bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2022‒05‒29
38 papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh
  1. High-Throughput Mass Spectrometry-Based Proteomics with dia-PASEF.
  2. Untargeted and Targeted Circadian Metabolomics Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) and Flow Injection-Electrospray Ionization-Tandem Mass Spectrometry (FIA-ESI-MS/MS).
  3. Mapping Cell Surface Proteolysis with Plasma Membrane-Targeted Subtiligase.
  4. DIA Proteomics and Machine Learning for the Fast Identification of Bacterial Species in Biological Samples.
  5. Phosphoproteomics and Organelle Proteomics in Pancreatic Islets.
  6. Targeted LC-MS/MS-based metabolomics and lipidomics on limited hematopoietic stem cell numbers.
  7. Rodent Lung Tissue Sample Preparation and Processing for Shotgun Proteomics.
  8. Editorial: Metabolism Meets Function: The Multifaced Role of Metabolism in Cancer.
  9. A High Throughput Lipidomics Method Using Scheduled Multiple Reaction Monitoring.
  10. Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies.
  11. Normalizing and Correcting Variable and Complex LC-MS Metabolomic Data with the R Package pseudoDrift.
  12. Understanding Inborn Errors of Metabolism through Metabolomics.
  13. 13C-Isotope-Assisted Assessment of Metabolic Quenching During Sample Collection from Suspension Cell Cultures.
  14. Metformin and Cancer, an Ambiguanidous Relationship.
  15. Recent Trends of Microbiota-Based Microbial Metabolites Metabolism in Liver Disease.
  16. A Comparison of Solvent-Based Extraction Methods to Assess the Central Carbon Metabolites in Mouse Bone and Muscle.
  17. Review of the Real and Sometimes Hidden Costs in Proteomics Experimental Workflows.
  18. Protein Purification and Digestion Methods for Bacterial Proteomic Analyses.
  19. Untargeted Metabolomic Profiling of Fungal Species Populations.
  20. Proteomic Profiling of the Interplay Between a Bacterial Pathogen and Host Uncovers Novel Anti-Virulence Strategies.
  21. Integrated Multiomics, Bioinformatics, and Computational Modeling Approaches to Central Metabolism in Organs.
  22. NADH-independent enzymatic assay to quantify extracellular and intracellular L-lactate levels.
  23. Retrospective analysis of metabolite patterns of clobazam and N-desmethylclobazam in human plasma by LC-MS/MS.
  24. Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area.
  25. An Update on Sphingolipidomics: Is Something Still Missing? Some Considerations on the Analysis of Complex Sphingolipids and Free-Sphingoid Bases in Plasma and Red Blood Cells.
  26. Targeted Metabolomics Using LC-MS in Neurospora crassa.
  27. CFM-ID 4.0 - a web server for accurate MS-based metabolite identification.
  28. Metabolomes of Lewis lung carcinoma metastases and normal lung tissue from mice fed different diets.
  29. Metabolomic Analysis of Plasma from Breast Cancer Patients Using Ultra-High-Performance Liquid Chromatography Coupled with Mass Spectrometry: An Untargeted Study.
  30. Lysosomal lipid switch sensitises to nutrient deprivation and mTOR targeting in pancreatic cancer.
  31. Towards Unbiased Evaluation of Ionization Performance in LC-HRMS Metabolomics Method Development.
  32. Developing dietary interventions as therapy for cancer.
  33. Isolation of Detergent Insoluble Proteins from Mouse Brain Tissue for Quantitative Analysis Using Data Independent Acquisition (DIA).
  34. Diversion of Acetyl CoA to 3-Methylglutaconic Acid Caused by Discrete Inborn Errors of Metabolism.
  35. N-Terminomics/TAILS of Tissue and Liquid Biopsies.
  36. Defining mitochondrial protein functions through deep multiomic profiling.
  37. Lipidomics Analysis of Free Fatty Acids in Human Plasma of Healthy and Diabetic Subjects by Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS).
  38. Automating Assignment, Quantitation, and Biological Annotation of Redox Proteomics Datasets with ProteoSushi.
  1. Methods Mol Biol. 2022 ;2456 15-27
    High-Throughput Mass Spectrometry-Based Proteomics with dia-PASEF.
    Patricia Skowronek, Florian Meier.
      Ion mobility separation is becoming an integral part in mass spectrometry-based proteomics. Here we describe the use of a trapped ion mobility-quadrupole time-of-flight (TIMS-QTOF) mass spectrometer for high-throughput label-free quantification with data-independent acquisition. The parallel accumulation-serial fragmentation (PASEF) operation mode positions the mass-selecting quadrupole as a function of the TIMS separation, which allows highly efficient data-independent acquisition schemes (dia-PASEF), but also increases complexity in the method design. We provide a step-by-step protocol for instrument setup, method design, data acquisition and ion mobility-aware, library-based data analysis with Spectronaut. We highlight key acquisition parameters and illustrate their optimization for short gradients. Using the EvosepOne liquid chromatography system, we demonstrate expected results for the analysis of a human cancer cell line at a throughput of 60 samples per day, leading to the quantification of about 6000 protein groups with very high reproducibility. Importantly, the protocol can be readily adapted to other gradients and sample types such as modified peptides.
    Keywords:  Data-independent acquisition; Ion mobility; Mass spectrometry; PASEF; Proteomics; TIMS; dia-PASEF
    DOI:  https://doi.org/10.1007/978-1-0716-2124-0_2
  2. Methods Mol Biol. 2022 ;2482 311-327
    Untargeted and Targeted Circadian Metabolomics Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) and Flow Injection-Electrospray Ionization-Tandem Mass Spectrometry (FIA-ESI-MS/MS).
    Anna Artati, Cornelia Prehn, Dominik Lutter, Kenneth Allen Dyar.
      A diverse array of 24-h oscillating hormones and metabolites direct and reflect circadian clock function. Circadian metabolomics uses advanced high-throughput analytical chemistry techniques to comprehensively profile these small molecules (<1.5 kDa) across 24 h in cells, media, body fluids, breath, tissues, and subcellular compartments. The goals of circadian metabolomics experiments are often multifaceted. These include identifying and tracking rhythmic metabolic inputs and outputs of central and peripheral circadian clocks, quantifying endogenous free-running period, monitoring relative phase alignment between clocks, and mapping pathophysiological consequences of clock disruption or misalignment. Depending on the particular experimental question, samples are collected under free-running or entrained conditions. Here we describe both untargeted and targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) and flow injection-electrospray ionization-tandem mass spectrometry (FIA-ESI-MS/MS) based assays we have used for circadian metabolomics studies. We discuss tissue homogenization, chemical derivatization, measurement, and tips for data processing, normalization, scaling, how to handle outliers, and imputation of missing values.
    Keywords:  Circadian; Circadian metabolomics; Flow injection-electrospray ionization-tandem mass spectrometry (FIA-ESI-MS/MS); Liquid chromatography-tandem mass spectrometry (LC-MS/MS); Metabolites; Targeted metabolomics; Untargeted metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-2249-0_21
  3. Methods Mol Biol. 2022 ;2456 71-83
    Mapping Cell Surface Proteolysis with Plasma Membrane-Targeted Subtiligase.
    Aspasia A Amiridis, Amy M Weeks.
      N terminomics methods combine selective isolation of protein N-terminal peptides with mass spectrometry (MS)-based proteomics for global profiling of proteolytic cleavage sites. However, traditional N terminomics workflows require cell lysis before N-terminal enrichment and provide poor coverage of N termini derived from cell surface proteins. Here, we describe application of subtiligase-TM, a plasma membrane-targeted peptide ligase, for selective biotinylation of cell surface N termini, enabling their enrichment and analysis by liquid chromatography-tandem MS (LC-MS/MS). This method provides increased coverage of and specificity for cell surface N termini and is compatible with existing quantitative LC-MS/MS workflows.
    Keywords:  Cell surface; Enrichment; Mass spectrometry; N terminomics; Post-translational modification; Proteolysis; Proteomics
    DOI:  https://doi.org/10.1007/978-1-0716-2124-0_6
  4. Methods Mol Biol. 2022 ;2456 299-317
    DIA Proteomics and Machine Learning for the Fast Identification of Bacterial Species in Biological Samples.
    Florence Roux-Dalvai, Mickaël Leclercq, Clarisse Gotti, Arnaud Droit.
      Identification of bacterial species in biological samples is essential in many applications. However, the standard methods usually use a time-consuming bacterial culture (24-48 h) and sometimes lack in specificity. To overcome these limitations, we developed a new protocol, combining LC-MS/MS analysis in Data Independent Acquisition mode and machine learning algorithms, enabling the accurate identification of the bacterial species contaminating a sample in a few hours without bacterial culture. In this chapter, we describe the three steps of the protocol (spectral libraries generation, training step, identification step) to generate customized peptide signatures and use them for bacterial identification in biological samples through targeted proteomics analyses and prediction models.
    Keywords:  Bacterial identification; Data independent acquisition; LC-MS/MS; Machine learning; Peptide signature
    DOI:  https://doi.org/10.1007/978-1-0716-2124-0_21
  5. Methods Mol Biol. 2022 ;2456 123-140
    Phosphoproteomics and Organelle Proteomics in Pancreatic Islets.
    Özüm Sehnaz Caliskan, Giorgia Massacci, Natalie Krahmer, Francesca Sacco.
      Over the recent years, mass spectrometry (MS)-based proteomics has undergone dramatic advances in sample preparation, instrumentation, and computational methods. Here, we describe in detail, how a workflow quantifies global protein phosphorylation in pancreatic islets and characterizes intracellular organelle composition on protein level by MS-based proteomics.
    Keywords:  Mass spectrometry-based phosphoproteomics; Organelles; Pancreatic islets; Protein correlation profiling
    DOI:  https://doi.org/10.1007/978-1-0716-2124-0_9
  6. STAR Protoc. 2022 Jun 17. 3(2): 101408
    Targeted LC-MS/MS-based metabolomics and lipidomics on limited hematopoietic stem cell numbers.
    Katharina Schönberger, Michael Mitterer, Joerg M Buescher, Nina Cabezas-Wallscheid.
      Metabolism is important for the regulation of hematopoietic stem cells (HSCs) and drives cellular fate. Due to the scarcity of HSCs, it has been technically challenging to perform metabolome analyses gaining insight into HSC metabolic regulatory networks. Here, we present two targeted liquid chromatography-mass spectrometry approaches that enable the detection of metabolites after fluorescence-activated cell sorting when sample amounts are limited. One protocol covers signaling lipids and retinoids, while the second detects tricarboxylic acid cycle metabolites and amino acids. For complete details on the use and execution of this protocol, please refer to Schönberger et al. (2022).
    Keywords:  Mass Spectrometry; Metabolomics; Stem Cells
    DOI:  https://doi.org/10.1016/j.xpro.2022.101408
  7. Methods Mol Biol. 2022 ;2456 53-62
    Rodent Lung Tissue Sample Preparation and Processing for Shotgun Proteomics.
    Hadeesha Piyadasa, Ying Lao, Oleg Krokhin, Neeloffer Mookherjee.
      Mass spectrometry (MS) is a routinely used approach to characterize global protein profile in various biological samples. Here we describe rodent lung tissue homogenization, sample preparation, and liquid chromatography with tandem mass spectrometry (LC-MS/MS) method for shotgun proteomics.
    Keywords:  Mass spectrometry; Rodent lungs; Sample preparation; Shotgun proteomics
    DOI:  https://doi.org/10.1007/978-1-0716-2124-0_4
  8. Front Oncol. 2022 ;12 906421
    Editorial: Metabolism Meets Function: The Multifaced Role of Metabolism in Cancer.
    Isabella Giacomini, Monica Montopoli.
      
    Keywords:  cancer; cancer metabolism; drug resistance; metabolic reprogramming; targeting metabolism; tumor microenviroment (TME)
    DOI:  https://doi.org/10.3389/fonc.2022.906421
  9. Biomolecules. 2022 May 16. pii: 709. [Epub ahead of print]12(5):
    A High Throughput Lipidomics Method Using Scheduled Multiple Reaction Monitoring.
    Akash Kumar Bhaskar, Salwa Naushin, Arjun Ray, Praveen Singh, Anurag Raj, Shalini Pradhan, Khushboo Adlakha, Towfida Jahan Siddiqua, Dipankar Malakar, Debasis Dash, Shantanu Sengupta.
      Lipid compositions of cells, tissues, and bio-fluids are complex, with varying concentrations and structural diversity making their identification challenging. Newer methods for comprehensive analysis of lipids are thus necessary. Herein, we propose a targeted-mass spectrometry based lipidomics screening method using a combination of variable retention time window and relative dwell time weightage. Using this method, we identified more than 1000 lipid species within 24-min. The limit of detection varied from the femtomolar to the nanomolar range. About 883 lipid species were detected with a coefficient of variance &lt;30%. We used this method to identify plasma lipids altered due to vitamin B12 deficiency and found a total of 18 lipid species to be altered. Some of the lipid species with ω-6 fatty acid chains were found to be significantly increased while ω-3 decreased in vitamin B12 deficient samples. This method enables rapid screening of a large number of lipid species in a single experiment and would substantially advance our understanding of the role of lipids in biological processes.
    Keywords:  dwell time; isomers; lipidomics; mass spectrometry; plasma lipidome; scheduled MRM; variable RT window; vitamin B12
    DOI:  https://doi.org/10.3390/biom12050709
  10. Methods Mol Biol. 2022 ;2456 319-338
    Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies.
    Caitlin M A Simopoulos, Daniel Figeys, Mathieu Lavallée-Adam.
      Constant improvements in mass spectrometry technologies and laboratory workflows have enabled the proteomics investigation of biological samples of growing complexity. Microbiomes represent such complex samples for which metaproteomics analyses are becoming increasingly popular. Metaproteomics experimental procedures create large amounts of data from which biologically relevant signal must be efficiently extracted to draw meaningful conclusions. Such a data processing requires appropriate bioinformatics tools specifically developed for, or capable of handling metaproteomics data. In this chapter, we outline current and novel tools that can perform the most commonly used steps in the analysis of cutting-edge metaproteomics data, such as peptide and protein identification and quantification, as well as data normalization, imputation, mining, and visualization. We also provide details about the experimental setups in which these tools should be used.
    Keywords:  Bioinformatics; Computational biology; Mass spectrometry; Metaproteomics; Microbiome; Proteomics; Quantification; Software; Statistics
    DOI:  https://doi.org/10.1007/978-1-0716-2124-0_22
  11. Metabolites. 2022 May 12. pii: 435. [Epub ahead of print]12(5):
    Normalizing and Correcting Variable and Complex LC-MS Metabolomic Data with the R Package pseudoDrift.
    Jonas Rodriguez, Lina Gomez-Cano, Erich Grotewold, Natalia de Leon.
      In biological research domains, liquid chromatography-mass spectroscopy (LC-MS) has prevailed as the preferred technique for generating high quality metabolomic data. However, even with advanced instrumentation and established data acquisition protocols, technical errors are still routinely encountered and can pose a significant challenge to unveiling biologically relevant information. In large-scale studies, signal drift and batch effects are how technical errors are most commonly manifested. We developed pseudoDrift, an R package with capabilities for data simulation and outlier detection, and a new training and testing approach that is implemented to capture and to optionally correct for technical errors in LC-MS metabolomic data. Using data simulation, we demonstrate here that our approach performs equally as well as existing methods and offers increased flexibility to the researcher. As part of our study, we generated a targeted LC-MS dataset that profiled 33 phenolic compounds from seedling stem tissue in 602 genetically diverse non-transgenic maize inbred lines. This dataset provides a unique opportunity to investigate the dynamics of specialized metabolism in plants.
    Keywords:  LC–MS; data normalization; maize; metabolomics; signal drift
    DOI:  https://doi.org/10.3390/metabo12050435
  12. Metabolites. 2022 Apr 27. pii: 398. [Epub ahead of print]12(5):
    Understanding Inborn Errors of Metabolism through Metabolomics.
    Karen Driesen, Peter Witters.
      Inborn errors of metabolism (IEMs) are rare diseases caused by a defect in a single enzyme, co-factor, or transport protein. For most IEMs, no effective treatment is available and the exact disease mechanism is unknown. The application of metabolomics and, more specifically, tracer metabolomics in IEM research can help to elucidate these disease mechanisms and hence direct novel therapeutic interventions. In this review, we will describe the different approaches to metabolomics in IEM research. We will discuss the strengths and weaknesses of the different sample types that can be used (biofluids, tissues or cells from model organisms; modified cell lines; and patient fibroblasts) and when each of them is appropriate to use.
    Keywords:  inborn errors of metabolism; metabolomics; stable isotopes
    DOI:  https://doi.org/10.3390/metabo12050398
  13. Anal Chem. 2022 May 25.
    13C-Isotope-Assisted Assessment of Metabolic Quenching During Sample Collection from Suspension Cell Cultures.
    Bo Wang, Jamey D Young.
      Metabolomics and fluxomics are core approaches to directly profile and interrogate cellular metabolism in response to various genetic or environmental perturbations. In order to accurately measure the abundance and isotope enrichment of intracellular metabolites, cell culture samples must be rapidly harvested and cold quenched to preserve the in vivo metabolic state of the cells at the time of sample collection. When dealing with suspension cultures, this process is complicated by the need to separate the liquid culture media from cellular biomass prior to metabolite extraction. Here, we examine the efficacy of several commonly used metabolic quenching methods, using the model cyanobacterium Synechocystis sp. PCC 6803 as an example. Multiple 13C-labeled compounds, including 13C-bicarbonate, 13C-glucose, and 13C-glutamine, were used as tracers during the sample collection and the cold-quenching process to assess the extent of metabolic turnover after cells were harvested from culture flasks. We show that the combination of rapid filtration followed by 100% cold (-80 °C) methanol quenching exhibits the highest quenching efficiency, while mixing cell samples with a partially frozen 30% methanol slurry (-24 °C) followed by centrifugation is slightly less effective at quenching metabolism but enables less laborious sample processing. By contrast, rapidly mixing the cells with a saline ice slurry (∼0 °C) is less effective, as indicated by high isotope-labeling rates after sample harvest, while mixing the cells with 60% cold methanol (-65 °C) prior to centrifugation causes significant metabolite loss. This study demonstrates a rigorous, quantitative, and broadly applicable method for assessing the metabolic quenching efficacy of protocols used for sample collection in metabolomics and fluxomics studies.
    DOI:  https://doi.org/10.1021/acs.analchem.1c05338
  14. Pharmaceuticals (Basel). 2022 May 19. pii: 626. [Epub ahead of print]15(5):
    Metformin and Cancer, an Ambiguanidous Relationship.
    Sarah J Skuli, Safwan Alomari, Hallie Gaitsch, A'ishah Bakayoko, Nicolas Skuli, Betty M Tyler.
      The deregulation of energetic and cellular metabolism is a signature of cancer cells. Thus, drugs targeting cancer cell metabolism may have promising therapeutic potential. Previous reports demonstrate that the widely used normoglycemic agent, metformin, can decrease the risk of cancer in type 2 diabetics and inhibit cell growth in various cancers, including pancreatic, colon, prostate, ovarian, and breast cancer. While metformin is a known adenosine monophosphate-activated protein kinase (AMPK) agonist and an inhibitor of the electron transport chain complex I, its mechanism of action in cancer cells as well as its effect on cancer metabolism is not clearly established. In this review, we will give an update on the role of metformin as an antitumoral agent and detail relevant evidence on the potential use and mechanisms of action of metformin in cancer. Analyzing antitumoral, signaling, and metabolic impacts of metformin on cancer cells may provide promising new therapeutic strategies in oncology.
    Keywords:  AMPK; PI3K; cancer metabolism; diabetes; drug repurposing; metformin; therapeutics
    DOI:  https://doi.org/10.3390/ph15050626
  15. Front Med (Lausanne). 2022 ;9 841281
    Recent Trends of Microbiota-Based Microbial Metabolites Metabolism in Liver Disease.
    Raja Ganesan, Jin-Ju Jeong, Dong Joon Kim, Ki Tae Suk.
      The gut microbiome and microbial metabolomic influences on liver diseases and their diagnosis, prognosis, and treatment are still controversial. Research studies have provocatively claimed that the gut microbiome, metabolomics understanding, and microbial metabolite screening are key approaches to understanding liver cancer and liver diseases. An advance of logical innovations in metabolomics profiling, the metabolome inclusion, challenges, and the reproducibility of the investigations at every stage are devoted to this domain to link the common molecules across multiple liver diseases, such as fatty liver, hepatitis, and cirrhosis. These molecules are not immediately recognizable because of the huge underlying and synthetic variety present inside the liver cellular metabolome. This review focuses on microenvironmental metabolic stimuli in the gut-liver axis. Microbial small-molecule profiling (i.e., semiquantitative monitoring, metabolic discrimination, target profiling, and untargeted profiling) in biological fluids has been incompletely addressed. Here, we have reviewed the differential expression of the metabolome of short-chain fatty acids (SCFAs), tryptophan, one-carbon metabolism and bile acid, and the gut microbiota effects are summarized and discussed. We further present proof-of-evidence for gut microbiota-based metabolomics that manipulates the host's gut or liver microbes, mechanosensitive metabolite reactions and potential metabolic pathways. We conclude with a forward-looking perspective on future attention to the "dark matter" of the gut microbiota and microbial metabolomics.
    Keywords:  liver diseases; liver therapies; metabolic discrimination; metabolites alteration; microbial metabolomics; short-chain fatty acids; tryptophan metabolism
    DOI:  https://doi.org/10.3389/fmed.2022.841281
  16. Metabolites. 2022 May 18. pii: 453. [Epub ahead of print]12(5):
    A Comparison of Solvent-Based Extraction Methods to Assess the Central Carbon Metabolites in Mouse Bone and Muscle.
    Daniela B Dias, Raphaela Fritsche-Guenther, Friederike Gutmann, Georg N Duda, Jennifer Kirwan, Patrina S P Poh.
      The identification of endogenous metabolites has great potential for understanding the underlying tissue processes occurring in either a homeostatic or a diseased state. The application of gas chromatography-mass spectrometry (GC-MS)-based metabolomics on musculoskeletal tissue samples has gained traction. However, limited comparison studies exist evaluating the sensitivity, reproducibility, and robustness of the various existing extraction protocols for musculoskeletal tissues. Here, we evaluated polar metabolite extraction from bone and muscle of mouse origin. The extraction methods compared were (1) modified Bligh-Dyer (mBD), (2) low chloroform (CHCl3)-modified Bligh-Dyer (mBD-low), and (3) modified Matyash (mMat). In particular, the central carbon metabolites (CCM) appear to be relevant for musculoskeletal regeneration, given their role in energy metabolism. However, the sensitivity, reproducibility, and robustness of these methods for detecting targeted polar CCM remains unknown. Overall, the extraction of metabolites using the mBD, mBD-low, and mMat methods appears sufficiently robust and reproducible for bone, with the mBD method slightly bettering the mBD-low and mMat methods. Furthermore, mBD, mBD-low, and mMat were sufficiently sensitive in detecting polar metabolites extracted from mouse muscle; however, they lacked repeatability. This study highlights the need for a re-thinking, towards a tissue-specific optimization of methods for metabolite extractions, ensuring sufficient sensitivity, repeatability, and robustness.
    Keywords:  GC-MS; bone; central carbon metabolism; metabolites; metabolomics; muscle
    DOI:  https://doi.org/10.3390/metabo12050453
  17. Methods Mol Biol. 2022 ;2456 1-14
    Review of the Real and Sometimes Hidden Costs in Proteomics Experimental Workflows.
    Aicha Asma Houfani, Leonard James Foster.
      A typical proteomics workflow covers all the steps from growing or collecting the cells/tissues/organism, protein extraction, digestion and cleanup, mass spectrometric analysis, and, finally, extensive bioinformatics to derive biological insight from the data. The details of the procedures employed for this can vary widely by laboratory and by sample type: e.g., hard tissues or cells with walls require much more mechanical disruption to extract proteins than do soft tissues, biological fluids, or wall-less cells. Everything then converges on the mass spectrometer, where there are further choices to be made about how to do the analysis. There is one commonality, however, virtually every group around the world now uses liquid chromatography on-line coupled to tandem mass spectrometry, which means that significant amounts of instrument time are dedicated to every sample. There are many other reviews or methods papers, including in this volume, that cover the details of the various procedures involved in proteomic analyses of all types of samples. Our focus here will be on the cost considerations for such analyses, including considerations to ensure that useful data can be obtained the first time a sample is analyzed. Some of these costs are often overlooked, particularly for those groups who operate their own mass spectrometer(s) and do not have to go to a fee-for-service facility to have something analyzed. The chapter presents several challenges and key suggestions in proving hypotheses in proteomics experimental workflow in different biological systems with specific regard to the costs involved, both real and hidden. The detailed methodology for cost-based studies reported in this chapter can help researchers to set up their laboratory with appropriate equipment as well as to identify potential collaborations based on their analytical instrumentation.
    Keywords:  Automation; Mass spectrometry; Proteomics; Sample preparation; Systems biology
    DOI:  https://doi.org/10.1007/978-1-0716-2124-0_1
  18. Methods Mol Biol. 2022 ;2456 63-70
    Protein Purification and Digestion Methods for Bacterial Proteomic Analyses.
    Nicole Hansmeier, Samrachana Sharma, Tzu-Chiao Chao.
      Reproducible protein extraction is critical for the quantitative analysis of bacterial proteomes. While a wide range of techniques exist, there is no one-size-fits-all solution that will be suitable for all applications. In this report, we describe a set of standard extraction methods that have been adapted for a range of bacterial proteome analyses.
    Keywords:  Mass spectrometry; Proteome purification; Quantitative proteomics; Sample preparation; Surfactant
    DOI:  https://doi.org/10.1007/978-1-0716-2124-0_5
  19. Methods Mol Biol. 2022 ;2456 349-365
    Untargeted Metabolomic Profiling of Fungal Species Populations.
    Thomas E Witte, David P Overy.
      This chapter describes protocols for the development of consensus chemical phenotypes or "metabolomes" of fungal populations using ultra-high pressure liquid chromatography coupled to high resolution mass spectrometry (UPLC-HRMS). Isolates are cultured using multiple media conditions to elicit the expression of diverse secondary metabolite biosynthetic gene clusters. The mycelium and spent culture media are extracted using organic solvents and profiled by ultra-high pressure chromatography coupled with a high resolution Thermo Orbitrap XL mass spectrometer with the ability to trap and fragment ions to general MS2 spectra. MS data preprocessing is explained and illustrated using the freely available software MZMine 2. Through data processing, binary matrices of mass features can be generated and then combined into a consensus secondary metabolite phenotype of all isolates grown in all media conditions. The production of consensus chemical phenotypes is useful for screening large fungal populations (both inter and intra-species populations) for isolates potentially expressing novel secondary metabolites or analogs of known secondary metabolites.
    Keywords:  Consensus chemical phenotypes; Fungal natural products; Fungi; LCMS; MZMine; Mass spectrometry; Metabolomics; Secondary metabolites; Thermo Orbitrap XL; UPLC-HRMS
    DOI:  https://doi.org/10.1007/978-1-0716-2124-0_24
  20. Methods Mol Biol. 2022 ;2456 253-262
    Proteomic Profiling of the Interplay Between a Bacterial Pathogen and Host Uncovers Novel Anti-Virulence Strategies.
    Arjun Sukumaran, Jennifer Geddes-McAlister.
      Bottom-up proteomics enables a systems-level analysis of proteins involved in a particular sample set. In this protocol, we describe the workflow to prepare Klebsiella pneumoniae and macrophage cells for co-culture, how to extract and prepare samples for analysis by high-resolution mass spectrometry, and lastly, how to analyze the output data files. This workflow allows for the identification of proteins involved in both the bacterial and host perspective during pathogenesis.
    Keywords:  Bacterial pathogen; Macrophage; Mass spectrometry; Quantitative proteomics
    DOI:  https://doi.org/10.1007/978-1-0716-2124-0_17
  21. Methods Mol Biol. 2022 ;2399 151-170
    Integrated Multiomics, Bioinformatics, and Computational Modeling Approaches to Central Metabolism in Organs.
    Sonia Cortassa, Pierre Villon, Steven J Sollott, Miguel A Aon.
      Data-driven research led by computational systems biology methods, encompassing bioinformatics of multiomics datasets and mathematical modeling, are critical for discovery. Herein, we describe a multiomics (metabolomics-fluxomics) approach as applied to heart function in diabetes. The methodology presented has general applicability and enables the quantification of the fluxome or set of metabolic fluxes from cytoplasmic and mitochondrial compartments in central catabolic pathways of glucose and fatty acids. Additionally, we present, for the first time, a general method to reduce the dimension of detailed kinetic, and in general stoichiometric models of metabolic networks at the steady state, to facilitate their optimization and avoid numerical problems. Representative results illustrate the powerful mechanistic insights that can be gained from this integrative and quantitative methodology.
    Keywords:  Diabetes; Fluxomics; Glucose and fatty acids catabolism; Heart; Kinetic modeling; Metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-1831-8_7
  22. STAR Protoc. 2022 Jun 17. 3(2): 101403
    NADH-independent enzymatic assay to quantify extracellular and intracellular L-lactate levels.
    Cyrielle L Bouchez, Thomas Daubon, Arnaud Mourier.
      Lactate is a central metabolite in energy metabolism and is also involved in cell signaling and epigenetic regulations. Here, we describe an NADH-independent enzymatic assay allowing rapid, selective, and sensitive quantification of L-lactate down to the pmol range. We detail lactate extraction from intracellular and extracellular fractions, followed by total protein amount determination and enzymatic assay. This approach allows quantification of intracellular and extracellular L-lactate levels, validated by treating adherent and non-adherent cells with inhibitors of lactate transporters (MCT).
    Keywords:  Cell Biology; Cell culture; Metabolism; Protein Biochemistry
    DOI:  https://doi.org/10.1016/j.xpro.2022.101403
  23. J Mass Spectrom Adv Clin Lab. 2022 Apr;24 100-106
    Retrospective analysis of metabolite patterns of clobazam and N-desmethylclobazam in human plasma by LC-MS/MS.
    Amol O Bajaj, Diane Ly, Kamisha L Johnson-Davis.
      Introduction: Clobazam is a benzodiazepine drug, used to treat Lennox-Gastaut syndrome in patients aged 2 years and older.Objective: To support patient care, our laboratory developed a liquid chromatography tandem mass spectrometry (LC-MS/MS) method for the quantification of clobazam (CLB) and its major active metabolite N-desmethylclobazam (N-CLB) in human plasma or serum samples.
    Methods: The chromatographic separation was achieved with an Agilent Zorbax Eclipse Plus C-18 RRHD column with mobile phase consisting of 0.05% formic acid in 5 mM ammonium formate, pH 3.0 and 0.1% formic acid in acetonitrile at a flow rate of 600 µL/minute and an injection volume of 5 µL. The detection was performed on a triple quadrupole mass spectrometer in multiple reaction monitoring mode to monitor precursor-to-product ion transitions in positive electrospray ionization mode.
    Results: The method was validated over a concentration range of 20-2000 ng/mL for CLB and 200-10,000 ng/mL for N-CLB. The lower limit of quantification was 20 ng/mL for CLB and 200 ng/mL for N-CLB with good accuracy and precision. The method performance was successfully evaluated by comparison with two different external laboratories. Retrospective data analysis was performed to evaluate the positivity rate and metabolic patterns for clobazam from our patient population, as a reference laboratory. Among the positive samples, both parent and metabolite were detected in 96.4% of the samples.
    Conclusion: The method was developed to support therapeutic drug monitoring and the data generated from retrospective analysis could be useful for result interpretation in conjunction with clinical patient information.
    Keywords:  CLB, Clobazam; CLIA, Clinical Laboratory Improvement Amendment; CLRW, Clinical Laboratory Reagent Water; Clobazam; DAD, Diode Array Detector; ESI, Electrospray ionization; IRB, Institutional Review Board; LC-MS/MS; LC-MS/MS, liquid chromatography tandem mass spectrometry; LLOQ, lower limit of quantification; LOD, limit of detection; MRM, multiple reaction monitoring; N-CLB, N-desmethylclobazam; N-Desmethylclobazam; Plasma; Retrospective data analysis; TDM, Therapeutic drug monitoring; ULOQ, upper limit of quantification; UV, Ultraviolet
    DOI:  https://doi.org/10.1016/j.jmsacl.2022.04.005
  24. J Proteome Res. 2022 May 23.
    Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area.
    Marco Tognetti, Kamil Sklodowski, Sebastian Müller, Dominique Kamber, Jan Muntel, Roland Bruderer, Lukas Reiter.
      The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancers. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma. In the cohort, we identified 2732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the diseased state. For pancreatic cancer, a separation by stage was achieved. Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling.
    Keywords:  SWATH; cancer; clinical proteomics; data-independent acquisition; depletion; high throughput; label-free quantification; library; plasma proteomics; single shot; stable isotope-based quantification
    DOI:  https://doi.org/10.1021/acs.jproteome.2c00122
  25. Metabolites. 2022 May 17. pii: 450. [Epub ahead of print]12(5):
    An Update on Sphingolipidomics: Is Something Still Missing? Some Considerations on the Analysis of Complex Sphingolipids and Free-Sphingoid Bases in Plasma and Red Blood Cells.
    Camillo Morano, Aida Zulueta, Anna Caretti, Gabriella Roda, Rita Paroni, Michele Dei Cas.
      The main concerns in targeted "sphingolipidomics" are the extraction and proper handling of biological samples to avoid interferences and achieve a quantitative yield well representing all the sphingolipids in the matrix. Our work aimed to compare different pre-analytical procedures and to evaluate a derivatization step for sphingoid bases quantification, to avoid interferences and improve sensitivity. We tested four protocols for the extraction of sphingolipids from human plasma, at different temperatures and durations, and two derivatization procedures for the conversion of sphingoid bases into phenylthiourea derivatives. Different columns and LC-MS/MS chromatographic conditions were also tested. The protocol that worked better for sphingolipids analysis involved a single-phase extraction in methanol/chloroform mixture (2:1, v/v) for 1 h at 38 °C, followed by a 2 h alkaline methanolysis at 38 °C, for the suppression of phospholipids signals. The derivatization of sphingoid bases promotes the sensibility of non-phosphorylated species but we proved that it is not superior to a careful choice of the appropriate column and a full-length elution gradient. Our procedure was eventually validated by analyzing plasma and erythrocyte samples of 20 volunteers. While both extraction and methanolysis are pivotal steps, our final consideration is to analyze sphingolipids and sphingoid bases under different chromatographic conditions, minding the interferences.
    Keywords:  lipidomics; mass spectrometry; sphingoid bases; sphingolipidomics; sphingolipids
    DOI:  https://doi.org/10.3390/metabo12050450
  26. Curr Protoc. 2022 May;2(5): e454
    Targeted Metabolomics Using LC-MS in Neurospora crassa.
    Alexander J Carrillo, Lida Halilovic, Manhoi Hur, Jay S Kirkwood, Katherine A Borkovich.
      The filamentous fungus Neurospora crassa has historically been a model for understanding the relationship between genes and metabolism-auxotrophic mutants of N. crassa were used by Beadle and Tatum to develop the one-gene-one-enzyme hypothesis for which they earned the Nobel Prize in 1958. In the ensuing decades, several techniques have been developed for the systematic analysis of metabolites in N. crassa and other fungi. Untargeted and targeted approaches have been used, with a focus on secondary metabolites over primary metabolism. Here, we describe a pipeline for sample preparation, metabolite extraction, Liquid Chromatography-Mass Spectrometry (LC-MS), and data analysis that can be used for targeted metabolomics of primary metabolites in N. crassa. Liquid cultures are grown with shaking in a defined minimal medium and then collected using filtration. Samples are lyophilized for 2 days at -80°C, pulverized, and mixed with a solution to extract polar metabolites. The metabolites are separated and identified using LC-MS, with downstream analysis using Skyline interpretive software. Relative levels of hundreds of metabolites can be detected and compared across strains. © 2022 Wiley Periodicals LLC. Basic Protocol: Metabolite extraction and detection from Neurospora crassa cell cultures using Liquid Chromatography-Mass Spectrometry.
    Keywords:  LC-MS; Metabolomics; Neurospora crassa
    DOI:  https://doi.org/10.1002/cpz1.454
  27. Nucleic Acids Res. 2022 May 24. pii: gkac383. [Epub ahead of print]
    CFM-ID 4.0 - a web server for accurate MS-based metabolite identification.
    Fei Wang, Dana Allen, Siyang Tian, Eponine Oler, Vasuk Gautam, Russell Greiner, Thomas O Metz, David S Wishart.
      The CFM-ID 4.0 web server (https://cfmid.wishartlab.com) is an online tool for predicting, annotating and interpreting tandem mass (MS/MS) spectra of small molecules. It is specifically designed to assist researchers pursuing studies in metabolomics, exposomics and analytical chemistry. More specifically, CFM-ID 4.0 supports the: 1) prediction of electrospray ionization quadrupole time-of-flight tandem mass spectra (ESI-QTOF-MS/MS) for small molecules over multiple collision energies (10 eV, 20 eV, and 40 eV); 2) annotation of ESI-QTOF-MS/MS spectra given the structure of the compound; and 3) identification of a small molecule that generated a given ESI-QTOF-MS/MS spectrum at one or more collision energies. The CFM-ID 4.0 web server makes use of a substantially improved MS fragmentation algorithm, a much larger database of experimental and in silico predicted MS/MS spectra and improved scoring methods to offer more accurate MS/MS spectral prediction and MS/MS-based compound identification. Compared to earlier versions of CFM-ID, this new version has an MS/MS spectral prediction performance that is ∼22% better and a compound identification accuracy that is ∼35% better on a standard (CASMI 2016) testing dataset. CFM-ID 4.0 also features a neutral loss function that allows users to identify similar or substituent compounds where no match can be found using CFM-ID's regular MS/MS-to-compound identification utility. Finally, the CFM-ID 4.0 web server now offers a much more refined user interface that is easier to use, supports molecular formula identification (from MS/MS data), provides more interactively viewable data (including proposed fragment ion structures) and displays MS mirror plots for comparing predicted with observed MS/MS spectra. These improvements should make CFM-ID 4.0 much more useful to the community and should make small molecule identification much easier, faster, and more accurate.
    DOI:  https://doi.org/10.1093/nar/gkac383
  28. J Nutr Biochem. 2022 May 21. pii: S0955-2863(22)00122-X. [Epub ahead of print] 109051
    Metabolomes of Lewis lung carcinoma metastases and normal lung tissue from mice fed different diets.
    Lin Yan, Sneha Sundaram, Bret M Rust, Matthew J Picklo, Michael R Bukowski.
      Metastasis is a devastating aspect of cancer. This study tested the hypothesis that metabolome of metastases differs from that of host organs by using the spontaneous metastasis model of Lewis lung carcinoma (LLC). In a 2 × 2 design, male C57BL/6 mice with or without a subcutaneous LLC inoculation were fed the standard AIN93G diet or a high-fat diet (HFD) for 12 weeks. Lung metastases from injected mice and the lungs from non-injected mice were harvested at the end of study for untargeted metabolomics of primary metabolism by using gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). We identified 91 metabolites for metabolomic analysis. The analysis demonstrated that amino acid and energy metabolism were altered the most in LLC metastases compared to the lungs. A 60% decrease in glutamine and a 25-fold elevation in sorbitol were observed in metastases. Cholesterol and its metabolite dihydrocholesterol were 50% lower in metastases than in the lungs. The HFD elevated arachidonic acid and its precursor linoleic acid in the lungs from non-cancer-bearing mice, reflecting the dietary fatty acid composition of the HFD. This elevation did not occur in metastases from HFD-fed LLC-bearing mice, suggesting alterations in lipid metabolism during LLC metastatic progression. Differences in metabolome between pulmonary LLC metastases and the normal healthy lungs can be useful in designing targeted studies for prevention and treatment of cancer spread using this LLC spontaneous metastasis model.
    Keywords:  Lewis lung carcinoma; diet; metabolome; metastasis; mice
    DOI:  https://doi.org/10.1016/j.jnutbio.2022.109051
  29. Metabolites. 2022 May 17. pii: 447. [Epub ahead of print]12(5):
    Metabolomic Analysis of Plasma from Breast Cancer Patients Using Ultra-High-Performance Liquid Chromatography Coupled with Mass Spectrometry: An Untargeted Study.
    Patricia A Da Cunha, Diana Nitusca, Luisa Matos Do Canto, Rency S Varghese, Habtom W Ressom, Shawna Willey, Catalin Marian, Bassem R Haddad.
      Breast cancer (BC) is one of the leading causes of cancer mortality in women worldwide, and therefore, novel biomarkers for early disease detection are critically needed. We performed herein an untargeted plasma metabolomic profiling of 55 BC patients and 55 healthy controls (HC) using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS). Pre-processed data revealed 2494 ions in total. Data matrices' paired t-tests revealed 792 ions (both positive and negative) which presented statistically significant changes (FDR &lt; 0.05) in intensity levels between cases versus controls. Metabolites identified with putative names via MetaboQuest using MS/MS and mass-based approaches included amino acid esters (i.e., N-stearoyl tryptophan, L-arginine ethyl ester), dipeptides (ile-ser, met-his), nitrogenous bases (i.e., uracil derivatives), lipid metabolism-derived molecules (caproleic acid), and exogenous compounds from plants, drugs, or dietary supplements. LASSO regression selected 16 metabolites after several variables (TNM Stage, Grade, smoking status, menopausal status, and race) were adjusted. A predictive conditional logistic regression model on the 16 LASSO selected ions provided a high diagnostic performance with an area-under-the-curve (AUC) value of 0.9729 (95% CI 0.96-0.98) on all 55 samples. This study proves that BC possesses a specific metabolic signature that could be exploited as a novel metabolomics-based approach for BC detection and characterization. Future studies of large-scale cohorts are needed to validate these findings.
    Keywords:  UHPLC/Q-TOF-MS; biomarkers; breast cancer; diagnostic; metabolomics
    DOI:  https://doi.org/10.3390/metabo12050447
  30. Gut. 2022 May 27. pii: gutjnl-2021-325117. [Epub ahead of print]
    Lysosomal lipid switch sensitises to nutrient deprivation and mTOR targeting in pancreatic cancer.
    Maria Chiara De Santis, Luca Gozzelino, Jean Piero Margaria, Andrea Costamagna, Edoardo Ratto, Federico Gulluni, Enza Di Gregorio, Erica Mina, Nicla Lorito, Marina Bacci, Rossano Lattanzio, Gianluca Sala, Paola Cappello, Francesco Novelli, Elisa Giovannetti, Caterina Vicentini, Silvia Andreani, Pietro Delfino, Vincenzo Corbo, Aldo Scarpa, Paolo Ettore Porporato, Andrea Morandi, Emilio Hirsch, Miriam Martini.
      OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with limited therapeutic options. However, metabolic adaptation to the harsh PDAC environment can expose liabilities useful for therapy. Targeting the key metabolic regulator mechanistic target of rapamycin complex 1 (mTORC1) and its downstream pathway shows efficacy only in subsets of patients but gene modifiers maximising response remain to be identified.DESIGN: Three independent cohorts of PDAC patients were studied to correlate PI3K-C2γ protein abundance with disease outcome. Mechanisms were then studied in mouse (KPC mice) and cellular models of PDAC, in presence or absence of PI3K-C2γ (WT or KO). PI3K-C2γ-dependent metabolic rewiring and its impact on mTORC1 regulation were assessed in conditions of limiting glutamine availability. Finally, effects of a combination therapy targeting mTORC1 and glutamine metabolism were studied in WT and KO PDAC cells and preclinical models.
    RESULTS: PI3K-C2γ expression was reduced in about 30% of PDAC cases and was associated with an aggressive phenotype. Similarly, loss of PI3K-C2γ in KPC mice enhanced tumour development and progression. The increased aggressiveness of tumours lacking PI3K-C2γ correlated with hyperactivation of mTORC1 pathway and glutamine metabolism rewiring to support lipid synthesis. PI3K-C2γ-KO tumours failed to adapt to metabolic stress induced by glutamine depletion, resulting in cell death.
    CONCLUSION: Loss of PI3K-C2γ prevents mTOR inactivation and triggers tumour vulnerability to RAD001 (mTOR inhibitor) and BPTES/CB-839 (glutaminase inhibitors). Therefore, these results might open the way to personalised treatments in PDAC with PI3K-C2γ loss.
    Keywords:  AMINO ACIDS; CELL BIOLOGY; LIPID METABOLISM; PANCREATIC CANCER; SIGNAL TRANSDUCTION
    DOI:  https://doi.org/10.1136/gutjnl-2021-325117
  31. Metabolites. 2022 May 10. pii: 426. [Epub ahead of print]12(5):
    Towards Unbiased Evaluation of Ionization Performance in LC-HRMS Metabolomics Method Development.
    Carsten Jaeger, Jan Lisec.
      As metabolomics increasingly finds its way from basic science into applied and regulatory environments, analytical demands on nontargeted mass spectrometric detection methods continue to rise. In addition to improved chemical comprehensiveness, current developments aim at enhanced robustness and repeatability to allow long-term, inter-study, and meta-analyses. Comprehensive metabolomics relies on electrospray ionization (ESI) as the most versatile ionization technique, and recent liquid chromatography-high resolution mass spectrometry (LC-HRMS) instrumentation continues to overcome technical limitations that have hindered the adoption of ESI for applications in the past. Still, developing and standardizing nontargeted ESI methods and instrumental setups remains costly in terms of time and required chemicals, as large panels of metabolite standards are needed to reflect biochemical diversity. In this paper, we investigated in how far a nontargeted pilot experiment, consisting only of a few measurements of a test sample dilution series and comprehensive statistical analysis, can replace conventional targeted evaluation procedures. To examine this potential, two instrumental ESI ion source setups were compared, reflecting a common scenario in practical method development. Two types of feature evaluations were performed, (a) summary statistics solely involving feature intensity values, and (b) analyses additionally including chemical interpretation. Results were compared in detail to a targeted evaluation of a large metabolite standard panel. We reflect on the advantages and shortcomings of both strategies in the context of current harmonization initiatives in the metabolomics field.
    Keywords:  chemical classification; electrospray ionization; feature statistics; liquid chromatography-high resolution mass spectrometry; method development; method harmonization; nontargeted analysis; quality control
    DOI:  https://doi.org/10.3390/metabo12050426
  32. Nat Rev Cancer. 2022 May 25.
    Developing dietary interventions as therapy for cancer.
    Samuel R Taylor, John N Falcone, Lewis C Cantley, Marcus D Goncalves.
      Cancer cells acquire distinct metabolic preferences based on their tissue of origin, genetic alterations and degree of interaction with systemic hormones and metabolites. These adaptations support the increased nutrient demand required for increased growth and proliferation. Diet is the major source of nutrients for tumours, yet dietary interventions lack robust evidence and are rarely prescribed by clinicians for the treatment of cancer. Well-controlled diet studies in patients with cancer are rare, and existing studies have been limited by nonspecific enrolment criteria that inappropriately grouped together subjects with disparate tumour and host metabolic profiles. This imprecision may have masked the efficacy of the intervention for appropriate candidates. Here, we review the metabolic alterations and key vulnerabilities that occur across multiple types of cancer. We describe how these vulnerabilities could potentially be targeted using dietary therapies including energy or macronutrient restriction and intermittent fasting regimens. We also discuss recent trials that highlight how dietary strategies may be combined with pharmacological therapies to treat some cancers, potentially ushering a path towards precision nutrition for cancer.
    DOI:  https://doi.org/10.1038/s41568-022-00485-y
  33. Methods Mol Biol. 2022 ;2456 29-51
    Isolation of Detergent Insoluble Proteins from Mouse Brain Tissue for Quantitative Analysis Using Data Independent Acquisition (DIA).
    Cristen Molzahn, Lorenz Nierves, Philipp F Lange, Thibault Mayor.
      Enrichment of detergent insoluble proteins is a commonly used technique for analyzing proteins that may be aggregating in disease or with age. However, various methods for enriching for these proteins are used. Here we present a method using a mild detergent (Triton X-100) and high centrifugation speed (20,000 × g) allowing for sufficient protein extraction and enrichment for large protein assemblies. Digestion is performed on columns allowing for a methanol chloroform wash to remove the highly prevalent lipids in brain tissue. This is followed by analysis by data independent acquisition mass spectrometry, which we have found to be highly reproducible. Our method is intended to enrich for amorphous aggregates, which may accumulate upon the collapse of protein homeostasis.
    Keywords:  Aging; Mass spectrometry; Neurodegenerative diseases; Protein aggregation
    DOI:  https://doi.org/10.1007/978-1-0716-2124-0_3
  34. Metabolites. 2022 Apr 21. pii: 377. [Epub ahead of print]12(5):
    Diversion of Acetyl CoA to 3-Methylglutaconic Acid Caused by Discrete Inborn Errors of Metabolism.
    Dylan E Jones, Elizabeth A Jennings, Robert O Ryan.
      A growing number of inborn errors of metabolism (IEM) have been identified that manifest 3-methylglutaconic (3MGC) aciduria as a phenotypic feature. In primary 3MGC aciduria, IEM-dependent deficiencies in leucine pathway enzymes prevent catabolism of trans-3MGC CoA. Consequently, this metabolite is converted to 3MGC acid and excreted in urine. In secondary 3MGC aciduria, however, no leucine metabolism pathway enzyme deficiencies exist. These IEMs affect mitochondrial membrane structure, electron transport chain function or ATP synthase subunits. As a result, acetyl CoA oxidation via the TCA cycle slows and acetyl CoA is diverted to trans-3MGC CoA, and then to 3MGC acid. Whereas the trans diastereomer of 3MGC CoA is the only biologically relevant diastereomer, the urine of affected subjects contains both cis- and trans-3MGC acids. Studies have revealed that trans-3MGC CoA is susceptible to isomerization to cis-3MGC CoA. Once formed, cis-3MGC CoA undergoes intramolecular cyclization, forming an anhydride that, upon hydrolysis, yields cis-3MGC acid. Alternatively, cis-3MGC anhydride can acylate protein lysine side chains. Once formed, cis-3MGCylated proteins can be deacylated by the NAD+-dependent enzyme, sirtuin 4. Taken together, the excretion of 3MGC acid in secondary 3MGC aciduria represents a barometer of defective mitochondrial function.
    Keywords:  3-methylglutaconic acid; acetyl CoA; inborn errors of metabolism; leucine; mitochondria; organic aciduria; sirtuin 4
    DOI:  https://doi.org/10.3390/metabo12050377
  35. Methods Mol Biol. 2022 ;2456 85-94
    N-Terminomics/TAILS of Tissue and Liquid Biopsies.
    Anthonia Anowai, Sameeksha Chopra, Barbara Mainoli, Daniel Young, Antoine Dufour.
      The N-terminomics approach of Terminal Amine Isotopic Labeling of Substrates (TAILS) enables the identification and quantification of natural and neo-N-termini of proteins using liquid chromatography and tandem mass spectrometry (LC-MS/MS). This methodology has been used to study protease function and identify protease substrates in cell culture systems, animal disease models, and more recently, has been applied to clinical samples. Here, we present the application of TAILS to tissue and liquid biopsies.
    Keywords:  Biopsy; Mass spectrometry; N-terminomics; Protease; Proteomics; TAILS
    DOI:  https://doi.org/10.1007/978-1-0716-2124-0_7
  36. Nature. 2022 May 25.
    Defining mitochondrial protein functions through deep multiomic profiling.
    Jarred W Rensvold, Evgenia Shishkova, Yuriy Sverchkov, Ian J Miller, Arda Cetinkaya, Angela Pyle, Mateusz Manicki, Dain R Brademan, Yasemin Alanay, Julian Raiman, Adam Jochem, Paul D Hutchins, Sean R Peters, Vanessa Linke, Katherine A Overmyer, Austin Z Salome, Alexander S Hebert, Catherine E Vincent, Nicholas W Kwiecien, Matthew J P Rush, Michael S Westphall, Mark Craven, Nurten A Akarsu, Robert W Taylor, Joshua J Coon, David J Pagliarini.
      Mitochondria are epicentres of eukaryotic metabolism and bioenergetics. Pioneering efforts in recent decades have established the core protein componentry of these organelles1 and have linked their dysfunction to more than 150 distinct disorders2,3. Still, hundreds of mitochondrial proteins lack clear functions4, and the underlying genetic basis for approximately 40% of mitochondrial disorders remains unresolved5. Here, to establish a more complete functional compendium of human mitochondrial proteins, we profiled more than 200 CRISPR-mediated HAP1 cell knockout lines using mass spectrometry-based multiomics analyses. This effort generated approximately 8.3 million distinct biomolecule measurements, providing a deep survey of the cellular responses to mitochondrial perturbations and laying a foundation for mechanistic investigations into protein function. Guided by these data, we discovered that PIGY upstream open reading frame (PYURF) is an S-adenosylmethionine-dependent methyltransferase chaperone that supports both complex I assembly and coenzyme Q biosynthesis and is disrupted in a previously unresolved multisystemic mitochondrial disorder. We further linked the putative zinc transporter SLC30A9 to mitochondrial ribosomes and OxPhos integrity and established RAB5IF as the second gene harbouring pathogenic variants that cause cerebrofaciothoracic dysplasia. Our data, which can be explored through the interactive online MITOMICS.app resource, suggest biological roles for many other orphan mitochondrial proteins that still lack robust functional characterization and define a rich cell signature of mitochondrial dysfunction that can support the genetic diagnosis of mitochondrial diseases.
    DOI:  https://doi.org/10.1038/s41586-022-04765-3
  37. Biomedicines. 2022 May 20. pii: 1189. [Epub ahead of print]10(5):
    Lipidomics Analysis of Free Fatty Acids in Human Plasma of Healthy and Diabetic Subjects by Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS).
    Maroula G Kokotou, Christiana Mantzourani, Charikleia S Batsika, Olga G Mountanea, Ioanna Eleftheriadou, Ourania Kosta, Nikolaos Tentolouris, George Kokotos.
      Targeted analytical methods for the determination of free fatty acids (FFAs) in human plasma are of high interest because they may help in identifying biomarkers for diseases and in monitoring the progress of a disease. The determination of FFAs is of particular importance in the case of metabolic disorders because FFAs have been associated with diabetes. We present a liquid chromatography-high resolution mass spectrometry (LC-HRMS) method, which allows the simultaneous determination of 74 FFAs in human plasma. The method is fast (10-min run) and straightforward, avoiding any derivatization step and tedious sample preparation. A total of 35 standard saturated and unsaturated FFAs, as well as 39 oxygenated (either hydroxy or oxo) saturated FFAs, were simultaneously detected and quantified in plasma samples from 29 subjects with type 2 diabetes mellitus (T2D), 14 with type 1 diabetes mellitus (T1D), and 28 healthy subjects. Alterations in the levels of medium-chain FFAs (C6:0 to C10:0) were observed between the control group and T2D and T1D patients.
    Keywords:  LC-HRMS; diabetes; free fatty acids; hydroxy fatty acids; oxo fatty acids
    DOI:  https://doi.org/10.3390/biomedicines10051189
  38. Methods Mol Biol. 2022 ;2399 61-84
    Automating Assignment, Quantitation, and Biological Annotation of Redox Proteomics Datasets with ProteoSushi.
    Sjoerd van der Post, Robert W Seymour, Arshag D Mooradian, Jason M Held.
      Redox proteomics plays an increasingly important role characterizing the cellular redox state and redox signaling networks. As these datasets grow larger and identify more redox regulated sites in proteins, they provide a systems-wide characterization of redox regulation across cellular organelles and regulatory networks. However, these large proteomic datasets require substantial data processing and analysis in order to fully interpret and comprehend the biological impact of oxidative posttranslational modifications. We therefore developed ProteoSushi, a software tool to biologically annotate and quantify redox proteomics and other modification-specific proteomics datasets. ProteoSushi can be applied to differentially alkylated samples to assay overall cysteine oxidation, chemically labeled samples such as those used to profile the cysteine sulfenome, or any oxidative posttranslational modification on any residue.Here we demonstrate how to use ProteoSushi to analyze a large, public cysteine redox proteomics dataset. ProteoSushi assigns each modified peptide to shared proteins and genes, sums or averages signal intensities for each modified site of interest, and annotates each modified site with the most up-to-date biological information available from UniProt. These biological annotations include known functional roles or modifications of the site, the protein domain(s) that the site resides in, the protein's subcellular location and function, and more.
    Keywords:  Bioinformatics; Cysteines; Posttranslational modifications; Protein inference; ProteoSushi; Proteomics; Reactive oxygen species; Redox; Systems biology
    DOI:  https://doi.org/10.1007/978-1-0716-1831-8_4
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