bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2023‒03‒26
24 papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh
  1. Rethinking glutamine metabolism and the regulation of glutamine addiction by oncogenes in cancer.
  2. Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics.
  3. Role and therapeutic targeting of glutamine metabolism in non‑small cell lung cancer (Review).
  4. Metabolic Pathway of Monounsaturated Lipids Revealed by In-Depth Structural Lipidomics by Mass Spectrometry.
  5. Identification of bile acid-CoA:amino acid N-acyltransferase as the hepatic N-acyl taurine synthase for polyunsaturated fatty acids.
  6. Global detection of human variants and isoforms by deep proteome sequencing.
  7. Large-Scale metabolomics: Predicting biological age using 10,133 routine untargeted LC-MS measurements.
  8. Sample preparation for structural mass spectrometry via polyacrylamide gel electrophoresis.
  9. The dark side of lipid metabolism in prostate and renal carcinoma: novel insights into molecular diagnostic and biomarker discovery.
  10. To metabolomics and beyond: a technological portfolio to investigate cancer metabolism.
  11. Multiplex substrate profiling by mass spectrometry for proteases.
  12. Principles of phosphoproteomics and applications in cancer research.
  13. prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis.
  14. High-resolution separation of bioisomers using ion cloud profiling.
  15. Mass spectrometry quantitation of immunosuppressive drugs in clinical specimens using online solid-phase extraction and accurate-mass full scan-single ion monitoring.
  16. Analytical Toolbox to Unlock the Diversity of Oxidized Lipids.
  17. Reprogramming of tissue metabolism during cancer metastasis.
  18. Defects in lipid homeostasis reflect the function of TANGO2 in phospholipid and neutral lipid metabolism.
  19. Highly-sensitive label-free deep profiling of N-glycans released from biomedically-relevant samples.
  20. Comprehensive study of metabolic changes induced by a ketogenic diet therapy using GC/MS- and LC/MS-based metabolomics.
  21. Pickaxe: a Python library for the prediction of novel metabolic reactions.
  22. All-in-One digital microfluidics pipeline for proteomic sample preparation and analysis.
  23. Bayesian kinetic modeling for tracer-based metabolomic data.
  24. Non-essential amino acid availability influences proteostasis and breast cancer cell survival during proteotoxic stress.
  1. Front Oncol. 2023 ;13 1143798
    Rethinking glutamine metabolism and the regulation of glutamine addiction by oncogenes in cancer.
    Rui Ni, Ziwei Li, Li Li, Dan Peng, Yue Ming, Lin Li, Yao Liu.
      Glutamine, the most abundant non-essential amino acid in human blood, is crucial for cancer cell growth and cancer progression. Glutamine mainly functions as a carbon and nitrogen source for biosynthesis, energy metabolism, and redox homeostasis maintenance in cancer cells. Dysregulated glutamine metabolism is a notable metabolic characteristic of cancer cells. Some carcinogen-driven cancers exhibit a marked dependence on glutamine, also known as glutamine addiction, which has rendered the glutamine metabolic pathway a breakpoint in cancer therapeutics. However, some cancer cells can adapt to the glutamine unavailability by reprogramming metabolism, thus limiting the success of this therapeutic approach. Given the complexity of metabolic networks and the limited impact of inhibiting glutamine metabolism alone, the combination of glutamine metabolism inhibition and other therapeutic methods may outperform corresponding monotherapies in the treatment of cancers. This review summarizes the uptake, transport, and metabolic characteristics of glutamine, as well as the regulation of glutamine dependence by some important oncogenes in various cancers to emphasize the therapeutic potential of targeting glutamine metabolism. Furthermore, we discuss a glutamine metabolic pathway, the glutaminase II pathway, that has been substantially overlooked. Finally, we discuss the applicability of polytherapeutic strategies targeting glutamine metabolism to provide a new perspective on cancer therapeutics.
    Keywords:  cancer therapy; glutamine addiction; glutamine metabolism; metabolism inhibiton; oncogene
    DOI:  https://doi.org/10.3389/fonc.2023.1143798
  2. Front Mol Biosci. 2023 ;10 1130781
    Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics.
    Joe Wandy, Ross McBride, Simon Rogers, Nikolaos Terzis, Stefan Weidt, Justin J J van der Hooft, Kevin Bryson, Rónán Daly, Vinny Davies.
      Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their MS/MS spectral annotation performance in untargeted settings due to the lack of ground truth and the costs involved in running a large number of acquisitions. Here, we present a systematic in silico comparison of these two acquisition methods in untargeted metabolomics by extending our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. The versatility of the Virtual Metabolomics Mass Spectrometer (ViMMS) framework in simulating different parameters of both Data-Dependent and Data-Independent Acquisition (DDA and DIA) modes is a key advantage of this work. Researchers can easily explore and compare the performance of different acquisition methods within the ViMMS framework, without the need for expensive and time-consuming experiments with real experimental data. By identifying the strengths and limitations of each acquisition method, researchers can optimize their choice and obtain more accurate and robust results. Furthermore, the ability to simulate and validate results using the ViMMS framework can save significant time and resources, as it eliminates the need for numerous experiments. This work not only provides valuable insights into the performance of DDA and DIA, but it also opens the door for further advancements in LC-MS/MS data acquisition methods.
    Keywords:  data independent acquisition; data-dependent acquisition; digital twin; liquid chromatography tandem mass spectrometry; metabolomics
    DOI:  https://doi.org/10.3389/fmolb.2023.1130781
  3. Oncol Lett. 2023 Apr;25(4): 159
    Role and therapeutic targeting of glutamine metabolism in non‑small cell lung cancer (Review).
    Lei Zhou, Qi Zhang, Qing Zhu, Yuan Zhan, Yong Li, Xuan Huang.
      The Warburg effect indicates that cancer cells survive through glycolysis under aerobic conditions; as such, the topic of cancer metabolism has aroused interest. It is requisite to further explore cancer metabolism, as it helps to simultaneously explain the process of carcinogenesis and guide therapy. The flexible metabolism of cancer cells, which is the result of metabolic reprogramming, can meet the basic needs of cells, even in a nutrition-deficient environment. Glutamine is the most abundant non-essential amino acid in the circulation, and along with glucose, comprise the two basic nutrients of cancer cell metabolism. Glutamine is crucial in non-small cell lung cancer (NSCLC) cells and serves an important role in supporting cell growth, activating signal transduction and maintaining redox homeostasis. In this perspective, the present review aims to provide a new therapeutic strategy of NSCLC through inhibiting the metabolism of glutamine. This review not only summarizes the significance of glutamine metabolism in NSCLC cells, but also enumerates traditional glutamine inhibitors along with new targets. It also puts forward the concept of combination therapy and patient stratification with the aim of comprehensively showing the effect and prospect of targeted glutamine metabolism in NSCLC therapy. This review was completed by searching for keywords including 'glutamine', 'NSCLC' and 'therapy' on PubMed, and screening out articles.
    Keywords:  Warburg effect; glutamine metabolism; metabolism inhibitor; non-small cell lung cancer; therapeutic strategies
    DOI:  https://doi.org/10.3892/ol.2023.13745
  4. Research (Wash D C). 2023 ;6 0087
    Metabolic Pathway of Monounsaturated Lipids Revealed by In-Depth Structural Lipidomics by Mass Spectrometry.
    Simin Cheng, Donghui Zhang, Jiaxin Feng, Qingyuan Hu, Aolei Tan, Zhuoning Xie, Qinhua Chen, Huimin Huang, Ying Wei, Zheng Ouyang, Xiaoxiao Ma.
      The study of lipid metabolism relies on the characterization of the lipidome, which is quite complex due to the structure variations of the lipid species. New analytical tools have been developed recently for characterizing fine structures of lipids, with C=C location identification as one of the major improvements. In this study, we studied the lipid metabolism reprograming by analyzing glycerol phospholipid compositions in breast cancer cell lines with structural specification extended to the C=C location level. Inhibition of the lipid desaturase, stearoyl-CoA desaturase 1, increased the proportion of n-10 isomers that are produced via an alternative fatty acid desaturase 2 pathway. However, there were different variations of the ratio of n-9/n-7 isomers in C18:1-containing glycerol phospholipids after stearoyl-CoA desaturase 1 inhibition, showing increased tendency in MCF-7 cells, MDA-MB-468 cells, and BT-474 cells, but decreased tendency in MDA-MB-231 cells. No consistent change of the ratio of n-9/n-7 isomers was observed in SK-BR-3 cells. This type of heterogeneity in reprogrammed lipid metabolism can be rationalized by considering both lipid desaturation and fatty acid oxidation, highlighting the critical roles of comprehensive lipid analysis in both fundamental and biomedical applications.
    DOI:  https://doi.org/10.34133/research.0087
  5. J Lipid Res. 2023 Mar 21. pii: S0022-2275(23)00034-2. [Epub ahead of print] 100361
    Identification of bile acid-CoA:amino acid N-acyltransferase as the hepatic N-acyl taurine synthase for polyunsaturated fatty acids.
    Samuel A J Trammell, Luke F Gamon, Kamil Gotfryd, Katja Thorøe Michler, Bandar D Alrehaili, Iben Rix, Filip K Knop, Pontus Gourdon, Yoon-Kwang Lee, Michael J Davies, Matthew P Gillum, Trisha J Grevengoed.
      N-acyl taurines (NATs) are bioactive lipids with emerging roles in glucose homeostasis and lipid metabolism. The acyl-chains of hepatic and biliary NATs are enriched in poly-unsaturated fatty acids (PUFAs). Dietary supplementation with a class of PUFAs, the omega-3 fatty acids, increases their cognate NATs in mice and humans. However, the synthesis pathway of the PUFA-containing NATs remains undiscovered. Here, we report that human livers synthesize NATs and that the acyl-chain preference is similar in murine liver homogenates. In the mouse, we found that hepatic NAT synthase activity localizes to the peroxisome and depends upon an active-site cysteine. Using unbiased metabolomics and proteomics, we identified bile acid-CoA:amino acid N-acyltransferase (BAAT) as the likely hepatic NAT synthase in vitro. Subsequently, we confirmed that BAAT knockout livers lack up to 90% of NAT synthase activity and that biliary PUFA-containing NATs are significantly reduced compared to wildtype. In conclusion, we identified the in vivo PUFA-NAT synthase in the murine liver and expanded the known substrates of the bile acid-conjugating enzyme, BAAT, beyond classic bile acids to the synthesis of a novel class of bioactive lipids.
    Keywords:  Bile acids and salts/biosynthesis; Bile acids and salts/metabolism, Liver; N-acyl amino acid; N-acyl taurine; Omega-3 fatty acids; fatty acid amide hydrolase; metabolomics; peroxisomes; proteomics
    DOI:  https://doi.org/10.1016/j.jlr.2023.100361
  6. Nat Biotechnol. 2023 Mar 23.
    Global detection of human variants and isoforms by deep proteome sequencing.
    Pavel Sinitcyn, Alicia L Richards, Robert J Weatheritt, Dain R Brademan, Harald Marx, Evgenia Shishkova, Jesse G Meyer, Alexander S Hebert, Michael S Westphall, Benjamin J Blencowe, Jürgen Cox, Joshua J Coon.
      An average shotgun proteomics experiment detects approximately 10,000 human proteins from a single sample. However, individual proteins are typically identified by peptide sequences representing a small fraction of their total amino acids. Hence, an average shotgun experiment fails to distinguish different protein variants and isoforms. Deeper proteome sequencing is therefore required for the global discovery of protein isoforms. Using six different human cell lines, six proteases, deep fractionation and three tandem mass spectrometry fragmentation methods, we identify a million unique peptides from 17,717 protein groups, with a median sequence coverage of approximately 80%. Direct comparison with RNA expression data provides evidence for the translation of most nonsynonymous variants. We have also hypothesized that undetected variants likely arise from mutation-induced protein instability. We further observe comparable detection rates for exon-exon junction peptides representing constitutive and alternative splicing events. Our dataset represents a resource for proteoform discovery and provides direct evidence that most frame-preserving alternatively spliced isoforms are translated.
    DOI:  https://doi.org/10.1038/s41587-023-01714-x
  7. Aging Cell. 2023 Mar 19. e13813
    Large-Scale metabolomics: Predicting biological age using 10,133 routine untargeted LC-MS measurements.
    Johan K Lassen, Tingting Wang, Kirstine L Nielsen, Jørgen B Hasselstrøm, Mogens Johannsen, Palle Villesen.
      Untargeted metabolomics is the study of all detectable small molecules, and in geroscience, metabolomics has shown great potential to describe the biological age-a complex trait impacted by many factors. Unfortunately, the sample sizes are often insufficient to achieve sufficient power and minimize potential biases caused by, for example, demographic factors. In this study, we present the analysis of biological age in ~10,000 toxicologic routine blood measurements. The untargeted screening samples obtained from ultra-high pressure liquid chromatography-quadruple time of flight mass spectrometry (UHPLC- QTOF) cover + 300 batches and + 30 months, lack pooled quality controls, lack controlled sample collection, and has previously only been used in small-scale studies. To overcome experimental effects, we developed and tested a custom neural network model and compared it with existing prediction methods. Overall, the neural network was able to predict the chronological age with an rmse of 5.88 years (r2  = 0.63) improving upon the 6.15 years achieved by existing normalization methods. We used the feature importance algorithm, Shapley Additive exPlanations (SHAP), to identify compounds related to the biological age. Most importantly, the model returned known aging markers such as kynurenine, indole-3-aldehyde, and acylcarnitines along with a potential novel aging marker, cyclo (leu-pro). Our results validate the association of tryptophan and acylcarnitine metabolism to aging in a highly uncontrolled large-s cale sample. Also, we have shown that by using robust computational methods it is possible to deploy large LC-MS datasets for metabolomics studies to reduce the risk of bias and empower aging studies.
    Keywords:  accelerated aging; big data; inflammaging; machine learning; metabolomics; molecular biology of aging; tryptophan metabolism
    DOI:  https://doi.org/10.1111/acel.13813
  8. Methods Enzymol. 2023 ;pii: S0076-6879(22)00376-7. [Epub ahead of print]682 187-210
    Sample preparation for structural mass spectrometry via polyacrylamide gel electrophoresis.
    Ayako Takemori, Nobuaki Takemori.
      Mass spectrometry is an analytical technique that can detect protein molecules with high sensitivity. Its use is not limited to the mere identification of protein components in biological samples, but is recently being utilized for large-scale analysis of protein structures in vivo as well. Top-down mass spectrometry with an ultra-high resolution mass spectrometer, for example, ionizes proteins in their intact state and allows rapid analysis of their chemical structure, which is used to determine proteoform profiles. Furthermore, cross-linking mass spectrometry, which analyzes enzyme-digested fragments of chemically cross-linked protein complexes, allows acquisition of conformational information on protein complexes in multimolecular crowding environments. In the analysis workflow of structural mass spectrometry, prior fractionation of crude biological samples is an effective way to obtain more detailed structural information. Polyacrylamide gel electrophoresis (PAGE), known as a simple and reproducible means of protein separation in biochemistry, is one example of an excellent high-resolution sample prefractionation tool for structural mass spectrometry. This chapter describes elemental technologies for PAGE-based sample prefractionation including Passively Eluting Proteins from Polyacrylamide gels as Intact species for Mass Spectrometry (PEPPI-MS), a highly efficient method for intact in-gel protein recovery, and Anion-Exchange disk-assisted Sequential sample Preparation (AnExSP), a rapid enzymatic digestion method using a solid-phase extraction microspin column for gel-recovered proteins, in addition to presenting detailed experimental protocols and examples of their use for structural mass spectrometry.
    Keywords:  AnExSP; Cross-linking mass spectrometry; Native mass spectrometry; PEPPI-MS; Passive extraction; Polyacrylamide gel electrophoresis; Protein structure; SDS-PAGE; Top-down proteomics
    DOI:  https://doi.org/10.1016/bs.mie.2022.08.051
  9. Expert Rev Mol Diagn. 2023 Mar 24.
    The dark side of lipid metabolism in prostate and renal carcinoma: novel insights into molecular diagnostic and biomarker discovery.
    Nicola Antonio di Meo, Francesco Lasorsa, Monica Rutigliano, Martina Milella, Matteo Ferro, Michele Battaglia, Pasquale Ditonno, Giuseppe Lucarelli.
      INTRODUCTION: Lipidomics focuses on the in-depth analysis of lipids, which are crucial macromolecules involved in a wide range of metabolic pathways. The increased intracellular accumulation of different classes of lipids in renal cell carcinoma (RCC) and prostate cancer (PCa) cells may be caused by elevated absorption or by increased de novo lipogenesis as a consequence of lipid metabolism reprogramming. The involvement of cholesterol metabolism in cancer's aberrant pathways has also been demonstrated.AREAS COVERED: This review provides an update on the most important lipidomics studies and applications in RCC and PCa, with a particular focus on how knowledge of aberrant lipid pathways may be used to identify biomarkers and novel therapeutic targets. In addition, the application of this methodologies have led to novel cancer subtypes identification and patient's risk stratification. Tracking tumor progression using specific biofluid metabolite profiles offers a huge translational opportunity for urological malignancies.
    EXPERT OPINION: Lipidomics is a promising branch of "omics" approach and should include in next decade new standardized analysis methods and randomized clinical trials in order to reach the aim to use this high-throughput technique in patient-tailored therapy perspective.
    Keywords:  biomarker; lipidomics; metabolism; metabolomics; prostate cancer; renal cell carcinoma
    DOI:  https://doi.org/10.1080/14737159.2023.2195553
  10. Signal Transduct Target Ther. 2023 Mar 22. 8(1): 137
    To metabolomics and beyond: a technological portfolio to investigate cancer metabolism.
    Federica Danzi, Raffaella Pacchiana, Andrea Mafficini, Maria T Scupoli, Aldo Scarpa, Massimo Donadelli, Alessandra Fiore.
      Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.
    DOI:  https://doi.org/10.1038/s41392-023-01380-0
  11. Methods Enzymol. 2023 ;pii: S0076-6879(22)00390-1. [Epub ahead of print]682 375-411
    Multiplex substrate profiling by mass spectrometry for proteases.
    Peter J Rohweder, Zhenze Jiang, Brianna M Hurysz, Anthony J O'Donoghue, Charles S Craik.
      Proteolysis is a central regulator of many biological pathways and the study of proteases has had a significant impact on our understanding of both native biology and disease. Proteases are key regulators of infectious disease and misregulated proteolysis in humans contributes to a variety of maladies, including cardiovascular disease, neurodegeneration, inflammatory diseases, and cancer. Central to understanding a protease's biological role, is characterizing its substrate specificity. This chapter will facilitate the characterization of individual proteases and complex, heterogeneous proteolytic mixtures and provide examples of the breadth of applications that leverage the characterization of misregulated proteolysis. Here we present the protocol of Multiplex Substrate Profiling by Mass Spectrometry (MSP-MS), a functional assay that quantitatively characterizes proteolysis using a synthetic library of physiochemically diverse, model peptide substrates, and mass spectrometry. We present a detailed protocol as well as examples of the use of MSP-MS for the study of disease states, for the development of diagnostic and prognostic tests, for the generation of tool compounds, and for the development of protease-targeted drugs.
    Keywords:  Cancer; Diagnostics; Enzymology; Infectious disease; Mass spectrometry; Post-translational modifying enzymes; Prognostics; Proteases; Substrate profiling
    DOI:  https://doi.org/10.1016/bs.mie.2022.09.009
  12. Biochem J. 2023 Mar 31. 480(6): 403-420
    Principles of phosphoproteomics and applications in cancer research.
    Luke Higgins, Henry Gerdes, Pedro R Cutillas.
      Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.
    Keywords:  kinase biology; mass spectrometry; proteomics
    DOI:  https://doi.org/10.1042/BCJ20220220
  13. J Proteome Res. 2023 Mar 20.
    prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis.
    Witold E Wolski, Paolo Nanni, Jonas Grossmann, Maria d'Errico, Ralph Schlapbach, Christian Panse.
      Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.
    Keywords:  differential expression analysis; proteomics; statistical software
    DOI:  https://doi.org/10.1021/acs.jproteome.2c00441
  14. Nat Commun. 2023 Mar 20. 14(1): 1535
    High-resolution separation of bioisomers using ion cloud profiling.
    Xiaoyu Zhou, Zhuofan Wang, Jingjin Fan, Zheng Ouyang.
      Elucidation of complex structures of biomolecules plays a key role in the field of chemistry and life sciences. In the past decade, ion mobility, by coupling with mass spectrometry, has become a unique tool for distinguishing isomers and isoforms of biomolecules. In this study, we develop a concept for performing ion mobility analysis using an ion trap, which enables isomer separation under ultra-high fields to achieve super high resolutions over 10,000. The potential of this technology has been demonstrated for analysis of isomers for biomolecules including disaccharides, phospholipids, and peptides with post-translational modifications.
    DOI:  https://doi.org/10.1038/s41467-023-37281-7
  15. J Mass Spectrom Adv Clin Lab. 2023 Apr;28 99-104
    Mass spectrometry quantitation of immunosuppressive drugs in clinical specimens using online solid-phase extraction and accurate-mass full scan-single ion monitoring.
    Priscilla S-W Yeung, Paige Miller, Tran Bao Lai-Nyugen, Phil Cheng, Amira Ibrahim, Run-Zhang Shi, Raffick A R Bowen, Ruben Yiqi Luo.
      Introduction: Therapeutic drug monitoring (TDM) of immunosuppressants is essential for optimal care of transplant patients. Immunoassays and liquid chromatography-mass spectrometry (LC-MS) are the most commonly used methods for TDM. However, immunoassays can suffer from interference from heterophile antibodies and structurally similar drugs and metabolites. Additionally, nominal-mass LC-MS assays can be difficult to optimize and are limited in the number of detectable compounds.Objectives: The aim of this study was to implement a mass spectrometry-based test for immunosuppressant TDM using online solid-phase extraction (SPE) and accurate-mass full scan-single ion monitoring (FS-SIM) data acquisition mode.
    Methods: LC-MS analysis was performed on a TLX-2 multi-channel HPLC with a Q-Exactive Plus mass spectrometer. TurboFlow online SPE was used for sample clean up. The accurate-mass MS was set to positive electrospray ionization mode with FS-SIM for quantitation of tacrolimus, sirolimus, everolimus, and cyclosporine A. MS2 fragmentation pattern was used for compound confirmation.
    Results: The method was validated in terms of precision, analytical bias, limit of quantitation, linearity, carryover, sample stability, and interference. Quantitation of tacrolimus, sirolimus, everolimus, and cyclosporine A correlated well with results from an independent reference laboratory (r = 0.926-0.984).
    Conclusions: Accurate-mass FS-SIM can be successfully utilized for immunosuppressant TDM with good correlation with results generated by standard methods. TurboFlow online SPE allows for a simple "protein crash and shoot" sample preparation protocol. Compared to traditional MRM, analyte quantitation by FS-SIM facilitates a streamlined assay optimization process.
    Keywords:  Accurate-mass; CAP, College of American Pathologists; CLSI, Clinical & Laboratory Standards Institute; CV, coefficient of variation; ESI, electrospray ionization; FS-SIM, full scan-single ion monitoring; Full scan single-ion monitoring; HCD, high-energy C-trap dissociation; IRB, Institutional Review Board; Immunosuppressive drugs; LC-MS, liquid chromatography-mass spectrometry; LDT, laboratory developed test; MRM, multiple reaction monitoring; Mass spectrometry; Online solid-phase extraction; SD, standard deviation; SPE, solid-phase extraction; TDM, therapeutic drug monitoring; Therapeutic drug monitoring
    DOI:  https://doi.org/10.1016/j.jmsacl.2023.03.002
  16. Acc Chem Res. 2023 Mar 21.
    Analytical Toolbox to Unlock the Diversity of Oxidized Lipids.
    Michele Wölk, Patricia Prabutzki, Maria Fedorova.
      ConspectusLipids are diverse class of small biomolecules represented by a large variety of chemical structures. In addition to the classical biosynthetic routes, lipids can undergo numerous modifications via introduction of small chemical moieties forming hydroxyl, phospho, and nitro derivatives, among others. Such modifications change the physicochemical properties of a parent lipid and usually result in new functionalities either by mediating signaling events or by changing the biophysical properties of lipid membranes. Over the last decades, a large body of evidence indicated the involvement of lipid modifications in a variety of physiological and pathological events. For instance, lipid (per)oxidation for a long time was considered as a hallmark of oxidative stress and related proinflammatory signaling. Recently, however, with the burst in the development of the redox biology field, oxidative modifications of lipids are also recognized as a part of regulatory and adaptive events that are highly specific for particular cell types, tissues, and conditions.The initial diversity of lipid species and the variety of possible lipid modifications result in an extremely large chemical space of the epilipidome, the subset of the natural lipidome formed by enzymatic and non-enzymatic lipid modifications occurring in biological systems. Together with their low natural abundance, structural annotation of modified lipids represents a major analytical challenge limiting the discovery of their natural variety and functions. Furthermore, the number of available chemically characterized standards representing various modified lipid species remains limited, making analytical and functional studies very challenging. Over the past decade we have developed and implemented numerous analytical methods to study lipid modifications and applied them in the context of different biological conditions. In this Account, we outline the development and evolution of modern mass-spectrometry-based techniques for the structural elucidation of modified/oxidized lipids and corresponding applications. Research of our group is mostly focused on redox biology, and thus, our primary interest was always the analysis of lipid modifications introduced by redox disbalance, including lipid peroxidation (LPO), oxygenation, nitration, and glycation. To this end, we developed an array of analytical solutions to measure carbonyls derived from LPO, oxidized and nitrated fatty acid derivatives, and oxidized and glycated complex lipids. We will briefly describe the main analytical challenges along with corresponding solutions developed by our group toward deciphering the complexity of natural epilipdomes, starting from in vitro-oxidized lipid mixtures, artificial membranes, and lipid droplets, to illustrate the diversity of lipid modifications in the context of metabolic diseases and ferroptotic cell death.
    DOI:  https://doi.org/10.1021/acs.accounts.2c00842
  17. Trends Cancer. 2023 Mar 17. pii: S2405-8033(23)00028-6. [Epub ahead of print]
    Reprogramming of tissue metabolism during cancer metastasis.
    Koelina Ganguly, Alec C Kimmelman.
      Cancer is a systemic disease that involves malignant cell-intrinsic and -extrinsic metabolic adaptations. Most studies have tended to focus on elucidating the metabolic vulnerabilities in the primary tumor microenvironment, leaving the metastatic microenvironment less explored. In this opinion article, we discuss the current understanding of the metabolic crosstalk between the cancer cells and the tumor microenvironment, both at local and systemic levels. We explore the possible influence of the primary tumor secretome to metabolically and epigenetically rewire the nonmalignant distant organs during prometastatic niche formation and successful metastatic colonization by the cancer cells. In an attempt to understand the process of prometastatic niche formation, we have speculated how cancer may hijack the inherent regenerative propensity of tissue parenchyma during metastatic colonization.
    Keywords:  metabolism; metastasis; prometastatic niche; stroma; tissue regeneration; wound response
    DOI:  https://doi.org/10.1016/j.trecan.2023.02.005
  18. Elife. 2023 Mar 24. pii: e85345. [Epub ahead of print]12
    Defects in lipid homeostasis reflect the function of TANGO2 in phospholipid and neutral lipid metabolism.
    Agustin Leonardo Lujan, Ombretta Foresti, Conor Sugden, Nathalie Brouwers, Alex Mateo Farre, Alessio Vignoli, Mahshid Azamian, Alicia Turner, Jose Wojnacki, Vivek Malhotra.
      We show that TANGO2 in mammalian cells localizes predominantly to mitochondria and partially at mitochondria sites juxtaposed to lipid droplets (LDs) and the endoplasmic reticulum. HepG2 cells and fibroblasts of patients lacking TANGO2 exhibit enlarged LDs. Quantitative lipidomics revealed a marked increase in lysophosphatidic acid (LPA) and a concomitant decrease in its biosynthetic precursor phosphatidic acid (PA). These changes were exacerbated in nutrient-starved cells. Based on our data, we suggest that TANGO2 function is linked to acyl-CoA metabolism, which is necessary for the acylation of LPA to generate PA. The defect in acyl-CoA availability impacts the metabolism of many other fatty acids, generates high levels of reactive oxygen (ROS), and promotes lipid peroxidation. We suggest that the increased size of LDs is a combination of enrichment in peroxidized lipids and a defect in their catabolism. Our findings help explain the physiological consequence of mutations in TANGO2 that induce acute metabolic crises, including rhabdomyolysis, cardiomyopathy, and cardiac arrhythmias, often leading to fatality upon starvation and stress.
    Keywords:  cell biology; human
    DOI:  https://doi.org/10.7554/eLife.85345
  19. Nat Commun. 2023 Mar 23. 14(1): 1618
    Highly-sensitive label-free deep profiling of N-glycans released from biomedically-relevant samples.
    Anne-Lise Marie, Somak Ray, Alexander R Ivanov.
      Alterations of protein glycosylation can serve as sensitive and specific disease biomarkers. Labeling procedures for improved separation and detectability of oligosaccharides have several drawbacks, including incomplete derivatization, side-products, noticeable desialylation/defucosylation, sample loss, and interference with downstream analyses. Here, we develop a label-free workflow based on high sensitivity capillary zone electrophoresis-mass spectrometry (CZE-MS) for profiling of native underivatized released N-glycans. Our workflow provides a >45-fold increase in signal intensity compared to the conventional CZE-MS approaches used for N-glycan analysis. Qualitative and quantitative N-glycan profiling of purified human serum IgG, bovine serum fetuin, bovine pancreas ribonuclease B, blood-derived extracellular vesicle isolates, and total plasma results in the detection of >250, >400, >150, >310, and >520 N-glycans, respectively, using injected amounts equivalent to <25 ng of model protein and nL-levels of plasma-derived samples. Compared to reported results for biological samples of similar amounts and complexity, the number of identified N-glycans is increased up to ~15-fold, enabling highly sensitive analysis of sample amounts as low as sub-0.2 nL of plasma volume equivalents. Furthermore, highly sialylated N-glycans are identified and structurally characterized, and untreated sialic acid-linkage isomers are resolved in a single CZE-MS analysis.
    DOI:  https://doi.org/10.1038/s41467-023-37365-4
  20. Seizure. 2023 Mar 16. pii: S1059-1311(23)00075-4. [Epub ahead of print]107 52-59
    Comprehensive study of metabolic changes induced by a ketogenic diet therapy using GC/MS- and LC/MS-based metabolomics.
    Mari Akiyama, Tomoyuki Akiyama, Daisuke Saigusa, Eiji Hishinuma, Naomi Matsukawa, Takashi Shibata, Hiroki Tsuchiya, Atsushi Mori, Yuji Fujii, Yukiko Mogami, Chiho Tokorodani, Kozue Kuwahara, Yurika Numata-Uematsu, Kenji Inoue, Katsuhiro Kobayashi.
      OBJECTIVE: The ketogenic diet (KD), a high-fat and low-carbohydrate diet, is effective for a subset of patients with drug-resistant epilepsy, although the mechanisms of the KD have not been fully elucidated. The aims of this observational study were to investigate comprehensive short-term metabolic changes induced by the KD and to explore candidate metabolites or pathways for potential new therapeutic targets.METHODS: Subjects included patients with intractable epilepsy who had undergone the KD therapy (the medium-chain triglyceride [MCT] KD or the modified Atkins diet using MCT oil). Plasma and urine samples were obtained before and at 2-4 weeks after initiation of the KD. Targeted metabolome analyses of these samples were performed using gas chromatography-tandem mass spectrometry (GC/MS/MS) and liquid chromatography-tandem mass spectrometry (LC/MS/MS).
    RESULTS: Samples from 10 and 11 patients were analysed using GC/MS/MS and LC/MS/MS, respectively. The KD increased ketone bodies, various fatty acids, lipids, and their conjugates. In addition, levels of metabolites located upstream of acetyl-CoA and propionyl-CoA, including catabolites of branched-chain amino acids and structural analogues of γ-aminobutyric acid and lactic acid, were elevated.
    CONCLUSIONS: The metabolites that were significantly changed after the initiation of the KD and related metabolites may be candidates for further studies for neuronal actions to develop new anti-seizure medications.
    Keywords:  Amino acids; Biomarkers; Intractable epilepsy; Ketone bodies; Organic acids
    DOI:  https://doi.org/10.1016/j.seizure.2023.03.014
  21. BMC Bioinformatics. 2023 Mar 22. 24(1): 106
    Pickaxe: a Python library for the prediction of novel metabolic reactions.
    Kevin M Shebek, Jonathan Strutz, Linda J Broadbelt, Keith E J Tyo.
      BACKGROUND: Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user's application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation.RESULTS: Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset.
    CONCLUSION: Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.
    Keywords:  Biosynthetic design; Enzyme promiscuity; Metabolite identification; Network generation; Retrobiosynthesis
    DOI:  https://doi.org/10.1186/s12859-023-05149-8
  22. Chem Sci. 2023 Mar 15. 14(11): 2887-2900
    All-in-One digital microfluidics pipeline for proteomic sample preparation and analysis.
    Jiaxi Peng, Calvin Chan, Shuailong Zhang, Alexandros A Sklavounos, Maxwell E Olson, Erica Y Scott, Yechen Hu, Vigneshwar Rajesh, Bingyu B Li, M Dean Chamberlain, Shen Zhang, Hui Peng, Aaron R Wheeler.
      Highly sensitive and reproducible analysis of samples containing low amounts of protein is restricted by sample loss and the introduction of contaminants during processing. Here, we report an All-in-One digital microfluidic (DMF) pipeline for proteomic sample reduction, alkylation, digestion, isotopic labeling and analysis. The system features end-to-end automation, with integrated thermal control for digestion, optimized droplet additives for sample manipulation and analysis, and an automated interface to liquid chromatography with tandem mass spectrometry (HPLC-MS/MS). Dimethyl labeling was integrated into the pipeline to allow for relative quantification of the trace samples at the nanogram level, and the new pipeline was applied to evaluating cancer cell lines and cancer tissue samples. Several known proteins (including HSP90AB1, HSPB1, LDHA, ENO1, PGK1, KRT18, and AKR1C2) and pathways were observed between model breast cancer cell lines related to hormone response, cell metabolism, and cell morphology. Furthermore, differentially quantified proteins (such as PGS2, UGDH, ASPN, LUM, COEA1, and PRELP) were found in comparisons of healthy and cancer breast tissues, suggesting potential utility of the All-in-One pipeline for the emerging application of proteomic cancer sub-typing. In sum, the All-in-One pipeline represents a powerful new tool for automated proteome processing and analysis, with the potential to be useful for evaluating mass-limited samples for a wide range of applications.
    DOI:  https://doi.org/10.1039/d3sc00560g
  23. BMC Bioinformatics. 2023 Mar 22. 24(1): 108
    Bayesian kinetic modeling for tracer-based metabolomic data.
    Xu Zhang, Ya Su, Andrew N Lane, Arnold J Stromberg, Teresa W M Fan, Chi Wang.
      BACKGROUND: Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly [Formula: see text]-enriched glucose ([Formula: see text]-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups.RESULTS: We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux .
    CONCLUSIONS: Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.
    Keywords:  Bayesian method; Kinetic modeling; SIRM
    DOI:  https://doi.org/10.1186/s12859-023-05211-5
  24. Mol Cancer Res. 2023 Mar 24. pii: MCR-22-0843. [Epub ahead of print]
    Non-essential amino acid availability influences proteostasis and breast cancer cell survival during proteotoxic stress.
    Sara Sannino, Allison M Manuel, Chaowei Shang, Stacy G Wendell, Peter Wipf, Jeffrey L Brodsky.
      Protein homeostasis (proteostasis) regulates tumor growth and proliferation when cells are exposed to proteotoxic stress, such as during treatment with certain chemotherapeutics. Consequently, cancer cells depend to a greater extent on stress signaling, and require the integrated stress response (ISR), amino acid metabolism, and efficient protein folding and degradation pathways to survive. To define how these interconnected pathways are wired when cancer cells are challenged with proteotoxic stress, we investigated how amino acid abundance influences cell survival when Hsp70, a master proteostasis regulator, is inhibited. We previously demonstrated that cancer cells exposed to a specific Hsp70 inhibitor induce the ISR via the action of two sensors, GCN2 and PERK, in stress-resistant and sensitive cells, respectively. In resistant cells, the induction of GCN2 and autophagy supported resistant cell survival, yet the mechanism by which these events were induced remained unclear. We now report that amino acid availability reconfigures the proteostasis network. Amino acid supplementation, and in particular arginine addition, triggered cancer cell death by blocking autophagy. Consistent with the importance of amino acid availability, which when limited activates GCN2, resistant cancer cells succumbed when challenged with a potentiator for another amino acid sensor, mTORC1, in conjunction with Hsp70 inhibition. Implications: These data position amino acid abundance, GCN2, mTORC1, and autophagy as integrated therapeutic targets whose coordinated modulation regulates the survival of proteotoxic-resistant breast cancer cells.
    DOI:  https://doi.org/10.1158/1541-7786.MCR-22-0843
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