bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2022‒11‒27
24 papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh

  1. Nat Commun. 2022 Nov 24. 13(1): 7238
      Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides ( ). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition ( ).
  2. Metabolomics. 2022 Nov 19. 18(12): 94
      BACKGROUND: Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments.AIM OF REVIEW: We describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries.
    KEY SCIENTIFIC CONCEPTS OF REVIEW: This review focuses on the current state of spectral libraries for untargeted LC-MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future.
    Keywords:  Compound identification; Mass spectrometry; Spectral library; Untargeted metabolomics
  3. Methods Mol Biol. 2023 ;2581 309-319
      Mass spectrometry-based proteomics provide a powerful tool for plant research, allowing global detection of steady-state levels of proteins under a given experimental setup. Here, we provide an optimized protocol for proteomic profiling using tandem mass tag (TMT) labeling followed by liquid chromatography-mass spectrometry (LC-MS/MS) to quantitate phosphopeptides and non-phosphopeptides from the same samples. The outlined protocol comprises a series of successive steps, namely, SDS (sodium dodecyl sulfate) protein extraction, protein precipitation, digestion, TMT labeling, phosphopeptide enrichment, high pH reversed-phase fractionation, LC-MS/MS analysis, protein identification, and data analysis. Our proteome-scale protocol requires 0.1 mg protein per sample and allows for the reliable and accurate quantification of more than 8000 proteins in Arabidopsis plant samples across multiple conditions, including low abundant peptides.
    Keywords:  Mass spectrometry; Phosphopeptide enrichment; Protein quantification; Proteome analysis; Tandem mass tag
  4. Biochim Biophys Acta Rev Cancer. 2022 Nov 17. pii: S0304-419X(22)00162-7. [Epub ahead of print]1878(1): 188837
      Acetyl-CoA, as an important molecule, not only participates in multiple intracellular metabolic reactions, but also affects the post-translational modification of proteins, playing a key role in the metabolic activity and epigenetic inheritance of cells. Cancer cells require extensive lipid metabolism to fuel for their growth, while also require histone acetylation modifications to increase the expression of cancer-promoting genes. As a raw material for de novo lipid synthesis and histone acetylation, acetyl-CoA has a major impact on lipid metabolism and histone acetylation in cancer. More importantly, in cancer, acetyl-CoA connects lipid metabolism with histone acetylation, forming a more complex regulatory mechanism that influences cancer growth, proliferation, metastasis.
    Keywords:  Acetyl-coenzyme A (acetyl-CoA); Cancer; Histone acetylation; Lipid metabolism
  5. Handb Exp Pharmacol. 2022 Nov 22.
      The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.
    Keywords:  Compound identification; Identification confidence; LC-MS; MS/MS search; Metabolomics; Molecular formula assignment
  6. Anal Chem. 2022 Nov 25.
      One of the technical challenges in the field of metabolomics is the development of a single-run method to detect the full complement of polar metabolites in biological samples. However, an ideal method to meet this demand has not yet been developed. Herein, we proposed a simple methodology that enables the comprehensive and simultaneous analysis of polar metabolites using unified-hydrophilic-interaction/anion-exchange liquid chromatography mass spectrometry (unified-HILIC/AEX/MS) with a polymer-based mixed amines column composed of methacrylate-based polymer particles with primary, secondary, tertiary, and quaternary amines as functional groups. The optimized unified-HILIC/AEX/MS method is composed of two consecutive chromatographic separations, HILIC-dominant separation for cationic, uncharged, and zwitterionic polar metabolites [retention times (RTs) = 0-12.8 min] and AEX-dominant separation for polar anionic metabolites (RTs = 12.8-26.5 min), by varying the ratio of acetonitrile to 40 mM ammonium bicarbonate solution (pH 9.8). A total of 400 polar metabolites were analyzed simultaneously through a combination of highly efficient separation using unified-HILIC/AEX and remarkably sensitive detection using multiple reaction monitoring-based triple quadrupole mass spectrometry (unified-HILIC/AEX/MS/MS). A nontargeted metabolomic approach using unified-HILIC/AEX high-resolution mass spectrometry (unified-HILIC/AEX/HRMS) also provided more comprehensive information on polar metabolites (3242 metabolic features) in HeLa cell extracts than the conventional HILIC/HRMS method (2068 metabolic features). Our established unified-HILIC/AEX/MS/MS and unified-HILIC/AEX/HRMS methods have several advantages over conventional techniques, including polar metabolome coverage, throughput, and accurate quantitative performance, and represent potentially useful tools for in-depth studies on metabolism and biomarker discovery.
  7. Int J Mol Sci. 2022 Nov 10. pii: 13818. [Epub ahead of print]23(22):
      Glioblastoma (GBM) is the most malignant primary brain tumor. Despite increasing research on GBM treatment, the overall survival rate has not significantly improved over the last two decades. Although recent studies have focused on aberrant metabolism in GBM, there have been few advances in clinical application. Thus, it is important to understand the systemic metabolism to eradicate GBM. Together with the Warburg effect, lipid metabolism has emerged as necessary for GBM progression. GBM cells utilize lipid metabolism to acquire energy, membrane components, and signaling molecules for proliferation, survival, and response to the tumor microenvironment. In this review, we discuss fundamental cholesterol, fatty acid, and sphingolipid metabolism in the brain and the distinct metabolic alterations in GBM. In addition, we summarize various studies on the regulation of factors involved in lipid metabolism in GBM therapy. Focusing on the rewiring of lipid metabolism will be an alternative and effective therapeutic strategy for GBM treatment.
    Keywords:  cholesterol; fatty acid; glioblastoma; lipid metabolism; metabolic reprogramming; sphingolipid
  8. Methods Mol Biol. 2023 ;2581 323-335
      Many peptide hormones and growth factors in plants, particularly the small posttranslationally modified signaling peptides, are synthesized as larger precursor proteins. Proteolytic processing is thus required for peptide maturation, and additional posttranslational modifications may contribute to bioactivity. To what extent these posttranslational modifications impact on processing is largely unknown. Likewise, it is poorly understood how the cleavage sites within peptide precursors are selected by specific processing proteases, and whether or not posttranslational modifications contribute to cleavage site recognition. Here, we describe a mass spectrometry-based approach to address these questions. We developed a method using heavy isotope labeling to directly compare cleavage efficiency of different precursor-derived synthetic peptides by mass spectrometry. Thereby, we can analyze the effect of posttranslational modifications on processing and the specific sequence requirements of the processing proteases. As an example, we describe how this method has been used to assess the relevance of tyrosine sulfation for the processing of the Arabidopsis CIF4 precursor by the subtilase SBT5.4.
    Keywords:  Cleavage specificity; Heavy isotope; Liquid chromatography-mass spectrometry (LC-MS); Nicotiana benthamiana; Peptide isotopologs; Posttranslational modification; Precursor processing; Quantitative proteomics; Transient expression; Tyrosine sulfation
  9. Metabolites. 2022 Nov 21. pii: 1154. [Epub ahead of print]12(11):
      Clinical endocrinology entails an understanding of the mechanisms involved in the regulation of tumors that occur in the endocrine system. The exact cause of endocrine cancers remains an enigma, especially when discriminating malignant lesions from benign ones and early diagnosis. In the past few years, the concepts of personalized medicine and metabolomics have gained great popularity in cancer research. In this systematic review, we discussed the clinical metabolomics studies in the diagnosis of endocrine cancers within the last 12 years. Cancer metabolomic studies were largely conducted using nuclear magnetic resonance (NMR) and mass spectrometry (MS) combined with separation techniques such as gas chromatography (GC) and liquid chromatography (LC). Our findings revealed that the majority of the metabolomics studies were conducted on tissue, serum/plasma, and urine samples. Studies most frequently emphasized thyroid cancer, adrenal cancer, and pituitary cancer. Altogether, analytical hyphenated techniques and chemometrics are promising tools in unveiling biomarkers in endocrine cancer and its metabolism disorders.
    Keywords:  biomarkers; early diagnosis; endocrine cancers; metabolic pathways; metabolomics
  10. Methods Mol Biol. 2023 ;2588 13-23
      Mass spectrometry-based proteomic approaches permit the high-throughput assessment of proteins from oral biofluids, therefore, allowing a deeper insight into the mechanistic study of periodontal disease. Here we describe an entire experimental design of proteomic workflow for oral biofluids, exemplified by saliva and gingival crevicular fluid collected from periodontal health or disease subjects and using a label-free quantification strategy for mass spectrometric data acquisition.
    Keywords:  Gingival crevicular fluid; Label-free quantification; Mass spectrometry; Periodontal disease; Proteomics; Saliva
  11. Nat Commun. 2022 Nov 25. 13(1): 7246
      Single cell proteomics is a powerful tool with potential for markedly enhancing understanding of cellular processes. Here we report the development and application of multiplexed single cell proteomics using trapped ion mobility time-of-flight mass spectrometry. When employing a carrier channel to improve peptide signal, this method allows over 40,000 tandem mass spectra to be acquired in 30 min. Using a KRASG12C model human-derived cell line, we demonstrate the quantification of over 1200 proteins per cell with high relative sequence coverage permitting the detection of multiple classes of post-translational modifications in single cells. When cells were treated with a KRASG12C covalent inhibitor, this approach revealed cell-to-cell variability in the impact of the drug, providing insight missed by traditional proteomics. We provide multiple resources necessary for the application of single cell proteomics to drug treatment studies including tools to reduce cell cycle linked proteomic effects from masking pharmacological phenotypes.
  12. Anal Chem. 2022 Nov 22.
      The Paternò-Büchi (PB) reaction is a carbon-carbon double bond (C═C)-specific derivatization reaction that can be used to pinpoint the location(s) of C═C(s) in unsaturated lipids and quantitate the location of isomers when coupled with tandem mass spectrometry (MS/MS). As the data of PB-MS/MS are increasingly generated, the establishment of a corresponding data analysis tool is highly needed. Herein, LipidOA, a machine-learning and prior-knowledge-based data analysis tool, is developed to analyze PB-MS/MS data generated by liquid chromatography-mass spectrometry workflows. LipidOA consists of four key functional modules to realize an annotation of glycerophospholipid (GPL) structures at the fatty acyl-specific C═C location level. These include (1) data preprocessing, (2) picking C═C diagnostic ions, (3) de novo annotation, and (4) result ranking. Importantly, in the result-ranking module, the reliability of structural annotation is sorted via the use of a machine learning classifier and comparison to the total fatty acid database generated from the same sample. LipidOA is trained and validated by four PB-MS/MS data sets acquired using different PB reagents on mass spectrometers of different resolutions and of different biological samples. Overall, LipidOA provides high precision (higher than 0.9) and a wide coverage for structural annotations of GPLs. These results demonstrate that LipidOA can be used as a robust and flexible tool for annotating PB-MS/MS data collected under different experimental conditions using different lipidomic workflows.
  13. EMBO J. 2022 Nov 21. e111268
      Reprogramming of lipid metabolism is emerging as a hallmark of cancer, yet involvement of specific fatty acids (FA) species and related enzymes in tumorigenesis remains unclear. While previous studies have focused on involvement of long-chain fatty acids (LCFAs) including palmitate in cancer, little attention has been paid to the role of very long-chain fatty acids (VLCFAs). Here, we show that depletion of acetyl-CoA carboxylase (ACC1), a critical enzyme involved in the biosynthesis of fatty acids, inhibits both de novo synthesis and elongation of VLCFAs in human cancer cells. ACC1 depletion markedly reduces cellular VLCFA but only marginally influences LCFA levels, including palmitate that can be nutritionally available. Therefore, tumor growth is specifically susceptible to regulation of VLCFAs. We further demonstrate that VLCFA deficiency results in a significant decrease in ceramides as well as downstream glucosylceramides and sphingomyelins, which impairs mitochondrial morphology and renders cancer cells sensitive to oxidative stress and cell death. Taken together, our study highlights that VLCFAs are selectively required for cancer cell survival and reveals a potential strategy to suppress tumor growth.
    Keywords:  acetyl-CoA carboxylase; fatty acid elongation; fatty acid synthase; mitochondria potential; very long-chain fatty acids
  14. Adv Biol (Weinh). 2022 Nov 23. e2200233
      Relapses negatively impact cancer patient survival due to the tumorigenesis ability of surviving cancer cells post-therapy. Efforts are needed to better understand and combat this problem. This study hypothesized that dead cell debris post-radiation therapy creates an advantageous microenvironment rich in metabolic materials promoting the growth of remaining live cancer cells. In this study, live cancer cells are co-cultured with dead cancer cells eradicated by UV radiation to mimic a post-therapy environment. Isotopic labeling metabolomics is used to investigate the metabolic behavior of cancer cells grown in a post-radiation-therapy environment. It is found that post-UV-eradicated dead cancer cells serve as nutritional sources of "off-the-shelf" and precursor metabolites for surviving cancer cells. The surviving cancer cells then take up these metabolites, integrate and upregulate multiple vital metabolic processes, thereby significantly increasing growth in vitro and probably in vivo beyond their intrinsic fast-growing characteristics. Importantly, this active metabolite uptake behavior is only observed in oncogenic but not in non-oncogenic cells, presenting opportunities for therapeutic approaches to interrupt the active uptake process of oncogenic cells without affecting normal cells. The process by which living cancer cells re-use vital metabolites released by dead cancer cells post-therapy is coined in this study as "metabolic recycling" of oncogenic cells.
    Keywords:  13C6-glucose labeling; MYC-transformed lymphoma B cells; cancer metabolism; glucose metabolism; mass spectrometry; metabolic recycling; metabolomics
  15. Metabolites. 2022 Nov 15. pii: 1111. [Epub ahead of print]12(11):
      Mass spectrometry (MS)-based techniques, including liquid chromatography coupling, shotgun lipidomics, MS imaging, and ion mobility, are widely used to analyze lipids. However, with enhanced separation capacity and an optimized chemical derivatization approach, comprehensive two-dimensional gas chromatography (GC×GC) can be a powerful tool to investigate some groups of small lipids in the framework of lipidomics. This study describes the optimization of a dedicated two-stage derivatization and extraction process to analyze different saturated and unsaturated fatty acids in plasma by two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) using a full factorial design. The optimized condition has a composite desirability of 0.9159. This optimized sample preparation and chromatographic condition were implemented to differentiate between positive (BT) and negative (UT) boar-tainted pigs based on fatty acid profiling in pig serum using GC×GC-TOFMS. A chemometric screening, including unsupervised (PCA, HCA) and supervised analysis (PLS-DA), as well as univariate analysis (volcano plot), was performed. The results suggested that the concentration of PUFA ω-6 and cholesterol derivatives were significantly increased in BT pigs, whereas SFA and PUFA ω-3 concentrations were increased in UT pigs. The metabolic pathway and quantitative enrichment analysis suggest the significant involvement of linolenic acid metabolism.
    Keywords:  GC×GC–TOFMS; boar taint; fatty acids; gas chromatography; lipidomics
  16. Front Microbiol. 2022 ;13 1053330
      The metabolic microenvironment of bacteria impacts drug efficacy. However, the metabolic mechanisms of drug-resistant Salmonella spp. remain largely unknown. This study characterized the metabolic mechanism of gentamicin-resistant Salmonella Choleraesuis and found that D-ribose increased the gentamicin-mediated killing of this bacteria. Non-targeted metabolomics of homologous gentamicin-susceptible Salmonella Choleraesuis (SCH-S) and gentamicin-resistant S. Choleraesuis (SCH-R) was performed using UHPLC-Q-TOF MS. The metabolic signature of SCH-R included disrupted central carbon metabolism and energy metabolism, along with dysregulated amino acid and nucleotide metabolism, vitamin and cofactor metabolism, and fatty acid synthesis. D-ribose, the most suppressed metabolite in SCH-R, was shown to strengthen gentamicin efficacy against SCH-R and a clinically isolated multidrug-resistant strain. This metabolite had a similar impact on Salmonella. Derby and Salmonella. Typhimurium. D-ribose activates central carbon metabolism including glycolysis, the pentose phosphate pathway (PPP), and the tricarboxylic acid cycle (TCA cycle), increases the abundance of NADH, polarizes the electron transport chain (ETC), and elevates the proton motive force (PMF) of cells, and induces drug uptake and cell death. These findings suggest that central carbon metabolism plays a critical role in the acquisition of gentamicin resistance by Salmonella, and that D-ribose may serve as an antibiotic adjuvant for gentamicin treatment of resistant bacterial infections.
    Keywords:  D-ribose; Salmonella; gentamicin; metabolomics; resistance
  17. J Proteome Res. 2022 Nov 25.
      Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy; still, they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become a promising tool for this purpose. However, it is sometimes applied in an opaque manner and generally requires specialized knowledge. To enable easy access to ML for biomarker discovery without any programming or bioinformatics skills, we developed "OmicLearn" (, an open-source browser-based ML tool using the latest advances in the Python ML ecosystem. Data matrices from omics experiments are easily uploaded to an online or a locally installed web server. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental data sets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.
    Keywords:  diagnostics; machine learning; mass spectrometry; metabolome; omics; proteome; transcriptome
  18. Proteomics. 2022 Nov 24. e2100387
      The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic Stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulse SILAC (pSILAC). Very recent studies including ours have integrated the study of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics), with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased "hypothesis-free" analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover. This article is protected by copyright. All rights reserved.
    Keywords:  Clustering; DIA-MS; Data analysis; DeltaSILAC; Peptidoform; Phosphorylation; Protein turnover; Pulse SILAC; Time course
  19. J Proteome Res. 2022 Nov 22.
      Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich spectra challenges existing data storage and access technologies, therefore precluding informatics advancements. We present MZA (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed of a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and cross-programming language access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable, example MS-data and example Python and R scripts are freely available at
    Keywords:  data conversion; data-independent acquisition; ion mobility spectrometry; mass spectrometry; open data format
  20. Adv Clin Chem. 2022 ;pii: S0065-2423(22)00067-1. [Epub ahead of print]111 217-263
      Traditional clinical toxicology involves the analysis of patient urine samples by immunoassays designed to detect opiates/opioids, amphetamine/methamphetamine, benzodiazepines, barbiturates, cocaine metabolite and tetrahydrocannabinol. Expanded drug screens may also include assays for oxycodone, buprenorphine, methadone, 6-monoacetylmorphine, phencyclidine and fentanyl. Patient samples that are positive are commonly reflexed to be run on a liquid chromatography-tandem mass spectrometry confirmatory assay, as are samples that are negative for drugs that are prescribed to the patient. These mass spectrometry assays are targeted and so only detect the drugs or drug metabolites that they were designed to detect. With the explosion of new psychoactive substances in the past decade, it has become necessary for clinical laboratories to reevaluate traditional targeted drug screening approaches. The utility of high-resolution mass spectrometry in this arena has been recognized and this review will discuss the traditional approach to, and the recent advances in clinical toxicology including data collection and interrogation strategies for new psychoactive substances using high-resolution mass spectrometry (HRMS). Various modes of data processing techniques including targeted analysis, suspect screening and non-targeted analysis will also be described using HRMS. Several published methods will be described to demonstrate the utility of various data acquisition and processing techniques using HRMS in NPS analysis specifically.
    Keywords:  Accurate mass; Data dependent acquisition; Data independent acquisition; Drug screening; Exact mass; HRMS; High-resolution mass spectrometry; Immunoassay; LC-MS/MS; New psychoactive substances; Non-targeted analysis; Sample preparation; Suspect screening; Targeted analysis; Time-of-flight; Toxicology
  21. Biomed Pharmacother. 2022 Dec;pii: S0753-3322(22)01312-9. [Epub ahead of print]156 113923
      Malignant tumors are non-communicable diseases that affect human life health and quality of life. Anti-tumour-related research has also been the focus and difficulty in oncology research. With the rise of metabolomics, tumour biology, and the theory of tumour reprogramming, amino acid metabolic reprogramming has become a new target for antitumor research. Amino acids provide biomolecules such as nucleotides for tumour cell proliferation, invasion, and immune escape processes. They are also essential metabolites for immune cell activation and antitumor effects in the tumour microenvironment. Abnormal changes in amino acid metabolism are closely related to tumour development and immunity. Some essential proteins or critical enzymes in their metabolic pathways can be used for tumour diagnosis and prognosis assessment markers. Therefore, this paper reviews the effects of amino acid metabolism on tumour cell proliferation and the abnormal alterations of amino acid metabolism during the tumour metabolic cycle and analyzes and prospects the tumour therapeutic drugs targeting amino acid metabolism. This paper provides theoretical references for the in-depth study of the regulation of amino acid metabolism on tumour development and its possible therapeutic targets.
    Keywords:  Amino acids; Metabolic reprogramming; Targeted amino acid metabolism; Tumour immunity; Tumour therapy
  22. Methods Mol Biol. 2023 ;2583 149-156
      Diverse metabolic disorders can disrupt brain growth, and analyzing metabolism in animal models of microcephaly may reveal new mechanisms of pathogenesis. The metabolism of functioning cells in a living organism is constantly changing in response to a changing environment, circadian rhythms, consumed food, drugs, progressing sicknesses, aging, and many other factors. Metabolic profiling can give important insights into the working machinery of the cell. However, a frozen snapshot of the interconnected, complex network of reactions gives very limited information about this system. Flux analysis using stable isotope labels enables more robust metabolic studies that consider interrogate metabolite processing and changes in molecular concentrations over time.
    Keywords:  13C; Fluxomics; Intermediaries; Metabolomics; Stable isotope label
  23. Metabolites. 2022 Nov 11. pii: 1098. [Epub ahead of print]12(11):
      Freezing and thawing plasma samples is known to perturb metabolite stability. However, no study has systematically tested how different freezing and thawing methods affect plasma metabolite levels. The objective of this study was to isolate the effects of freezing from thawing on mouse plasma metabolite levels, by comparing a matrix of freezing and thawing conditions through 10 freeze-thaw cycles. We tested freezing with liquid nitrogen (LN2), at -80 °C, or at -20 °C, and thawing quickly in room temperature water or slowly on ice. Plasma samples were extracted and the relative abundance of 87 metabolites was obtained via liquid chromatography-mass spectrometry (LC-MS). Observed changes in metabolite abundance by treatment group correlated with the amount of time it took for samples to freeze or thaw. Thus, snap-freezing with LN2 and quick-thawing with water led to minimal changes in metabolite levels. Conversely, samples frozen at -20 °C exhibited the most changes in metabolite levels, likely because freezing required about 4 h, versus freezing instantaneously in LN2. Overall, our results show that plasma samples subjected to up to 10 cycles of LN2 snap-freezing with room temperature water quick-thawing exhibit remarkable metabolomic stability.
    Keywords:  LC–MS; freeze–thaw; metabolite stability; metabolomics; plasma
  24. Cancers (Basel). 2022 Nov 19. pii: 5691. [Epub ahead of print]14(22):
      Rapid tumor growth requires elevated biosynthetic activity, supported by metabolic rewiring occurring both intrinsically in cancer cells and extrinsically in the cancer host. The Warburg effect is one such example, burning glucose to produce a continuous flux of biomass substrates in cancer cells at the cost of energy wasting metabolic cycles in the host to maintain stable glycemia. Amino acid (AA) metabolism is profoundly altered in cancer cells, which use AAs for energy production and for supporting cell proliferation. The peculiarities in cancer AA metabolism allow the identification of specific vulnerabilities as targets of anti-cancer treatments. In the current review, specific approaches targeting AAs in terms of either deprivation or supplementation are discussed. Although based on opposed strategies, both show, in vitro and in vivo, positive effects. Any AA-targeted intervention will inevitably impact the cancer host, who frequently already has cachexia. Cancer cachexia is a wasting syndrome, also due to malnutrition, that compromises the effectiveness of anti-cancer drugs and eventually causes the patient's death. AA deprivation may exacerbate malnutrition and cachexia, while AA supplementation may improve the nutritional status, counteract cachexia, and predispose the patient to a more effective anti-cancer treatment. Here is provided an attempt to describe the AA-based therapeutic approaches that integrate currently distant points of view on cancer-centered and host-centered research, providing a glimpse of several potential investigations that approach cachexia as a unique cancer disease.
    Keywords:  amino acid; cachexia; cancer metabolism; nutrition; supplement