltransferase activities.

Jürgen Eirich, Julia Sindlinger, Stefan Schön, Dirk Schwarzer, Iris Finkemeier.

  The acetylation of protein N-termini is a co- or posttranslational modification that plays important roles in protein homeostasis and stability. N-terminal acetyltransferases (NATs) catalyze the introduction of this modification using acetyl-coenzyme A (acetyl-CoA) as source of the acetyl-group. NATs operate in complex with auxiliary proteins that impact activity and specificity of these enzymes. Proper function of NATs is essential for development in plants and mammals alike. High resolution mass spectrometry (MS) is a powerful tool for investigating NATs and protein complexes in general. However, efficient methods for enriching NAT complexes ex vivo from cellular extracts are needed for the subsequent analysis. Based on bisubstrate analog inhibitors of lysine acetyltransferases, peptide-CoA conjugates have been developed as capture compounds of NATs. The N-terminal residue of these probes, serving as attachment site of the CoA moiety, was shown to impact NAT binding according to the respective amino acid specificity of these enzymes. This chapter reports the detailed protocols for the synthesis of peptide-CoA conjugates, the experimental procedures for NAT enrichment as well as the MS and data analysis. Collectively, these protocols provide a set of tools for profiling NAT complexes in cell lysates of healthy or diseases backgrounds.

Keywords:  Acetyltransferases; Activity-based profiling; Interactome; N-terminal acetylation; Peptide-CoA conjugates; Quantitative proteomics

DOI:  https://doi.org/10.1016/bs.mie.2022.09.005