bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2023‒08‒06
twenty-one papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh
  1. Nutritional lipidomics for the characterization of lipids in food.
  2. Tracking the Metabolic Fate of Exogenous Arachidonic Acid in Ferroptosis Using Dual-Isotope Labeling Lipidomics.
  3. Dynamic Data-Independent Acquisition Mass Spectrometry with Real-Time Retrospective Alignment.
  4. Intratumoral lipdi metabolic reprogramming as a pro-tumoral regulator in the tumor milieu.
  5. AlphaPeptStats: an open-source Python package for automated and scalable statistical analysis of mass spectrometry-based proteomics.
  6. Development of novel isobaric tags enables accurate and sensitive multiplexed proteomics using complementary ions.
  7. Customized Surface Design to Increase Throughput of Ambient Ionization Mass Spectrometry Lipidomics.
  8. DPHL v.2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins.
  9. Lipid Metabolic Regulatory Crosstalk Between Cancer Cells and Tumor-Associated Macrophages.
  10. Signal Response Evaluation Applied to Untargeted Mass Spectrometry Data to Improve Data Interpretability.
  11. Role of exosomes in transferring chemoresistance through modulation of cancer glycolytic cell metabolism.
  12. MultiplexMS: A Mass Spectrometry-Based Multiplexing Strategy for Ultra-High-Throughput Analysis of Complex Mixtures.
  13. The regulation of amino acid metabolism in tumor cell death: from the perspective of physiological functions.
  14. A comparison of hydrophilic interaction liquid chromatography and capillary electrophoresis for the metabolomics analysis of human serum.
  15. Identification of N-degrons and N-recognins using peptide pull-downs combined with quantitative mass spectrometry.
  16. The expanding organelle lipidomes: current knowledge and challenges.
  17. IsoMatchMS: Open-Source Software for Automated Annotation and Visualization of High Resolution MALDI-MS Spectra.
  18. Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics.
  19. Inosine enhances tumor mitochondrial respiration by inducing Rag GTPases and nascent protein synthesis under nutrient starvation.
  20. Aziridination-Assisted Mass Spectrometry of Nonpolar Sterol Lipids with Isomeric Resolution.
  21. Targeted proteomics data interpretation with DeepMRM.
  1. Adv Food Nutr Res. 2023 ;pii: S1043-4526(22)00093-6. [Epub ahead of print]105 97-172
    Nutritional lipidomics for the characterization of lipids in food.
    Carlos Calderón, Michael Lämmerhofer.
      Lipids represent one out of three major macronutrient classes in the human diet. It is estimated to account for about 15-20% of the total dietary intake. Triacylglycerides comprise the majority of them, estimated 90-95%. Other lipid classes include free fatty acids, phospholipids, cholesterol, and plant sterols as minor components. Various methods are used for the characterization of nutritional lipids, however, lipidomics approaches become increasingly attractive for this purpose due to their wide coverage, comprehensiveness and holistic view on composition. In this chapter, analytical methodologies and workflows utilized for lipidomics profiling of food samples are outlined with focus on mass spectrometry-based assays. The chapter describes common lipid extraction protocols, the distinct instrumental mass-spectrometry based analytical platforms for data acquisition, chromatographic and ion-mobility spectrometry methods for lipid separation, briefly mentions alternative methods such as gas chromatography for fatty acid profiling and mass spectrometry imaging. Critical issues of important steps of lipidomics workflows such as structural annotation and identification, quantification and quality assurance are discussed as well. Applications reported over the period of the last 5years are summarized covering the discovery of new lipids in foodstuff, differential profiling approaches for comparing samples from different origin, species, varieties, cultivars and breeds, and for food processing quality control. Lipidomics as a powerful tool for personalized nutrition and nutritional intervention studies is briefly discussed as well. It is expected that this field is significantly growing in the near future and this chapter gives a short insight into the power of nutritional lipidomics approaches.
    Keywords:  Adulteration; Authentication; Differential lipidomics; Foodomics; High-resolution mass spectrometry; Lipid class separation; Lipid species separation; Nutritional intervention studies; Personalized nutrition; Shotgun lipidomics; Targeted lipidomics; UHPLC-MS/MS; Untargeted lipidomics
    DOI:  https://doi.org/10.1016/bs.afnr.2022.12.002
  2. J Am Soc Mass Spectrom. 2023 Jul 31.
    Tracking the Metabolic Fate of Exogenous Arachidonic Acid in Ferroptosis Using Dual-Isotope Labeling Lipidomics.
    Noelle Reimers, Quynh Do, Rutan Zhang, Angela Guo, Ryan Ostrander, Alyson Shoji, Chau Vuong, Libin Xu.
      Lipid metabolism is implicated in a variety of diseases, including cancer, cell death, and inflammation, but lipidomics has proven to be challenging due to the vast structural diversity over a narrow range of mass and polarity of lipids. Isotope labeling is often used in metabolomics studies to follow the metabolism of exogenously added labeled compounds because they can be differentiated from endogenous compounds by the mass shift associated with the label. The application of isotope labeling to lipidomics has also been explored as a method to track the metabolism of lipids in various disease states. However, it can be difficult to differentiate a single isotopically labeled lipid from the rest of the lipidome due to the variety of endogenous lipids present over the same mass range. Here we report the development of a dual-isotope deuterium labeling method to track the metabolic fate of exogenous polyunsaturated fatty acids, e.g., arachidonic acid, in the context of ferroptosis using hydrophilic interaction-ion mobility-mass spectrometry (HILIC-IM-MS). Ferroptosis is a type of cell death that is dependent on lipid peroxidation. The use of two isotope labels rather than one enables the identification of labeled species by a signature doublet peak in the resulting mass spectra. A Python-based software, D-Tracer, was developed to efficiently extract metabolites with dual-isotope labels. The labeled species were then identified with LiPydomics based on their retention times, collision cross section, and m/z values. Changes in exogenous AA incorporation in the absence and presence of a ferroptosis inducer were elucidated.
    DOI:  https://doi.org/10.1021/jasms.3c00181
  3. Anal Chem. 2023 Aug 01.
    Dynamic Data-Independent Acquisition Mass Spectrometry with Real-Time Retrospective Alignment.
    Lilian R Heil, Philip M Remes, Jesse D Canterbury, Ping Yip, William D Barshop, Christine C Wu, Michael J MacCoss.
      Data-independent acquisition (DIA) mass spectrometry has grown in popularity in recent years, because of the reproducibility and quantitative rigor of a systematic tandem mass spectrometry (MS/MS) sampling method. However, traditional DIA methods may spend valuable instrument time acquiring MS/MS spectra with no usable information in them, affecting sensitivity and quantitative performance. We developed a DIA strategy that dynamically adjusts the MS/MS windows during the chromatographic separation. The method focuses MS/MS acquisition on the most relevant mass range at each point in time─increasing the quantitative sensitivity by increasing the time spent on each DIA window. We demonstrate an improved lower limit of quantification, on average, without sacrificing the number of peptides detected.
    DOI:  https://doi.org/10.1021/acs.analchem.3c00903
  4. Biochim Biophys Acta Rev Cancer. 2023 Aug 02. pii: S0304-419X(23)00111-7. [Epub ahead of print] 188962
    Intratumoral lipdi metabolic reprogramming as a pro-tumoral regulator in the tumor milieu.
    Runtian Wang, Qin Hu, Yueyao Wu, Nan Guan, Xin Han, Xiaoxiang Guan.
      Reprogramming of the tumor microenvironment (TME) is a hallmark of cancer. Metabolic reprogramming is a vital approach to sustaining the energy supply in the TME. This alteration exists in both cancer cells and TME cells, collectively establishing an immunotolerant niche to facilitate tumor progression. Limited resources lead to metabolic competition and hinder the biological functions of anti-tumoral immunity. Reprogramming of lipid metabolism and tumor progression is closely related to each other. Due to the complexity of fatty acid (FA) types and the lack of an effective approach for detection, the mechanisms and effects of FA metabolic reprogramming have been unclear. Herein, we review FA metabolism in the tumor milieu, summarize how FA metabolic reprogramming influences antitumor immune response, suggest the mechanisms by which FAs affect immunotherapy against cancer, and discuss the potential of FA metabolism-based drugs in cancer treatment.
    Keywords:  Fatty acid; Metabolic reprogramming; Metastasis; Statin; Tumorigenesis; cancer progression
    DOI:  https://doi.org/10.1016/j.bbcan.2023.188962
  5. Bioinformatics. 2023 Aug 01. pii: btad461. [Epub ahead of print]
    AlphaPeptStats: an open-source Python package for automated and scalable statistical analysis of mass spectrometry-based proteomics.
    Elena Krismer, Isabell Bludau, Maximilian T Strauss, Matthias Mann.
      SUMMARY: The widespread application of mass spectrometry (MS)-based proteomics in biomedical research increasingly requires robust, transparent and streamlined solutions to extract statistically reliable insights. We have designed and implemented AlphaPeptStats, an inclusive python package with currently with broad functionalities for normalization, imputation, visualization, and statistical analysis of label-free proteomics data. It modularly builds on the established stack of Python scientific libraries, and is accompanied by a rigorous testing framework with 98% test coverage. It imports the output of a range of popular search engines. Data can be filtered and normalized according to user specifications. At its heart, AlphaPeptStats provides a wide range of robust statistical algorithms such as t-tests, ANOVA, PCA, hierarchical clustering and multiple covariate analysis-all in an automatable manner. Data visualization capabilities include heat maps, volcano plots, scatter plots in publication-ready format. AlphaPeptStats advances proteomic research through its robust tools that enable researchers to manually or automatically explore complex datasets to identify interesting patterns and outliers.AVAILABILITY: AlphaPeptStats is implemented in Python and part of the AlphaPept framework. It is released under a permissive Apache license. The source code and one-click installers are freely available and on GitHub at https://github.com/MannLabs/alphapeptstats.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btad461
  6. Anal Bioanal Chem. 2023 Aug 02.
    Development of novel isobaric tags enables accurate and sensitive multiplexed proteomics using complementary ions.
    Miyang Li, Min Ma, Lingjun Li.
      High-throughput quantitative analysis of the cells' proteomes across multiple conditions such as various perturbations and different time points is essential for gaining insights into treatment-induced biological responses or disease pathological states. The advancements in mass spectrometry instrumentation and isobaric labeling methods provided useful tools to help address such demands. However, the current widely adopted isobaric labeling methods such as tandem mass tag (TMT) and isobaric tags for relative and absolute quantitation (iTRAQ) are based on low-mass reporter ions, which are indistinguishable among different peptide analytes, to achieve relative quantification. Therefore, these methods intrinsically suffer from severe ratio distortion when analyzing complex samples due to peptide coelution and cofragmentation. Here, we developed a novel set of isobaric tags named dimethylated leucine complementary ion (DiLeuC) and relied on complementary ions for relative quantification, in which the complementary ions are the remanent peptide segments after fragmentation in the high-mass range. Since those residual peptide fragments are precursor-specific, they retain the relative abundance information in an interference-free manner even in a complex matrix environment. The quantification accuracy of our method was validated in a two-proteome model where the yeast proteome was spiked with a strong background human proteome as interference. In addition, we also applied this strategy to single-cell proteome analysis, demonstrating its potential utility for sensitive high-throughput quantitative proteomics.
    Keywords:  Complementary ion; DiLeuC; Isobaric tags; Mass spectrometry; Quantitative proteomics
    DOI:  https://doi.org/10.1007/s00216-023-04877-3
  7. J Am Soc Mass Spectrom. 2023 Aug 04.
    Customized Surface Design to Increase Throughput of Ambient Ionization Mass Spectrometry Lipidomics.
    Christopher C Goudarzi, Ashley G Woolfork, Arjun Sengupta, Aalim M Weljie.
      Increased access to cheap and rapid mass spectrometry testing of biofluids is desirable for the analysis of disorders and diseases that may be linked to alterations in metabolite or lipid levels. The objective of this study is to establish an easily customized high-throughput workflow for the analysis of biological samples using desorption electrospray ionization-mass spectrometry (DESI-MS). The guiding principles of this workflow are the use of low-cost, open-source, and readily accessible materials with high-throughput and reproducibility. The design consists of 3 steps: (1) PARAFILM surface customization of size, shape, and depth of features on PARAFILM via 3D printed molds; (2) sample spotting via high-throughput robotics using the relatively inexpensive and open-source Opentrons platform to reduce variability and increase reliability of sample spotting; and (3) an open-source point-and-click graphical user interface (MSI.EAGLE) for data analysis via the R statistical language building on the Cardinal package. Here we describe this workflow and test optimal surface ionization characteristics by comparison of serum extracts spotted on PARAFILM and on PTFE (porous and nonporous). Untargeted analysis across three surfaces suggests that they are all suitable for ionization of a wide range of metabolites and lipids, with 3983 m/z features detected. Differential analysis of polar vs nonpolar serum extracts suggests that ∼80% of ions are desorbed preferentially from different surfaces. PARAFILM is less impacted by the interference of background ions derived from the surface. The developed system allows for a wide range of researchers to access custom surface design workflows and high-throughput analyses in a highly cost-effective manner.
    DOI:  https://doi.org/10.1021/jasms.3c00062
  8. Patterns (N Y). 2023 Jul 14. 4(7): 100792
    DPHL v.2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins.
    Zhangzhi Xue, Tiansheng Zhu, Fangfei Zhang, Cheng Zhang, Nan Xiang, Liujia Qian, Xiao Yi, Yaoting Sun, Wei Liu, Xue Cai, Linyan Wang, Xizhe Dai, Liang Yue, Lu Li, Thang V Pham, Sander R Piersma, Qi Xiao, Meng Luo, Cong Lu, Jiang Zhu, Yongfu Zhao, Guangzhi Wang, Junhong Xiao, Tong Liu, Zhiyu Liu, Yi He, Qijun Wu, Tingting Gong, Jianqin Zhu, Zhiguo Zheng, Juan Ye, Yan Li, Connie R Jimenez, Jun A, Tiannan Guo.
      A comprehensive pan-human spectral library is critical for biomarker discovery using mass spectrometry (MS)-based proteomics. DPHL v.1, a previous pan-human library built from 1,096 data-dependent acquisition (DDA) MS data of 16 human tissue types, allows quantifying of 10,943 proteins. Here, we generated DPHL v.2 from 1,608 DDA-MS data. The data included 586 DDA-MS data acquired from 18 tissue types, while 1,022 files were derived from DPHL v.1. DPHL v.2 thus comprises data from 24 sample types, including several cancer types (lung, breast, kidney, and prostate cancer, among others). We generated four variants of DPHL v.2 to include semi-tryptic peptides and protein isoforms. DPHL v.2 was then applied to two colorectal cancer cohorts. The numbers of identified and significantly dysregulated proteins increased by at least 21.7% and 14.2%, respectively, compared with DPHL v.1. Our findings show that the increased human proteome coverage of DPHL v.2 provides larger pools of potential protein biomarkers.
    DOI:  https://doi.org/10.1016/j.patter.2023.100792
  9. DNA Cell Biol. 2023 Aug 03.
    Lipid Metabolic Regulatory Crosstalk Between Cancer Cells and Tumor-Associated Macrophages.
    Shu Liu, Ying Ying Shen, Li Yang Yin, Jianghua Liu, Xuyu Zu.
      In the tumor microenvironment, tumor-associated macrophages (TAMs) are one of the most abundant cell populations, playing key roles in tumorigenesis, chemoresistance, immune evasion, and metastasis. There is an important interaction between TAMs and cancer cells: on the one hand, tumors control the function of infiltrating macrophages, contributing to reprogramming of TAMs, and on the other hand, TAMs affect the growth of cancer cells. This review focuses on lipid metabolism changes in the complex relationship between cancer cells and TAMs. We discuss how lipid metabolism in cancer cells affects macrophage phenotypic and metabolic changes and, subsequently, how altered lipid metabolism of TAMs influences tumor progression. Identifying the metabolic changes that influence the complex interaction between tumor cells and TAMs is also an important step in exploring new therapeutic approaches that target metabolic reprogramming of immune cells to enhance their tumoricidal potential and bypass therapy resistance. Our work may provide new targets for antitumor therapies.
    Keywords:  cancer cells; lipid metabolism; targeting therapy; tumor-associated macrophages
    DOI:  https://doi.org/10.1089/dna.2023.0071
  10. J Am Soc Mass Spectrom. 2023 Jul 31.
    Signal Response Evaluation Applied to Untargeted Mass Spectrometry Data to Improve Data Interpretability.
    Kirsten E Overdahl, Justin B Collier, Anton M Jetten, Alan K Jarmusch.
      Feature finding is a common way to process untargeted mass spectrometry (MS) data to obtain a list of chemicals present in a sample. Most feature finding algorithms naïvely search for patterns of unique descriptors (e.g., m/z, retention time, and mobility) and provide a list of unannotated features. There is a need for solutions in processing untargeted MS data, independent of chemical or origin, to assess features based on measurement quality with the aim of improving interpretation. Here, we report the signal response evaluation as a method by which to assess the individual features observed in untargeted MS data. The basis of this method is the ubiquitous relationship between the amount and response in all MS measurements. Three different metrics with user-defined parameters can be used to assess the monotonic or linear relationship of each feature in a dilution series or multiple injection volumes. We demonstrate this approach in metabolomics data obtained from a uniform biological matrix (NIST SRM 1950) and a variable biological matrix (murine kidney tissue). The code is provided to facilitate implementation of this data processing method.
    DOI:  https://doi.org/10.1021/jasms.3c00220
  11. Cytokine Growth Factor Rev. 2023 Jul 29. pii: S1359-6101(23)00035-7. [Epub ahead of print]
    Role of exosomes in transferring chemoresistance through modulation of cancer glycolytic cell metabolism.
    Mahrou Vahabi, Annalisa Comandatore, Marika A Franczak, Ryszard T Smolenski, Godefridus J Peters, Luca Morelli, Elisa Giovannetti.
      Chemoresistance constitute a major obstacle in cancer treatment, leading to limited options and decreased patient survival. Recent studies have revealed a novel mechanism of chemoresistance acquisition: the transfer of information via exosomes, small vesicles secreted by various cells. Exosomes play a crucial role in intercellular communication by carrying proteins, nucleic acids, and metabolites, influencing cancer cell behavior and response to treatment. One crucial mechanism of resistance is cancer metabolic reprogramming, which involves alterations in the cellular metabolic pathways to support the survival and proliferation of drug-resistant cancer cells. This metabolic reprogramming often includes increased glycolysis, providing cancer cells with the necessary energy and building blocks to evade the effects of chemotherapy. Notably, exosomes have been found to transport glycolytic enzymes, as identified in proteomic profiling, leading to the reprogramming of metabolic pathways, facilitating altered glucose metabolism and increased lactate production. As a result, they profoundly impact the tumor microenvironment, promoting tumor progression, survival, immune evasion, and drug resistance.Understanding the complexities of such exosome-mediated cell-to-cell communication might open new therapeutic avenues and facilitate biomarker development in managing cancers characterized by aggressive glycolytic features. Moreover, given the intricate nature of metabolic abnormalities combining future exosome-based-targeted therapies with existing treatments like chemotherapy, immunotherapy, and targeted therapies holds promise for achieving synergistic effects to overcome resistance and improve cancer treatment outcomes.
    Keywords:  Cancer metabolism; Cell-to-cell communication; Chemoresistance; Exosomes; Glycolysis
    DOI:  https://doi.org/10.1016/j.cytogfr.2023.07.004
  12. Anal Chem. 2023 Aug 02.
    MultiplexMS: A Mass Spectrometry-Based Multiplexing Strategy for Ultra-High-Throughput Analysis of Complex Mixtures.
    Michael J J Recchia, Tim U H Baumeister, Dennis Y Liu, Roger G Linington.
      High-throughput chemical analysis of natural product mixtures lags behind developments in genome sequencing technologies and laboratory automation, leading to a disconnect between library-scale chemical and biological profiling that limits new molecule discovery. Here, we report a new orthogonal sample multiplexing strategy that can increase mass spectrometry-based profiling up to 30-fold over traditional methods. Profiled pooled samples undergo subsequent computational deconvolution to reconstruct peak lists for each sample in the set. We validated this approach using in silico experiments and demonstrated a high assignment precision (>97%) for large, pooled samples (r = 30), particularly for infrequently occurring metabolites of relevance in drug discovery applications. Requiring only 5% of the previously required MS acquisition time, this approach was repeated in a recent biological activity profiling study on 925 natural product extracts, leading to the rediscovery of all previously reported bioactive metabolites. This new method is compatible with MS data from any instrument vendor and is supported by an open-source software package: https://github.com/liningtonlab/MultiplexMS.
    DOI:  https://doi.org/10.1021/acs.analchem.3c00939
  13. Apoptosis. 2023 Jul 31.
    The regulation of amino acid metabolism in tumor cell death: from the perspective of physiological functions.
    Jin Wang, Hongying Wang, Min Gao, Yilei Zhang, Lei Zhang, Dongsheng Huang, Kangsheng Tu, Qiuran Xu.
      Amino acids (AAs) are crucial molecules for the synthesis of mammalian proteins as well as a source of energy and redox equilibrium maintenance. The development of tumors also requires AAs as nutrients. Increased AAs metabolism is frequently seen in tumor cells to produce enough biomass, energy, and reduction agents. However, increased AA demand may result in auxotrophy in some cancer cells, highlighting the vulnerabilities of cancers and exposing the AA metabolism as a potential target for cancer therapy. The dynamic balance of cell survival and death is required for cellular homeostasis, growth, and development. Malignant cells manage to avoid cell death through a range of mechanisms, such as developing an addiction to amino acids through metabolic adaptation. In order to offer some guidance for AA-targeted cancer therapy, we have outlined the function of AA metabolism in tumor progression, the modalities of cell death, and the regulation of AA metabolism on tumor cell death in this review.
    Keywords:  Amino acids; Cancer; Cancer therapy; Cell death modality; Metabolic reprogramming
    DOI:  https://doi.org/10.1007/s10495-023-01875-9
  14. J Chromatogr A. 2023 Jul 22. pii: S0021-9673(23)00464-8. [Epub ahead of print]1706 464239
    A comparison of hydrophilic interaction liquid chromatography and capillary electrophoresis for the metabolomics analysis of human serum.
    Luca Narduzzi, María Del Mar Delgado-Povedano, Francisco J Lara, Bruno Le Bizec, Ana María García-Campaña, Gaud Dervilly, Maykel Hernández-Mesa.
      Cationic, anionic, zwitterionic and, partially polar metabolites are very important constituents of blood serum. Several of these metabolites underpin the core metabolism of cells (e.g., Krebs cycle, urea cycle, proteins synthesis, etc.), while others might be considered ancillary but still important to grasp the status of any organism through blood serum analysis. Due to its wide chemical diversity, modern metabolomics analysis of serum is still struggling to provide a complete and comprehensive picture of the polar metabolome, due to the limitations of each specific analytical method. In this study, two metabolomics-based analytical methods using the most successful techniques for polar compounds separation in human serum samples, namely hydrophilic interaction liquid chromatography (HILIC) and capillary electrophoresis (CE), are evaluated, both coupled to a high-resolution time-of-flight mass spectrometer via electrospray ionization (ESI-Q-TOF-MS). The performance of the two methods have been compared using five terms of comparison, three of which are specific to metabolomics, such as (1) compounds' detectability (2) Pezzatti score (Pezzatti et al. 2018), (3) intra-day precision (repeatability), (4) ease of automatic analysis of the data (through a common deconvolution alignment and extrapolation software, MS-DIAL, and (5) time & cost analysis. From this study, HILIC-MS proved to be a better tool for polar metabolome analysis, while CE-MS helped identify some interesting variables that gave it interest in completing metabolome coverage in metabolomics studies. Finally, in this framework, MS-DIAL demonstrates for the first time its ability to process CE data for metabolomics, although it is not designed for it.
    Keywords:  CE-MS; HILIC-MS; MS-DIAL; Migration time correction; Polar metabolomics analysis
    DOI:  https://doi.org/10.1016/j.chroma.2023.464239
  15. Methods Enzymol. 2023 ;pii: S0076-6879(23)00063-0. [Epub ahead of print]686 67-97
    Identification of N-degrons and N-recognins using peptide pull-downs combined with quantitative mass spectrometry.
    Franziska Müller, Tanja Bange.
      Regulated protein degradation controls protein levels of all short-lived proteins to ensure cellular homeostasis and also protects cells from misfolded or other abnormal proteins. The most important players in the degradation system are E3 ubiquitin ligases which recognize exposed sequence motifs, so-called degrons, of target proteins and mark them through the attachment of ubiquitin for degradation. N-terminal (Nt) sequences are extensively used as degrons (N-degrons) and all 20 amino acids are able to feed proteins in 1 of the 5 known N-degron pathways. Studies have mainly focused on characterizing systematically the role of the starting amino acid on protein stability and less on the identification of the E3 ligases involved. Recent data from our lab and literature suggest that there is an extensive interplay of N-recognins and Nt-modifying enzymes like Nt-acetyltransferases (NATs) or N-myristoyltransferases which only starts to be elucidated. It suggests that improperly modified or unexpectedly unmodified proteins become rapidly removed after synthesis ensuring protein maturation and quality control of specific subsets of proteins. Here, we describe a peptide pull-down and down-stream bioinformatics workflow conducted in the MaxQuant and Perseus computational environment to identify N-recognin candidates in an unbiased way using quantitative mass spectrometry (MS)-based proteomics. Our workflow allows the identification of N-recognin candidates for specific N-degrons, to determine their sequence specificity and it can be applied as well more general to identify binding partners of N-terminal modifications. This method paves the way to identify pathways involved in protein quality control and stability acting at the N-terminus.
    Keywords:  E3 ligase; Label-free quantification; Mass-spectrometry; N-degron; N-recognin; N-terminal acetylation; N-terminal modification; Peptide pull-down; Protein quality control and stability; Quantitative MS-based proteomics
    DOI:  https://doi.org/10.1016/bs.mie.2023.02.007
  16. Cell Mol Life Sci. 2023 Aug 02. 80(8): 237
    The expanding organelle lipidomes: current knowledge and challenges.
    Maria J Sarmento, Alicia Llorente, Toni Petan, Denis Khnykin, Iuliana Popa, Matea Nikolac Perkovic, Marcela Konjevod, Morana Jaganjac.
      Lipids in cell membranes and subcellular compartments play essential roles in numerous cellular processes, such as energy production, cell signaling and inflammation. A specific organelle lipidome is characterized by lipid synthesis and metabolism, intracellular trafficking, and lipid homeostasis in the organelle. Over the years, considerable effort has been directed to the identification of the lipid fingerprints of cellular organelles. However, these fingerprints are not fully characterized due to the large variety and structural complexity of lipids and the great variability in the abundance of different lipid species. The process becomes even more challenging when considering that the lipidome differs in health and disease contexts. This review summarizes the information available on the lipid composition of mammalian cell organelles, particularly the lipidome of the nucleus, mitochondrion, endoplasmic reticulum, Golgi apparatus, plasma membrane and organelles in the endocytic pathway. The lipid compositions of extracellular vesicles and lamellar bodies are also described. In addition, several examples of subcellular lipidome dynamics under physiological and pathological conditions are presented. Finally, challenges in mapping organelle lipidomes are discussed.
    Keywords:  Cellular organelles; Lipidomics; Lipids; Mass spectrometry; Subcellular fractionation
    DOI:  https://doi.org/10.1007/s00018-023-04889-3
  17. J Am Soc Mass Spectrom. 2023 Jul 31.
    IsoMatchMS: Open-Source Software for Automated Annotation and Visualization of High Resolution MALDI-MS Spectra.
    David J Degnan, Kevin J Zemaitis, Logan A Lewis, Lee Ann McCue, Lisa M Bramer, James M Fulcher, Dušan Veličković, Ljiljana Paša-Tolić, Mowei Zhou.
      Due to its speed, accuracy, and adaptability to various sample types, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has become a popular method to identify molecular isotope profiles from biological samples. Often MALDI-MS data do not include tandem MS fragmentation data, and thus the identification of compounds in samples requires external databases so that the accurate mass of detected signals can be matched to known molecular compounds. Most relevant MALDI-MS software tools developed to confirm compound identifications are focused on small molecules (e.g., metabolites, lipids) and cannot be easily adapted to protein data due to their more complex isotopic distributions. Here, we present an R package called IsoMatchMS for the automated annotation of MALDI-MS data for multiple datatypes: intact proteins, peptides, and glycans. This tool accepts already derived molecular formulas or, for proteomics applications, can derive molecular formulas from a list of input peptides or proteins including proteins with post-translational modifications. Visualization of all matched isotopic profiles is provided in a highly accessible HTML format called a trelliscope display, which allows users to filter and sort by several parameters such as match scores and the number of peaks matched. IsoMatchMS simplifies the annotation and visualization of MALDI-MS data for downstream analyses.
    Keywords:  MALDI-MS; R package; isotope profile; mass spectrometry; trelliscope
    DOI:  https://doi.org/10.1021/jasms.3c00180
  18. J Proteome Res. 2023 Aug 02.
    Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics.
    Christophe Vanderaa, Laurent Gatto.
      Missing values are a notable challenge when analyzing mass spectrometry-based proteomics data. While the field is still actively debating the best practices, the challenge increased with the emergence of mass spectrometry-based single-cell proteomics and the dramatic increase in missing values. A popular approach to deal with missing values is to perform imputation. Imputation has several drawbacks for which alternatives exist, but currently, imputation is still a practical solution widely adopted in single-cell proteomics data analysis. This perspective discusses the advantages and drawbacks of imputation. We also highlight 5 main challenges linked to missing value management in single-cell proteomics. Future developments should aim to solve these challenges, whether it is through imputation or data modeling. The perspective concludes with recommendations for reporting missing values, for reporting methods that deal with missing values, and for proper encoding of missing values.
    Keywords:  RNA-Seq; data analysis; imputation; mass spectrometry; missing values; proteomics; reproducible research; single-cell
    DOI:  https://doi.org/10.1021/acs.jproteome.3c00227
  19. Cell Death Dis. 2023 Aug 02. 14(8): 492
    Inosine enhances tumor mitochondrial respiration by inducing Rag GTPases and nascent protein synthesis under nutrient starvation.
    Mei-Xin Li, Xiao-Ting Wu, Wen-Qiang Jing, Wen-Kui Hou, Sheng Hu, Wei Yan.
      Metabolic heterogeneity of tumor microenvironment (TME) is a hallmark of cancer and a big barrier to cancer treatment. Cancer cells display diverse capacities to utilize alternative carbon sources, including nucleotides, under poor nutrient circumstances. However, whether and how purine, especially inosine, regulates mitochondrial metabolism to buffer nutrient starvation has not been well-defined yet. Here, we identify the induction of 5'-nucleotidase, cytosolic II (NT5C2) gene expression promotes inosine accumulation and maintains cancer cell survival in the nutrient-poor region. Inosine elevation further induces Rag GTPases abundance and mTORC1 signaling pathway by enhancing transcription factor SP1 level in the starved tumor. Besides, inosine supplementary stimulates the synthesis of nascent TCA cycle enzymes, including citrate synthesis (CS) and aconitase 1 (ACO1), to further enhance oxidative phosphorylation of breast cancer cells under glucose starvation, leading to the accumulation of iso-citric acid. Inhibition of the CS activity or knockdown of ACO1 blocks the rescue effect of inosine on cancer survival under starvation. Collectively, our finding highlights the vital signal role of inosine linking mitochondrial respiration and buffering starvation, beyond serving as direct energy carriers or building blocks for genetic code in TME, shedding light on future cancer treatment by targeting inosine metabolism.
    DOI:  https://doi.org/10.1038/s41419-023-06017-2
  20. J Am Soc Mass Spectrom. 2023 Jul 31.
    Aziridination-Assisted Mass Spectrometry of Nonpolar Sterol Lipids with Isomeric Resolution.
    Erin Hirtzel, Madison Edwards, Dallas Freitas, Ziying Liu, Fen Wang, Xin Yan.
      Characterization of nonpolar lipids is crucial due to their essential biological functions and ability to exist in various isomeric forms. In this study, we introduce the N-H aziridination method to target carbon-carbon double bonds (C═C bonds) in nonpolar sterol lipids. The resulting fragments are readily dissociated upon collision-induced dissociation, generating specific fragment ions for C═C bond position determination and fingerprint fragments for backbone characterization. This method significantly enhances lipid ionization efficiency, thereby improving the sensitivity and accuracy of nonpolar lipid analysis. We demonstrated that aziridination of sterols leads to distinctive fragmentation pathways for chain and ring C═C bonds, enabling the identification of sterol isomers such as desmosterol and 7-dehydrocholesterol. Furthermore, aziridination can assist in identifying the sterol backbone by providing fingerprint tandem mass spectra. We also demonstrated the quantitative capacity of this approach with a limit of detection of 10 nM in the solvent mixture of methanol and water. To test the feasibility of this method in complex biological samples, we used mouse prostate cancerous tissues and found significant differences in nonpolar lipid profiles between healthy and cancerous samples. The high efficiency and specificity of aziridination-assisted mass spectrometric analysis, as well as its quantitative analysis ability, make it highly suitable for broad applications in nonpolar lipid research.
    DOI:  https://doi.org/10.1021/jasms.3c00161
  21. Cell Rep Methods. 2023 Jul 24. 3(7): 100521
    Targeted proteomics data interpretation with DeepMRM.
    Jungkap Park, Christopher Wilkins, Dmitry Avtonomov, Jiwon Hong, Seunghoon Back, Hokeun Kim, Nicholas Shulman, Brendan X MacLean, Sang-Won Lee, Sangtae Kim.
      Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool.
    Keywords:  Skyline; machine learning; multiple reaction monitoring; object detection; peak detection; quality control; quantification; selected reaction monitoring; targeted proteomics
    DOI:  https://doi.org/10.1016/j.crmeth.2023.100521
This page is being maintained by Thomas Krichel. It was last updated on 2023‒08‒15 at 17:50.