bims-mascan 2022-10-30 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2022–10–30
forty-two papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Tag you're it: Application of stable isotope labeling and LC-MS to identify the precursors of specialized metabolites in plants.
Regulation of Lipid Metabolism Under Stress and Its Role in Cancer.
Integrating Multiple Quantitative Proteomic Analyses Using MetaMSD.
Hepatic glutamine synthetase controls N5-methylglutamine in homeostasis and cancer.
Coisolation of Peptide Pairs for Peptide Identification and MS/MS-Based Quantification.
An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome.
ADViSELipidomics: a workflow for analyzing lipidomics data.
Generation of 13C-Labeled Inositol and Inositol Phosphates by Stable Isotope Labeling Cell Culture for Quantitative Metabolomics.
LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data.
Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar.
High Sensitivity Limited Material Proteomics Empowered by Data-Independent Acquisition on Linear Ion Traps.
Reprogramming Carbohydrate Metabolism in Cancer and Its Role in Regulating the Tumor Microenvironment.
Enhanced Proteomic Data Analysis with MetaMorpheus.
Fast, Free, and Flexible Peptide and Protein Quantification with FlashLFQ.
inSPIRE: An open-source tool for increased mass spectrometry identification rates using Prosit spectral prediction.
Molecular Classification of Genes Associated with Hypoxic Lipid Metabolism in Pancreatic Cancer.
Validation of MS/MS Identifications and Label-Free Quantification Using Proline.
Uncertainty-Aware Protein-Level Quantification and Differential Expression Analysis of Proteomics Data with seaMass.
Integrated Quantitative Targeted Lipidomics and Proteomics Reveal Unique Fingerprints of Multiple Metabolic Conditions.
Elevated pentose phosphate pathway flux supports appendage regeneration.
Lipids for CD8+ TILs: Beneficial or harmful?
Exogenous detection of 13C-glucose metabolism in tumor and diet-induced obesity models.
Using targeted metabolomics to elucidate the indole auxin network in plants.
A Pipeline for Peptide Detection Using Multiple Decoys.
Amino acid metabolism in primary bone sarcomas.
Metabolic Regulation of Lysine Acetylation: Implications in Cancer.
Evaluation and correction of injection order effects in LC-MS/MS based targeted metabolomics.
A benchmarking protocol for intact protein-level Tandem Mass Tag (TMT) labeling for quantitative top-down proteomics.
Rewired Metabolism of Amino Acids and Its Roles in Glioma Pathology.
Assessing the Potential of Untargeted SWATH Mass Spectrometry-Based Metabolomics to Differentiate Closely Related Exposures in Observational Studies.
Tracing Carbon Metabolism with Stable Isotope Metabolomics Reveals the Legacy of Diverse Carbon Sources in Soil.
MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets.
Statistical Analysis of Post-Translational Modifications Quantified by Label-Free Proteomics Across Multiple Biological Conditions with R: Illustration from SARS-CoV-2 Infected Cells.
Serum Metabolomics Profiling Reveals Metabolic Alterations Prior to a Diagnosis with Non-Small Cell Lung Cancer among Chinese Community Residents: A Prospective Nested Case-Control Study.
Epigenetic Reprogramming of the Glucose Metabolic Pathways by the Chromatin Effectors During Cancer.
Targeting amino acid metabolism in cancer.
Limiting glutamine utilization activates a GCN2/TRAIL-R2/Caspase-8 apoptotic pathway in glutamine-addicted tumor cells.
Multi-Omics Reveals Mechanisms of Partial Modulation of COVID-19 Dysregulation by Glucocorticoid Treatment.
A system suitability testing platform for untargeted, high-resolution mass spectrometry.
Molecular Network Guided Cataloging of the Secondary Metabolome of Selected Egyptian Red Sea Soft Corals.
Ferroptosis inhibition by lysosome-dependent catabolism of extracellular protein.
Ion mobility-resolved phosphoproteomics with dia-PASEF and short gradients.

Methods Enzymol. 2022 ;pii: S0076-6879(22)00303-2. [Epub ahead of print]676 279-303

Tag you're it: Application of stable isotope labeling and LC-MS to identify the precursors of specialized metabolites in plants.

Jeffrey Simpson, Clint Chapple.

  Untargeted liquid chromatography/mass spectrometry (LC-MS) can contribute a comprehensive and unbiased picture of the metabolic space of plants. These data can be used to quantify natural metabolite variation for genome wide association studies, to compare global metabolic responses from environmental or genetic perturbations, and to identify previously undescribed metabolites in Nature. A major limitation with untargeted metabolomics is the classification and identification of the thousands of metabolite features that can be detected in a single analytical run. Isotopic labeling improves the informational value of these datasets by categorizing metabolites as being derived from specific upstream precursors and/or to known metabolic pathways. When a 13C-labeled precursor is fed to either a plant or tissue, the downstream metabolites produced from it have a higher m/z value than the molecules in the pre-existing pool, generating an m/z peak pair that can be specifically identified within the MS data. This paper outlines methods and principles to consider when supplementing untargeted MS data with isotopic labeling, including how to choose the appropriate isotopic label, grow and feed plant tissues to maximize label uptake and incorporation into derivatives, optimize LC-MS methods, and interpret the resulting labeling data. Although the focus here is on annotation of amino acid-derived metabolites using LC-MS, we anticipate that the methods are generally adaptable to other precursors, plant species, and chromatographic approaches.

Keywords:  Amino acids; Isotopic labeling; LC-MS; Specialized metabolites; Untargeted metabolomics

DOI:  https://doi.org/10.1016/bs.mie.2022.07.039
Subcell Biochem. 2022 ;100 81-113

Regulation of Lipid Metabolism Under Stress and Its Role in Cancer.

Rimsha Munir, Nousheen Zaidi.

  Within the tumor microenvironment, cancer cells are often exposed to oxygen and nutrient deficiency, leading to various changes in their lipid composition and metabolism. These alterations have important therapeutic implications as they affect the cancer cells' survival, membrane dynamics, and therapy response. This chapter provides an overview of recent insights into the regulation of lipid metabolism in cancer cells under metabolic stress. We discuss how this metabolic adaptation helps cancer cells thrive in a harsh tumor microenvironment.

Keywords:  Cancer; De Novo Fatty acid synthesis; Fatty acid synthase; Hypoxia; Lipid metabolism; Lipidomic profiles; Metabolic stress; Nutrient deprivation

DOI:  https://doi.org/10.1007/978-3-031-07634-3_3
Methods Mol Biol. 2023 ;2426 361-374

Integrating Multiple Quantitative Proteomic Analyses Using MetaMSD.

So Young Ryu, Miriam P Yun, Sujung Kim.

  MetaMSD is a proteomic software that integrates multiple quantitative mass spectrometry data analysis results using statistical summary combination approaches. By utilizing this software, scientists can combine results from their pilot and main studies to maximize their biomarker discovery while effectively controlling false discovery rates. It also works for combining proteomic datasets generated by different labeling techniques and/or different types of mass spectrometry instruments. With these advantages, MetaMSD enables biological researchers to explore various proteomic datasets in public repositories to discover new biomarkers and generate interesting hypotheses for future studies. In this protocol, we provide a step-by-step procedure on how to install and perform a meta-analysis for quantitative proteomics using MetaMSD.

Keywords:  Integrating multiple differential analyses; Mass spectrometry data analysis; Meta-analysis; Proteomic software; Quantitative proteomics

DOI:  https://doi.org/10.1007/978-1-0716-1967-4_16
Nat Chem Biol. 2022 Oct 24.

Hepatic glutamine synthetase controls N5-methylglutamine in homeostasis and cancer.

Victor H Villar, Maria Francesca Allega, Ruhi Deshmukh, Tobias Ackermann, Mark A Nakasone, Johan Vande Voorde, Thomas M Drake, Janina Oetjen, Algernon Bloom, Colin Nixon, Miryam Müller, Stephanie May, Ee Hong Tan, Lars Vereecke, Maude Jans, Gillian Blancke, Daniel J Murphy, Danny T Huang, David Y Lewis, Thomas G Bird, Owen J Sansom, Karen Blyth, David Sumpton, Saverio Tardito.

Glutamine synthetase (GS) activity is conserved from prokaryotes to humans, where the ATP-dependent production of glutamine from glutamate and ammonia is essential for neurotransmission and ammonia detoxification. Here, we show that mammalian GS uses glutamate and methylamine to produce a methylated glutamine analog, N5-methylglutamine. Untargeted metabolomics revealed that liver-specific GS deletion and its pharmacological inhibition in mice suppress hepatic and circulating levels of N5-methylglutamine. This alternative activity of GS was confirmed in human recombinant enzyme and cells, where a pathogenic mutation in the active site (R324C) promoted the synthesis of N5-methylglutamine over glutamine. N5-Methylglutamine is detected in the circulation, and its levels are sustained by the microbiome, as demonstrated by using germ-free mice. Finally, we show that urine levels of N5-methylglutamine correlate with tumor burden and GS expression in a β-catenin-driven model of liver cancer, highlighting the translational potential of this uncharacterized metabolite.

DOI: https://doi.org/10.1038/s41589-022-01154-9
Anal Chem. 2022 Oct 28.

Coisolation of Peptide Pairs for Peptide Identification and MS/MS-Based Quantification.

Ian R Smith, Jimmy K Eng, Anthony S Barente, Alexander Hogrebe, Ariadna Llovet, Ricard A Rodriguez-Mias, Judit Villén.

Stable-isotope labeling with amino acids in cell culture (SILAC)-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data-dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a DDA strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. This peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions. This study shows the feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

DOI: https://doi.org/10.1021/acs.analchem.2c01711
J Vis Exp. 2022 Sep 20.

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome.

Dehua Li, Junze Liang, Yongjian Zhang, Gong Zhang.

Untargeted metabolomics techniques are being widely used in recent years. However, the rapidly increasing throughput and number of samples create an enormous amount of spectra, setting challenges for quality control of the mass spectrometry spectra. To reduce the false positives, false discovery rate (FDR) quality control is necessary. Recently, we developed a software for FDR control of untargeted metabolome identification that is based on a Target-Decoy strategy named XY-Meta. Here, we demonstrated a complete analysis pipeline that integrates XY-Meta and metaX together. This protocol shows how to use XY-meta to generate a decoy database from an existing reference database and perform FDR control using the Target-Decoy strategy for large-scale metabolome identification on an open-access dataset. The differential analysis and metabolites annotation were performed after running metaX for metabolites peaks detection and quantitation. In order to help more researchers, we also developed a user-friendly cloud-based analysis platform for these analyses, without the need for bioinformatics skills or any computer languages.

DOI: https://doi.org/10.3791/63625
Bioinformatics. 2022 Oct 29. pii: btac706. [Epub ahead of print]

ADViSELipidomics: a workflow for analyzing lipidomics data.

Eugenio Del Prete, Ana Margarida Campos, Fabio Della Rocca, Carmela Gallo, Angelo Fontana, Genoveffa Nuzzo, Claudia Angelini.

SUMMARY: ADViSELipidomics is a novel Shiny app for preprocessing, analyzing, and visualizing lipidomics data. It handles the outputs from LipidSearch and LIQUID for lipid identification and quantification and the data from the Metabolomics Workbench. ADViSELipidomics extracts information by parsing lipid species (using LIPID MAPS classification) and, together with information available on the samples, performs several exploratory and statistical analyses. When the experiment includes internal lipid standards, ADViSELipidomics can normalize the data matrix, providing normalized concentration values per lipids and samples. Moreover, it identifies differentially abundant lipids in simple and complex experimental designs, dealing with batch effect correction. Finally, ADViSELipidomics has a user-friendly Graphical User Interface (GUI) and supports an extensive series of interactive graphics.
AVAILABILITY AND IMPLEMENTATION: ADViSELipidomics is freely available at https://github.com/ShinyFabio/ADViSELipidomics.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btac706
Anal Chem. 2022 Oct 28.

Generation of 13C-Labeled Inositol and Inositol Phosphates by Stable Isotope Labeling Cell Culture for Quantitative Metabolomics.

Peng Li, Michael Lämmerhofer.

Inositol and inositol phosphates (IPx) are central metabolites. Their accurate quantitative analysis in complex biological samples is challenging due to lengthy sample preparation procedures, sample losses by strong adsorption to surfaces, and unpredictable matrix effects. Currently, U13C-inositol and U13C-IPx are not available from commercial sources. In this study, we developed a method that is capable of generating U13C-inositol and U13C-IPx. An inositol-independent cell line L929S was cultured in inositol-free medium supplemented with U13C-glucose. Inositol contamination in FBS was observed as the critical parameter for labeling efficiency (LE). A balance between cell growth and LE was achieved by adopting a two-step labeling strategy. In the first step, a LE of 90% could be obtained by normal cell growth in the long-term. Cells were then cultured in a second step in ultra-labeling medium for improved LE for a short duration before harvesting. The generated U13Canalogs were of high isotopic purity (>99%). Utilized as internal standards spiked before sample preparation in biological applications, U13Canalogs can effectively compensate sample loss during sample preparation as well as the matrix effect during electrospray ionization. An exemplary pharmacological study was conducted with phospholipase C inhibitor and activator to document the great utility of the prepared stable isotope-labeled internal standards in elucidating the PLC-dependent IP code. U13CIPx are used as internal standards to generate quantitative profiles of IPx in HeLa cell samples after treatment with PLC inhibitor and activator. This established method generating U13Canalogs is cost-effective, robust, and reproducible, which can facilitate quantitative studies of inositol and IPx in biological scenarios.

DOI: https://doi.org/10.1021/acs.analchem.2c02819
Brief Bioinform. 2022 Oct 21. pii: bbac455. [Epub ahead of print]

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data.

Qingxia Yang, Bo Li, Panpan Wang, Jicheng Xie, Yuhao Feng, Ziqiang Liu, Feng Zhu.

  Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

Keywords:  data integration; data processing; large-scale metabolomics; marker identification; metabolite annotation

DOI:  https://doi.org/10.1093/bib/bbac455
Methods Mol Biol. 2023 ;2426 163-196

Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar.

Marianne Tardif, Enora Fremy, Anne-Marie Hesse, Thomas Burger, Yohann Couté, Samuel Wieczorek.

  Prostar is a software tool dedicated to the processing of quantitative data resulting from mass spectrometry-based label-free proteomics. Practically, once biological samples have been analyzed by bottom-up proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, notably by means of precursor ion chromatogram integration. From that point, the classical workflows aggregate these pieces of peptide-level information to infer protein-level identities and amounts. Finally, protein abundances can be statistically analyzed to find out proteins that are significantly differentially abundant between compared conditions. Prostar original workflow has been developed based on this strategy. However, recent works have demonstrated that processing peptide-level information is often more accurate when searching for differentially abundant proteins, as the aggregation step tends to hide some of the data variabilities and biases. As a result, Prostar has been extended by workflows that manage peptide-level data, and this protocol details their use. The first one, deemed "peptidomics," implies that the differential analysis is conducted at peptide level, independently of the peptide-to-protein relationship. The second workflow proposes to aggregate the peptide abundances after their preprocessing (i.e., after filtering, normalization, and imputation), so as to minimize the amount of protein-level preprocessing prior to differential analysis.

Keywords:  Data processing; Differential analysis; Label-free proteomics; Relative quantification; Statistical software

DOI:  https://doi.org/10.1007/978-1-0716-1967-4_9
J Proteome Res. 2022 Oct 26.

High Sensitivity Limited Material Proteomics Empowered by Data-Independent Acquisition on Linear Ion Traps.

Teeradon Phlairaharn, Samuel Grégoire, Lukas R Woltereck, Valdemaras Petrosius, Benjamin Furtwängler, Brian C Searle, Erwin M Schoof.

  In recent years, the concept of cell heterogeneity in biology has gained increasing attention, concomitant with a push toward technologies capable of resolving such biological complexity at the molecular level. For single-cell proteomics using Mass Spectrometry (scMS) and low-input proteomics experiments, the sensitivity of an orbitrap mass analyzer can sometimes be limiting. Therefore, low-input proteomics and scMS could benefit from linear ion traps, which provide faster scanning speeds and higher sensitivity than an orbitrap mass analyzer, however at the cost of resolution. We optimized an acquisition method that combines the orbitrap and linear ion trap, as implemented on a tribrid instrument, while taking advantage of the high-field asymmetric waveform ion mobility spectrometry (FAIMS) pro interface, with a prime focus on low-input applications. First, we compared the performance of orbitrap- versus linear ion trap mass analyzers. Subsequently, we optimized critical method parameters for low-input measurement by data-independent acquisition on the linear ion trap mass analyzer. We conclude that linear ion traps mass analyzers combined with FAIMS and Whisper flow chromatography are well-tailored for low-input proteomics experiments, and can simultaneously increase the throughput and sensitivity of large-scale proteomics experiments where limited material is available, such as clinical samples and cellular subpopulations.

Keywords:  FAIMS-MS; data acquisition; low-input applications; mass spectrometry; peptide identification optimization; ultrasensitive proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.2c00376
Subcell Biochem. 2022 ;100 3-65

Reprogramming Carbohydrate Metabolism in Cancer and Its Role in Regulating the Tumor Microenvironment.

Swagata Adhikari, Deblina Guha, Chitra Mohan, Shravanti Mukherjee, Jessica K Tyler, Chandrima Das.

  Altered metabolism has become an emerging feature of cancer cells impacting their proliferation and metastatic potential in myriad ways. Proliferating heterogeneous tumor cells are surrounded by other resident or infiltrating cells, along with extracellular matrix proteins, and other secretory factors constituting the tumor microenvironment. The diverse cell types of the tumor microenvironment exhibit different molecular signatures that are regulated at their genetic and epigenetic levels. The cancer cells elicit intricate crosstalks with these supporting cells, exchanging essential metabolites which support their anabolic processes and can promote their survival, proliferation, EMT, angiogenesis, metastasis and even therapeutic resistance. In this context, carbohydrate metabolism ensures constant energy supply being a central axis from which other metabolic and biosynthetic pathways including amino acid and lipid metabolism and pentose phosphate pathway are diverged. In contrast to normal cells, increased glycolytic flux is a distinguishing feature of the highly proliferative cancer cells, which supports them to adapt to a hypoxic environment and also protects them from oxidative stress. Such rewired metabolic properties are often a result of epigenetic alterations in the cancer cells, which are mediated by several factors including, DNA, histone and non-histone protein modifications and non-coding RNAs. Conversely, epigenetic landscapes of the cancer cells are also dictated by their diverse metabolomes. Altogether, this metabolic and epigenetic interplay has immense potential for the development of efficient anti-cancer therapeutic strategies. In this book chapter we emphasize upon the significance of reprogrammed carbohydrate metabolism in regulating the tumor microenvironment and cancer progression, with an aim to explore the different metabolic and epigenetic targets for better cancer treatment.

Keywords:  Acetyl-CoA; Carbohydrate metabolism; DNMTs; Epigenetics; Glycolytic flux; HATs; HDACs; HDM; HMT; Hypoxia; Metabolic reprogramming; Metastasis; OXPHOS; Oncometabolites; SAM; Tumor microenvironment

DOI:  https://doi.org/10.1007/978-3-031-07634-3_1
Methods Mol Biol. 2023 ;2426 35-66

Enhanced Proteomic Data Analysis with MetaMorpheus.

Rachel M Miller, Robert J Millikin, Zach Rolfs, Michael R Shortreed, Lloyd M Smith.

  MetaMorpheus is a free and open-source software program dedicated to the comprehensive analysis of proteomic data. In bottom-up proteomics, protein samples are digested into peptides prior to chromatographic separation and tandem mass spectrometric analysis. The resulting fragmentation spectra are subsequently analyzed with search software programs to obtain peptide identifications and infer the presence of proteins in the samples. MetaMorpheus seeks to maximize the information gleaned from proteomic data through the use of (a) mass calibration, (b) post-translational modification discovery, (c) multiple search algorithms, which aid in the analysis of data from traditional, crosslinking, and glycoproteomic experiments, (d) isotope-based or label-free quantification, (e) multi-protease protein inference, and (f) spectral annotation and data visualization capabilities. This protocol provides detailed descriptions of how use MetaMorpheus and how to customize data analysis workflows using MetaMorpheus tasks to meet the specific needs of the user.

Keywords:  Bottom-up; Crosslink; Database search; Glycopeptides; Open-source; Post-translational modification discovery; Proteomics; Tandem mass spectrometry

DOI:  https://doi.org/10.1007/978-1-0716-1967-4_3
Methods Mol Biol. 2023 ;2426 303-313

Fast, Free, and Flexible Peptide and Protein Quantification with FlashLFQ.

Robert J Millikin, Michael R Shortreed, Mark Scalf, Lloyd M Smith.

  The rapid and accurate quantification of peptides is a critical element of modern proteomics that has become increasingly challenging as proteomic data sets grow in size and complexity. We present here FlashLFQ, a computer program for high-speed label-free quantification of peptides and proteins following a search of bottom-up mass spectrometry data. FlashLFQ is approximately an order of magnitude faster than established label-free quantification methods and can quantify data-dependent analysis (DDA) search results from any proteomics search program. It is available as a graphical user interface program, a command line tool, a Docker image, and integrated into the MetaMorpheus search software.

Keywords:  Label-free quantification; Post-translational modifications; Quantitative proteomics; Software

DOI:  https://doi.org/10.1007/978-1-0716-1967-4_13
Mol Cell Proteomics. 2022 Oct 21. pii: S1535-9476(22)00240-7. [Epub ahead of print] 100432

inSPIRE: An open-source tool for increased mass spectrometry identification rates using Prosit spectral prediction.

John A Cormican, Yehor Horokhovskyi, Wai Tuck Soh, Michele Mishto, Juliane Liepe.

  Rescoring of mass spectrometry (MS) search results using spectral predictors can strongly increase Peptide Spectrum Match (PSM) identification rates. This approach is particularly effective when aiming to search MS data against large databases, for example when dealing with non-specific cleavage in immunopeptidomics or inflation of the reference database for noncanonical peptide identification. Here, we present inSPIRE (in silico Spectral Predictor Informed REscoring), a flexible and performant open-source rescoring pipeline built on Prosit MS spectral prediction, which is compatible with common database search engines. inSPIRE allows large scale rescoring with data from multiple MS search files, increases sensitivity to minor differences in amino acid residue position, and can be applied to various MS sample types, including tryptic proteome digestions and immunopeptidomes. inSPIRE boosts PSM identification rates in immunopeptidomics, leading to better performance than the original Prosit rescoring pipeline, as confirmed by benchmarking of inSPIRE performance on ground truth datasets. The integration of various features in the inSPIRE backbone further boosts the PSM identification in immunopeptidomics, with a potential benefit for the identification of noncanonical peptides.

Keywords:  Percolator; Prosit; mass spectrometry; rescoring

DOI:  https://doi.org/10.1016/j.mcpro.2022.100432
Biomolecules. 2022 Oct 21. pii: 1533. [Epub ahead of print]12(10):

Molecular Classification of Genes Associated with Hypoxic Lipid Metabolism in Pancreatic Cancer.

Yaning Li, Xinyue Liang, Gang Che, Yutong Chen, Lisi Luo, Kecheng Liu, Rongzhi Xie, Linjuan Zeng.

  Abnormal lipid metabolism often occurs under hypoxic microenvironment, which is an important energy supplement for cancer cell proliferation and metastasis. We aimed to explore the lipid metabolism characteristics and gene expression features of pancreatic ductal adenocarcinoma (PDAC) related to hypoxia and identify biomarkers for molecular classification based on hypoxic lipid metabolism that are evaluable for PDAC prognosis and therapy. The multiple datasets were analyzed integratively, including corresponding clinical information of samples. PDAC possesses a distinct metabolic profile and oxygen level compared with normal pancreatic tissues, according to the bioinformatics methods. In addition, a study on untargeted metabolomics using Ultra Performance Liquid Chromatography Tandem Mass Spectrometry(UPLC-MS) revealed lipid metabolites differences affected by oxygen. Analysis of PDAC gene expression profiling in The Cancer Genome Atlas (TCGA) revealed that the sphingolipid process correlates closely with HIF1α. According to the characters of HIF-1 and sphingolipid, samples can be clustered into three subgroups using non-negative matrix factorization clustering. In cluster2, patients had an increased survival time. Relatively high MUC16 mutation arises in cluster2 and may positively influence the cancer survival rates. This study explored the expression pattern of lipid metabolism under hypoxia microenvironment in PDAC. On the basis of metabolic signatures, we identified the prognosis subtypes linking lipid metabolism to hypoxia. The classifications may be conducive to developing personalized treatment programs targeting metabolic profiles.

Keywords:  HIF-1α; hypoxia lipid metabolism; pancreatic cancer; sphingolipid

DOI:  https://doi.org/10.3390/biom12101533
Methods Mol Biol. 2023 ;2426 67-89

Validation of MS/MS Identifications and Label-Free Quantification Using Proline.

Véronique Dupierris, Anne-Marie Hesse, Jean-Philippe Menetrey, David Bouyssié, Thomas Burger, Yohann Couté, Christophe Bruley.

  In the proteomics field, the production and publication of reliable mass spectrometry (MS)-based label-free quantitative results is a major concern. Due to the intrinsic complexity of bottom-up proteomics experiments (requiring aggregation of data relating to both precursor and fragment peptide ions into protein information, and matching this data across samples), inaccuracies and errors can occur throughout the data-processing pipeline. In a classical label-free quantification workflow, the validation of identification results is critical since errors made at this first stage of the workflow may have an impact on the following steps and therefore on the final result. Although false discovery rate (FDR) of the identification is usually controlled by using the popular target-decoy method, it has been demonstrated that this method can sometimes lead to inaccurate FDR estimates. This protocol shows how Proline can be used to validate identification results by using the method based on the Benjamini-Hochberg procedure and then quantify the identified ions and proteins in a single software environment providing data curation capabilities and computational efficiency.

Keywords:  Benjamini–Hochberg false discovery rate; Data visualization; Discovery proteomics; Label-free quantification; Mass spectrometry software; Software engineering; Statistics

DOI:  https://doi.org/10.1007/978-1-0716-1967-4_4
Methods Mol Biol. 2023 ;2426 141-162

Uncertainty-Aware Protein-Level Quantification and Differential Expression Analysis of Proteomics Data with seaMass.

Alexander M Phillips, Richard D Unwin, Simon J Hubbard, Andrew W Dowsey.

  seaMass is an R package for protein-level quantification, normalization, and differential expression analysis of proteomics mass spectrometry data after peptide identification, protein grouping, and feature-level quantification. Using the concept of a blocked experimental design, seaMass can analyze all common discovery proteomics paradigms, including label-free (e.g., Waters Progenesis input), SILAC (e.g., MaxQuant input), isotope labelling (e.g., SCIEX ProteinPilot iTraq and Thermo ProteomeDiscoverer TMT input), and data-independent acquisition (e.g., OpenSWATH-PyProphet input), and is able to scale to study with hundreds of assays or more. By utilizing hierarchical Bayesian modelling, seaMass assesses the quantification reliability of each feature and peptide across assays so that only those in consensus influence the resulting protein group quantification strongly. Similarly, unexplained variation in each individual assay is captured, providing both a metric for quality control and automatic down-weighting of suspect assays. To achieve this, each protein group-level quantification outputted by seaMass is accompanied by the standard deviation of its posterior uncertainty. Moreover, seaMass integrates a flexible differential expression analysis subsystem with false discovery rate control based on the popular MCMCglmm package for Bayesian mixed-effects modelling, and also provides uncertainty-aware principal components analysis. We provide a description for using seaMass to perform an end-to-end analysis using a real dataset associated with a published clinical proteomics study.

Keywords:  Bayesian modelling; Differential expression analysis; False discovery rate control; Protein quantification; Quantitative proteomics

DOI:  https://doi.org/10.1007/978-1-0716-1967-4_8
Biomolecules. 2022 Oct 08. pii: 1439. [Epub ahead of print]12(10):

Integrated Quantitative Targeted Lipidomics and Proteomics Reveal Unique Fingerprints of Multiple Metabolic Conditions.

Anna A Ivanova, Jon C Rees, Bryan A Parks, Michael Andrews, Michael Gardner, Eunice Grigorutsa, Zsuzsanna Kuklenyik, James L Pirkle, John R Barr.

  Aberrations in lipid and lipoprotein metabolic pathways can lead to numerous diseases, including cardiovascular disease, diabetes, neurological disorders, and cancer. The integration of quantitative lipid and lipoprotein profiling of human plasma may provide a powerful approach to inform early disease diagnosis and prevention. In this study, we leveraged data-driven quantitative targeted lipidomics and proteomics to identify specific molecular changes associated with different metabolic risk categories, including hyperlipidemic, hypercholesterolemic, hypertriglyceridemic, hyperglycemic, and normolipidemic conditions. Based on the quantitative characterization of serum samples from 146 individuals, we have determined individual lipid species and proteins that were significantly up- or down-regulated relative to the normolipidemic group. Then, we established protein-lipid topological networks for each metabolic category and linked dysregulated proteins and lipids with defined metabolic pathways. To evaluate the differentiating power of integrated lipidomics and proteomics data, we have built an artificial neural network model that simultaneously and accurately categorized the samples from each metabolic risk category based on the determined lipidomics and proteomics profiles. Together, our findings provide new insights into molecular changes associated with metabolic risk conditions, suggest new condition-specific associations between apolipoproteins and lipids, and may inform new biomarker discovery in lipid metabolism-associated disorders.

Keywords:  artificial neural network classification; dyslipidemias; lipidomics; network analysis; proteomics

DOI:  https://doi.org/10.3390/biom12101439
Cell Rep. 2022 Oct 25. pii: S2211-1247(22)01408-5. [Epub ahead of print]41(4): 111552

Elevated pentose phosphate pathway flux supports appendage regeneration.

Jeet H Patel, Daniel J Ong, Claire R Williams, LuLu K Callies, Andrea E Wills.

  A fundamental step in regeneration is rapid growth to replace lost tissue. Cells must generate sufficient lipids, nucleotides, and proteins to fuel rapid cell division. To define metabolic pathways underlying regenerative growth, we undertake a multimodal investigation of metabolic reprogramming in Xenopus tropicalis appendage regeneration. Regenerating tissues have increased glucose uptake; however, inhibition of glycolysis does not decrease regeneration. Instead, glucose is funneled to the pentose phosphate pathway (PPP), which is essential for full tail regeneration. Liquid chromatography-mass spectrometry (LC-MS) metabolite profiling reveals increased nucleotide and nicotinamide intermediates required for cell division. Using single-cell RNA sequencing (scRNA-seq), we find that highly proliferative cells have increased transcription of PPP enzymes and not glycolytic enzymes. Further, PPP inhibition results in decreased cell division specifically in regenerating tissue. Our results inform a model wherein regenerating tissues direct glucose toward the PPP, yielding nucleotide precursors to drive regenerative cell proliferation.

Keywords:  CP: Developmental biology; CP: Metabolism; Xenopus tropicalis; glucose metabolism; glycolysis; pentose phosphate pathway; proliferation; regeneration

DOI:  https://doi.org/10.1016/j.celrep.2022.111552
Front Immunol. 2022 ;13 1020422

Lipids for CD8+ TILs: Beneficial or harmful?

Duojiao Wu, Yuwen Chen.

  Lipids and lipid metabolism play crucial roles in regulating T cell function and are tightly related to the establishment of immune memory. It is reported that tumor-infiltrating CD8+T lymphocytes (CD8+TILs) burn fats to restore their impaired effector function due to the lack of glucose. Conversely, fatty acids (FAs) and cholesterol in the tumor microenvironment (TME) drive the CD8+ TILs dysfunction. The origin of dysfunctional CD8+ TILs shares important features with memory T cell's precursor, but whether lipids and/or lipid metabolism reprogramming directly influence the memory plasticity of dysfunctional CD8+ TILs remains elusive. It is necessary to understand the interplay between cellular lipid metabolism and dysfunction of CD8+ TILs in the case of targeting T cell's metabolism to synergize cancer immunotherapy. Therefore, in this review, we summarize the latest research on CD8+ TILs lipid metabolism, evaluate the impacts of lipids in the TME to CD8+ TILs, and highlight the significance of promoting memory phenotype cell formation by targeting CD8+ T cells lipid metabolism to provide longer duration of cancer immunotherapy efficacy.

Keywords:  CD8+ T cell; Exhausted T cell; cholesterol; fatty acid; fatty acid oxidation; metabolism; tumor microenvironment (TME)

DOI:  https://doi.org/10.3389/fimmu.2022.1020422
Front Physiol. 2022 ;13 1023614

Exogenous detection of 13C-glucose metabolism in tumor and diet-induced obesity models.

Amandine Verlande, Sung Kook Chun, Wei A Song, Daniela Oettler, Harm J Knot, Selma Masri.

  Metabolic rewiring is a hallmark feature prevalent in cancer cells as well as insulin resistance (IR) associated with diet-induced obesity (DIO). For instance, tumor metabolism shifts towards an enhanced glycolytic state even under aerobic conditions. In contrast, DIO triggers lipid-induced IR by impairing insulin signaling and reducing insulin-stimulated glucose uptake. Based on physiological differences in systemic metabolism, we used a breath analysis approach to discriminate between different pathological states using glucose oxidation as a readout. We assessed glucose utilization in lung cancer-induced cachexia and DIO mouse models using a U-13C glucose tracer and stable isotope sensors integrated into an indirect calorimetry system. Our data showed increased 13CO2 expired by tumor-bearing (TB) mice and a reduction in exhaled 13CO2 in the DIO model. Taken together, our findings illustrate high glucose uptake and consumption in TB animals and decreased glucose uptake and oxidation in obese mice with an IR phenotype. Our work has important translational implications for the utility of stable isotopes in breath-based detection of glucose homeostasis in models of lung cancer progression and DIO.

Keywords:  circadian clock; diet-induced obesity (DIO); glucose oxidation and detection; insulin resistance; tumor metabolism

DOI:  https://doi.org/10.3389/fphys.2022.1023614
Methods Enzymol. 2022 ;pii: S0076-6879(22)00302-0. [Epub ahead of print]676 239-278

Using targeted metabolomics to elucidate the indole auxin network in plants.

Jerry D Cohen, Qian Tang, Adrian D Hegeman.

  The plant hormone auxin plays important roles throughout the entire life span of a plant and facilitates its adaptation to a changing environment. Multiple metabolic pathways intersect to control the levels and flux through indole-3-acetic acid (IAA), the primary auxin in most plant species. Measurement of changes in these pathways represents an important objective to understanding core aspects of auxin signal regulation. Such studies have become approachable through the technologies encompassed by targeted metabolomics. By monitoring incorporation of stable isotopes from labeled precursors into proposed intermediates, it is possible to trace pathway utilization and characterize new biosynthetic routes to auxin. Chemical inhibitors that target specific steps or entire pathways related to auxin synthesis aid these techniques. Here we describe methods for obtaining stable isotope labeled pathway intermediates necessary for pathway analysis and quantification of compounds. We describe how to use isotope dilution with methods employing either gas chromatography or high performance liquid chromatography mass spectrometry techniques for sensitive analysis of IAA. Complete biosynthetic pathway analysis in seedlings using multiple stable isotope-labeled precursors and chemical inhibitors coupled with highly sensitive liquid chromatography-mass spectrometry methods are described that allow rapid measurement of isotopic flux into biochemical pools. These methods should prove to be useful to researchers studying aspects of the auxin metabolic network in vivo in a variety of plant tissues and during various environmental conditions.

Keywords:  Auxin biosynthesis; Isotope dilution analysis; Mass spectrometry; Metabolic inhibitors; Pathway analysis; Stable isotope labeling

DOI:  https://doi.org/10.1016/bs.mie.2022.07.038
Methods Mol Biol. 2023 ;2426 25-34

A Pipeline for Peptide Detection Using Multiple Decoys.

Syamand Hasam, Kristen Emery, William Stafford Noble, Uri Keich.

  Target-decoy competition has been commonly used for over a decade to control the false discovery rate when analyzing tandem mass spectrometry (MS/MS) data. We recently developed a framework that uses multiple decoys to increase the number of detected peptides in MS/MS data. Here, we present a pipeline of Apache licensed, open-source software that allows the user to readily take advantage of our framework.

Keywords:  FDR control; Multiple decoys; Peptide detection; Tandem mass spectrometry

DOI:  https://doi.org/10.1007/978-1-0716-1967-4_2
Front Oncol. 2022 ;12 1001318

Amino acid metabolism in primary bone sarcomas.

Jennifer A Jiménez, Elizabeth R Lawlor, Costas A Lyssiotis.

  Primary bone sarcomas, including osteosarcoma (OS) and Ewing sarcoma (ES), are aggressive tumors with peak incidence in childhood and adolescence. The intense standard treatment for these patients consists of combined surgery and/or radiation and maximal doses of chemotherapy; a regimen that has not seen improvement in decades. Like other tumor types, ES and OS are characterized by dysregulated cellular metabolism and a rewiring of metabolic pathways to support the biosynthetic demands of malignant growth. Not only are cancer cells characterized by Warburg metabolism, or aerobic glycolysis, but emerging work has revealed a dependence on amino acid metabolism. Aside from incorporation into proteins, amino acids serve critical functions in redox balance, energy homeostasis, and epigenetic maintenance. In this review, we summarize current studies describing the amino acid metabolic requirements of primary bone sarcomas, focusing on OS and ES, and compare these dependencies in the normal bone and malignant tumor contexts. We also examine insights that can be gleaned from other cancers to better understand differential metabolic susceptibilities between primary and metastatic tumor microenvironments. Lastly, we discuss potential metabolic vulnerabilities that may be exploited therapeutically and provide better-targeted treatments to improve the current standard of care.

Keywords:  Ewing sarcoma; amino acid metabolism; osteoblast; osteoclast; sarcoma; tumor metabolism

DOI:  https://doi.org/10.3389/fonc.2022.1001318
Subcell Biochem. 2022 ;100 393-426

Metabolic Regulation of Lysine Acetylation: Implications in Cancer.

Siddharth Singh, Parijat Senapati, Tapas K Kundu.

  Lysine acetylation is the second most well-studied post-translational modification after phosphorylation. While phosphorylation regulates signaling cascades, one of the most significant roles of acetylation is regulation of chromatin structure. Acetyl-coenzyme A (acetyl-CoA) serves as the acetyl group donor for acetylation reactions mediated by lysine acetyltransferases (KATs). On the other hand, NAD+ serves as the cofactor for lysine deacetylases (KDACs). Both acetyl-CoA and NAD+ are metabolites integral to energy metabolism, and therefore, their metabolic flux can regulate the activity of KATs and KDACs impacting the epigenome. In this chapter, we review our current understanding of how metabolic pathways regulate lysine acetylation in normal and cancer cells.

Keywords:  Acetyl-CoA; Acetylation; Epigenome; Metabolic reprogramming; Metabolism; NAD+; Oral cancer

DOI:  https://doi.org/10.1007/978-3-031-07634-3_12
J Chromatogr B Analyt Technol Biomed Life Sci. 2022 Oct 19. pii: S1570-0232(22)00418-4. [Epub ahead of print]1212 123513

Evaluation and correction of injection order effects in LC-MS/MS based targeted metabolomics.

Yang Yue, Xun Bao, Jun Jiang, Jing Li.

  For large-scale and long-term metabolomics studies that involve a large batch or multiple batches of analyses, batch effects cause nonbiological systematic biases that may lead to false positive or false negative findings. Quantitative monitoring and correction of batch effects is critical to the development of reproducible and robust metabolomics platforms either for untargeted or targeted analyses. To achieve sufficient retention and separation of a broad range of metabolites with diverse chemical structures and physicochemical properties, LC-MS/MS based targeted metabolomics often involves 3 complemented chromatographic separation methods, including reversed-phase liquid chromatography (RP-LC), hydrophilic interaction liquid chromatography (HILIC), and ion-pair liquid chromatography (IP-LC). The purpose of this study is to quantitatively evaluate intra-batch variations or injection order effects of the RP-LC, HILIC, and IP-LC methods for targeted metabolomics analyses, and develop strategies to minimize intra-batch variations and correct injection order effects for problematic metabolites. Both RP-LC and HILIC methods exhibit robust intra-batch reproducibility in 0.2 µM standard mix QC, with ∼96 % of the measured metabolites showing acceptable intra-batch variations (<20 %); whereas, the intra-batch reproducibility for some metabolites in cell matrix QC may be compromised due to stability issue, suboptimal chromatographic retention, and/or matrix effects causing ionization suppression and/or retention instability. The IP-LC method exhibits significant injection order effects, which could be effectively corrected by the developed exponential models of signal drift trends as a function of injection order for individual targeted metabolites.

Keywords:  Batch effect; Hydrophilic interaction liquid chromatography (HILIC); Injection order effect; Ion-pair liquid chromatography; LC-MS/MS based targeted metabolomics; Reversed-phase liquid chromatography

DOI:  https://doi.org/10.1016/j.jchromb.2022.123513
MethodsX. 2022 ;9 101873

A benchmarking protocol for intact protein-level Tandem Mass Tag (TMT) labeling for quantitative top-down proteomics.

Yanting Guo, Dahang Yu, Kellye A Cupp-Sutton, Xiaowen Liu, Si Wu.

  Isobaric chemical tag labeling for quantification of intact proteins in complex samples is limited due to the tendency of intact proteins precipitate under labeling conditions and increased sample complexity as a result of side products (i.e., incomplete labeling or labeling of unintended residues). To reduce precipitation under labeling conditions, we developed a technique to remove large proteoforms that allowed for the labeling and characterization of small proteoforms (<35 kDa) using top-down proteomics. We also systematically optimized protein-level Tandem Mass Tag (TMT) labeling conditions to obtain optimal labeling parameters for complex samples. Here, we present a benchmarking protocol for protein-level TMT labeling for quantitative top-down proteomics, including complex intact protein sample preparation, protein-level TMT labeling, top-down LC/MS analysis, and TMT reporter ion quantification.•An optimized protocol for protein-level TMT labeling in complex sample.•Limits production of incorrectly labeled side products for minimization of spectral complexity.•A guideline for isobaric chemical tag quantification in top-down proteomics.

Keywords:  Isobaric chemical tag; Protein-level TMT labeling; Quantification; TMT; Top-down proteomics

DOI:  https://doi.org/10.1016/j.mex.2022.101873
Metabolites. 2022 Sep 28. pii: 918. [Epub ahead of print]12(10):

Rewired Metabolism of Amino Acids and Its Roles in Glioma Pathology.

Sirui Chen, Jingjing Jiang, Ao Shen, Ying Miao, Yunfeng Cao, Ying Zhang, Peiyu Cong, Peng Gao.

  Amino acids (AAs) are indispensable building blocks of diverse bio-macromolecules as well as functional regulators for various metabolic processes. The fact that cancer cells live with a voracious appetite for specific AAs has been widely recognized. Glioma is one of the most lethal malignancies occurring in the central nervous system. The reprogrammed metabolism of AAs benefits glioma proliferation, signal transduction, epigenetic modification, and stress tolerance. Metabolic alteration of specific AAs also contributes to glioma immune escape and chemoresistance. For clinical consideration, fluctuations in the concentrations of AAs observed in specific body fluids provides opportunities to develop new diagnosis and prognosis markers. This review aimed at providing an extra dimension to understanding glioma pathology with respect to the rewired AA metabolism. A deep insight into the relevant fields will help to pave a new way for new therapeutic target identification and valuable biomarker development.

Keywords:  amino acid; biomarker; glioma; metabolism; metabolomics

DOI:  https://doi.org/10.3390/metabo12100918
Metabolites. 2022 Oct 04. pii: 942. [Epub ahead of print]12(10):

Assessing the Potential of Untargeted SWATH Mass Spectrometry-Based Metabolomics to Differentiate Closely Related Exposures in Observational Studies.

Frank Klont, Piotr Sosnowski, Daan Kremer, Tim J Knobbe, Ron Bonner, Hans Blokzijl, Rinse K Weersma, Stephan J L Bakker, TransplantLines Investigators, Eelko Hak, Daan J Touw, Gérard Hopfgartner.

  Mass spectrometry (MS) is increasingly used in clinical studies to obtain molecular evidence of chemical exposures, such as tobacco smoke, alcohol, and drugs. This evidence can help verify clinical data retrieved through anamnesis or questionnaires and may provide insights into unreported exposures, for example those classified as the same despite small but possibly relevant chemical differences or due to contaminants in reported exposure compounds. Here, we aimed to explore the potential of untargeted SWATH metabolomics to differentiate such closely related exposures. This data-independent acquisition MS-based profiling technique was applied to urine samples of 316 liver and 570 kidney transplant recipients from the TransplantLines Biobank and Cohort Study (NCT03272841), where we focused on the immunosuppressive drug mycophenolate, which is either supplied as a morpholino-ester prodrug or as an enteric-coated product, the illicit drug cocaine, which is usually supplied as an adulterated product, and the proton pump inhibitors omeprazole and esomeprazole. Based on these examples, we found that untargeted SWATH metabolomics has considerable potential to identify different (unreported) exposure or co-exposure metabolites and may determine variations in their abundances. We also found that these signals alone may sometimes be unable to distinguish closely related exposures, and enhancement of differentiation, for example by integration with pharmacogenomics data, is needed.

Keywords:  SWATH; data-independent acquisition; exposomics; liquid chromatography; mass spectrometry; metabolomics; transplantation

DOI:  https://doi.org/10.3390/metabo12100942
Appl Environ Microbiol. 2022 Oct 27. e0083922

Tracing Carbon Metabolism with Stable Isotope Metabolomics Reveals the Legacy of Diverse Carbon Sources in Soil.

Roland C Wilhelm, Samuel E Barnett, Tami L Swenson, Nicholas D Youngblut, Chantal N Koechli, Benjamin P Bowen, Trent R Northen, Daniel H Buckley.

  Tracking the metabolic activity of whole soil communities can improve our understanding of the transformation and fate of carbon in soils. We used stable isotope metabolomics to trace 13C from nine labeled carbon sources into the water-soluble metabolite pool of an agricultural soil over time. Soil was amended with a mixture of all nine sources, with one source isotopically labeled in each treatment. We compared changes in the 13C enrichment of metabolites with respect to carbon source and time over a 48-day incubation and contrasted differences between soluble sources (glucose, xylose, amino acids, etc.) and insoluble sources (cellulose and palmitic acid). Whole soil metabolite profiles varied singularly by time, while the composition of 13C-labeled metabolites differed primarily by carbon source (R2 = 0.68) rather than time (R2 = 0.07), with source-specific differences persisting throughout incubations. The 13C labeling of metabolites from insoluble carbon sources occurred slower than that from soluble sources but yielded a higher average atom percent (atom%) 13C in metabolite markers of biomass (amino acids and nucleic acids). The 13C enrichment of metabolite markers of biomass stabilized between 5 and 15 atom% 13C by the end of incubations. Temporal patterns in the 13C enrichment of tricarboxylic acid cycle intermediates, nucleobases (uracil and thymine), and by-products of DNA salvage (allantoin) closely tracked microbial activity. Our results demonstrate that metabolite production in soils is driven by the carbon source supplied to the community and that the fate of carbon in metabolites do not generally converge over time as a result of ongoing microbial processing and recycling. IMPORTANCE Carbon metabolism in soil remains poorly described due to the inherent difficulty of obtaining information on the microbial metabolites produced by complex soil communities. Our study demonstrates the use of stable isotope probing (SIP) to study carbon metabolism in soil by tracking 13C from supplied carbon sources into metabolite pools and biomass. We show that differences in the metabolism of sources influence the fate of carbon in soils. Heterogeneity in 13C-labeled metabolite profiles corresponded with compositional differences in the metabolically active populations, providing a basis for how microbial community composition correlates with the quality of soil carbon. Our study demonstrates the application of SIP-metabolomics in studying soils and identifies several metabolite markers of growth, activity, and other aspects of microbial function.

Keywords:  community metabolism; metabolomics; soil carbon cycle; stable isotope probing

DOI:  https://doi.org/10.1128/aem.00839-22
Front Bioinform. 2022 ;2 842964

MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets.

Arnaud Gaudry, Florian Huber, Louis-Félix Nothias, Sylvian Cretton, Marcel Kaiser, Jean-Luc Wolfender, Pierre-Marie Allard.

  In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g., via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods since the experimental variation-changes in chromatographical or mass spectrometric conditions - hinders the direct comparison of the profiled samples. Here we introduce MEMO-MS2 BasEd SaMple VectOrization-a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics considering fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis.

Keywords:  computational metabolomics; drug discovery; mass spectrometry; natural products; vectorization

DOI:  https://doi.org/10.3389/fbinf.2022.842964
Methods Mol Biol. 2023 ;2426 267-302

Statistical Analysis of Post-Translational Modifications Quantified by Label-Free Proteomics Across Multiple Biological Conditions with R: Illustration from SARS-CoV-2 Infected Cells.

Quentin Giai Gianetto.

  Protein post-translational modifications (PTMs) are essential elements of cellular communication. Their variations in abundance can affect cellular pathways, leading to cellular disorders and diseases. A widely used method for revealing PTM-mediated regulatory networks is their label-free quantitation (LFQ) by high-resolution mass spectrometry. The raw data resulting from such experiments are generally interpreted using specific software, such as MaxQuant, MassChroQ, or Proline for instance. They provide data matrices containing quantified intensities for each modified peptide identified. Statistical analyses are then necessary (1) to ensure that the quantified data are of good enough quality and sufficiently reproducible, (2) to highlight the modified peptides that are differentially abundant between the biological conditions under study. The objective of this chapter is therefore to provide a complete data analysis pipeline for analyzing the quantified values of modified peptides in presence of two or more biological conditions using the R software. We illustrate our pipeline starting from MaxQuant outputs dealing with the analysis of A549-ACE2 cells infected by SARS-CoV-2 at different time stamps, freely available on PRIDE (PXD020019).

Keywords:  Clustering; Data quality control; Label-free proteomics; Post-translational modifications; R; Relative quantification; Statistics

DOI:  https://doi.org/10.1007/978-1-0716-1967-4_12
Metabolites. 2022 Sep 27. pii: 906. [Epub ahead of print]12(10):

Serum Metabolomics Profiling Reveals Metabolic Alterations Prior to a Diagnosis with Non-Small Cell Lung Cancer among Chinese Community Residents: A Prospective Nested Case-Control Study.

Yu Xiang, Qi Zhao, Yilin Wu, Xing Liu, Junjie Zhu, Yuting Yu, Xuyan Su, Kelin Xu, Yonggen Jiang, Genming Zhao.

  The present high mortality of lung cancer in China stems mainly from the lack of feasible, non-invasive and early disease detection biomarkers. Serum metabolomics profiling to reveal metabolic alterations could expedite the disease detection process and suggest those patients who are harboring disease. Using a nested case-control design, we applied ultra-high-performance liquid chromatography/mass spectrometry (LC-MS)-based serum metabolomics to reveal the metabolomic alterations and to indicate the presence of non-small cell lung cancer (NSCLC) using serum samples collected prior to disease diagnoses. The studied serum samples were collected from 41 patients before a NSCLC diagnosis (within 3.0 y) and 38 matched the cancer-free controls from the prospective Shanghai Suburban Adult Cohort. The NSCLC patients markedly presented cellular metabolism alterations in serum samples collected prior to their disease diagnoses compared with the cancer-free controls. In total, we identified 18 significantly expressed metabolites whose relative abundance showed either an upward or a downward trend, with most of them being lipid and lipid-like molecules, organic acids, and nitrogen compounds. Choline metabolism in cancer, sphingolipid, and glycerophospholipid metabolism emerged as the significant metabolic disturbance of NSCLC. The metabolites involved in these biological processes may be the distinctive features associated with NSCLC prior to a diagnosis.

Keywords:  cancer cells metabolism; metabolomics; nested case-control study; non-small cell lung cancer (NSCLC)

DOI:  https://doi.org/10.3390/metabo12100906
Subcell Biochem. 2022 ;100 269-336

Epigenetic Reprogramming of the Glucose Metabolic Pathways by the Chromatin Effectors During Cancer.

Payel Mondal, Niharika Tiwary, Amrita Sengupta, Sinjini Dhang, Siddhartha Roy, Chandrima Das.

  Glucose metabolism plays a vital role in regulating cellular homeostasis as it acts as the central axis for energy metabolism, alteration in which may lead to serious consequences like metabolic disorders to life-threatening diseases like cancer. Malignant cells, on the other hand, help in tumor progression through abrupt cell proliferation by adapting to the changed metabolic milieu. Metabolic intermediates also vary from normal cells to cancerous ones to help the tumor manifestation. However, metabolic reprogramming is an important phenomenon of cells through which they try to maintain the balance between normal and carcinogenic outcomes. In this process, transcription factors and chromatin modifiers play an essential role to modify the chromatin landscape of important genes related directly or indirectly to metabolism. Our chapter surmises the importance of glucose metabolism and the role of metabolic intermediates in the cell. Also, we summarize the influence of histone effectors in reprogramming the cancer cell metabolism. An interesting aspect of this chapter includes the detailed methods to detect the aberrant metabolic flux, which can be instrumental for the therapeutic regimen of cancer.

Keywords:  Epigenetic reader; Extracellular flux analysis; Gluconeogenesis; Glucose metabolism; Glycolysis; Hepatocellular carcinoma; Histone modification; Hypoxia; Metabolic intermediates; OXPHOS; TCA cycle; Techniques to measure; Warburg effect

DOI:  https://doi.org/10.1007/978-3-031-07634-3_9
Int Rev Cell Mol Biol. 2022 ;pii: S1937-6448(22)00109-5. [Epub ahead of print]373 37-79

Targeting amino acid metabolism in cancer.

Lucie Safrhansova, Katerina Hlozkova, Julia Starkova.

  Metabolic rewiring is a characteristic hallmark of cancer cells. This phenomenon sustains uncontrolled proliferation and resistance to apoptosis by increasing nutrients and energy supply. However, reprogramming comes together with vulnerabilities that can be used against tumor and can be applied in targeted therapy. In the last years, the genetic background of tumors has been identified thoroughly and new therapies targeting those mutations tested. Nevertheless, we propose that targeting the phenotype of cancer cells could be another way of treatment aiming to avoid drug resistance and non-responsiveness of cancer patients. Amino acid metabolism is part of the altered processes in cancer cells. Amino acids are building blocks and also sensors of signaling pathways regulating main biological processes. In this comprehensive review, we described four amino acids (asparagine, arginine, methionine, and cysteine) which have been actively investigated as potential targets for anti-tumor therapy. Asparagine depletion is successfully used for decades in the treatment of acute lymphoblastic leukemia and there is a strong implication to apply it to other types of tumors. Arginine auxotrophic tumors are great candidates for arginine-starvation therapy. Higher requirement for essential amino acids such as methionine and cysteine point out promising targetable weaknesses of cancer cells.

Keywords:  Amino acid metabolism; Arginine; Asparagine; Cancer; Cysteine; Methionine; Targeted therapy

DOI:  https://doi.org/10.1016/bs.ircmb.2022.08.001
Cell Death Dis. 2022 Oct 27. 13(10): 906

Limiting glutamine utilization activates a GCN2/TRAIL-R2/Caspase-8 apoptotic pathway in glutamine-addicted tumor cells.

Rosario Yerbes, Rocío Mora-Molina, F Javier Fernández-Farrán, Laura Hiraldo, Abelardo López-Rivas, Carmen Palacios.

Oncogenic transformation leads to changes in glutamine metabolism that make transformed cells highly dependent on glutamine for anabolic growth and survival. Herein, we investigated the cell death mechanism activated in glutamine-addicted tumor cells in response to the limitation of glutamine metabolism. We show that glutamine starvation triggers a FADD and caspase-8-dependent and mitochondria-operated apoptotic program in tumor cells that involves the pro-apoptotic TNF-related apoptosis-inducing ligand receptor 2 (TRAIL-R2), but is independent of its cognate ligand TRAIL. In glutamine-depleted tumor cells, activation of the amino acid-sensing general control nonderepressible-2 kinase (GCN2) is responsible for TRAIL-R2 upregulation, caspase-8 activation, and apoptotic cell death. Interestingly, GCN2-dependent ISR signaling induced by methionine starvation also leads to TRAIL-R2 upregulation and apoptosis. Moreover, pharmacological inhibition of transaminases activates a GCN2 and TRAIL-R2-dependent apoptotic mechanism that is inhibited by non-essential amino acids (NEAA). In addition, metabolic stress upon glutamine deprivation also results in GCN2-independent FLICE-inhibitory protein (FLIP) downregulation facilitating caspase-8 activation and apoptosis. Importantly, downregulation of the long FLIP splice form (FLIPL) and apoptosis upon glutamine deprivation are inhibited in the presence of a membrane-permeable α-ketoglutarate. Collectively, our data support a model in which limiting glutamine utilization in glutamine-addicted tumor cells triggers a previously unknown cell death mechanism regulated by GCN2 that involves the TRAIL-R2-mediated activation of the extrinsic apoptotic pathway.

DOI: https://doi.org/10.1038/s41419-022-05346-y
Int J Mol Sci. 2022 Oct 11. pii: 12079. [Epub ahead of print]23(20):

Multi-Omics Reveals Mechanisms of Partial Modulation of COVID-19 Dysregulation by Glucocorticoid Treatment.

Matt Spick, Amy Campbell, Ivona Baricevic-Jones, Johanna von Gerichten, Holly-May Lewis, Cecile F Frampas, Katie Longman, Alexander Stewart, Deborah Dunn-Walters, Debra J Skene, Nophar Geifman, Anthony D Whetton, Melanie J Bailey.

  Treatments for COVID-19 infections have improved dramatically since the beginning of the pandemic, and glucocorticoids have been a key tool in improving mortality rates. The UK's National Institute for Health and Care Excellence guidance is for treatment to be targeted only at those requiring oxygen supplementation, however, and the interactions between glucocorticoids and COVID-19 are not completely understood. In this work, a multi-omic analysis of 98 inpatient-recruited participants was performed by quantitative metabolomics (using targeted liquid chromatography-mass spectrometry) and data-independent acquisition proteomics. Both 'omics datasets were analysed for statistically significant features and pathways differentiating participants whose treatment regimens did or did not include glucocorticoids. Metabolomic differences in glucocorticoid-treated patients included the modulation of cortisol and bile acid concentrations in serum, but no alleviation of serum dyslipidemia or increased amino acid concentrations (including tyrosine and arginine) in the glucocorticoid-treated cohort relative to the untreated cohort. Proteomic pathway analysis indicated neutrophil and platelet degranulation as influenced by glucocorticoid treatment. These results are in keeping with the key role of platelet-associated pathways and neutrophils in COVID-19 pathogenesis and provide opportunity for further understanding of glucocorticoid action. The findings also, however, highlight that glucocorticoids are not fully effective across the wide range of 'omics dysregulation caused by COVID-19 infections.

Keywords:  COVID-19; dexamethasone; glucocorticoid; mass spectrometry; metabolomics; multi-omics; proteomics

DOI:  https://doi.org/10.3390/ijms232012079
Front Mol Biosci. 2022 ;9 1026184

A system suitability testing platform for untargeted, high-resolution mass spectrometry.

Andrei Dmitrenko, Michelle Reid, Nicola Zamboni.

  The broad coverage of untargeted metabolomics poses fundamental challenges for the harmonization of measurements along time, even if they originate from the very same instrument. Internal isotopic standards can hardly cover the chemical complexity of study samples. Therefore, they are insufficient for normalizing data a posteriori as done for targeted metabolomics. Instead, it is crucial to verify instrument's performance a priori, that is, before samples are injected. Here, we propose a system suitability testing platform for time-of-flight mass spectrometers independent of liquid chromatography. It includes a chemically defined quality control mixture, a fast acquisition method, software for extracting ca. 3,000 numerical features from profile data, and a simple web service for monitoring. We ran a pilot for 21 months and present illustrative results for anomaly detection or learning causal relationships between the spectral features and machine settings. Beyond mere detection of anomalies, our results highlight several future applications such as 1) recommending instrument retuning strategies to achieve desired values of quality indicators, 2) driving preventive maintenance, and 3) using the obtained, detailed spectral features for posterior data harmonization.

Keywords:  analytical chemistry; mass spectrometry; metabolomics; quality assurance; quality control

DOI:  https://doi.org/10.3389/fmolb.2022.1026184
Mar Drugs. 2022 Oct 01. pii: 630. [Epub ahead of print]20(10):

Molecular Network Guided Cataloging of the Secondary Metabolome of Selected Egyptian Red Sea Soft Corals.

Nesrine M Hegazi, Tarik A Mohamed, Hamada H Saad, Montaser A Al-Hammady, Taha A Hussien, Mohamed-Elamir F Hegazy, Harald Gross.

  Soft corals are recognized as an abundant source of diverse secondary metabolites with unique chemical features and physiologic capabilities. However, the discovery of these metabolites is usually hindered by the traditional protocol which requires a large quantity of living tissue for isolation and spectroscopic investigations. In order to overcome this problem, untargeted metabolomics protocols have been developed. The latter have been applied here to study the chemodiversity of common Egyptian soft coral species, using only minute amounts of coral biomass. Spectral similarity networks, based on high-resolution tandem mass spectrometry data, were employed to explore and highlight the metabolic biodiversity of nine Egyptian soft coral species. Species-specific metabolites were highlighted for future prioritization of soft coral species for MS-guided chemical investigation. Overall, 79 metabolites were tentatively assigned, encompassing diterpenes, sesquiterpenes, and sterols. Simultaneously, the methodology assisted in shedding light on newly-overlooked chemical diversity with potential undescribed scaffolds. For instance, glycosylated fatty acids, nitrogenated aromatic compounds, and polyketides were proposed in Sinularia leptoclados, while alkaloidal terpenes and N-acyl amino acids were proposed in both Sarcophyton roseum and Sarcophyton acutum.

Keywords:  Egyptian soft corals; metabolome profiling; molecular networking

DOI:  https://doi.org/10.3390/md20100630
Cell Chem Biol. 2022 Oct 24. pii: S2451-9456(22)00360-9. [Epub ahead of print]

Ferroptosis inhibition by lysosome-dependent catabolism of extracellular protein.

David A Armenta, Nouf N Laqtom, Grace Alchemy, Wentao Dong, Danielle Morrow, Carson D Poltorack, David A Nathanson, Monther Abu-Remalieh, Scott J Dixon.

  Cancer cells need a steady supply of nutrients to evade cell death and proliferate. Depriving cancer cells of the amino acid cystine can trigger the non-apoptotic cell death process of ferroptosis. Here, we report that cancer cells can evade cystine deprivation-induced ferroptosis by uptake and catabolism of the cysteine-rich extracellular protein albumin. This protective mechanism is enhanced by mTORC1 inhibition and involves albumin degradation in the lysosome, predominantly by cathepsin B (CTSB). CTSB-dependent albumin breakdown followed by export of cystine from the lysosome via the transporter cystinosin fuels the synthesis of glutathione, which suppresses lethal lipid peroxidation. When cancer cells are grown under non-adherent conditions as spheroids, mTORC1 pathway activity is reduced, and albumin supplementation alone affords considerable protection against ferroptosis. These results identify the catabolism of extracellular protein within the lysosome as a mechanism that can inhibit ferroptosis in cancer cells.

Keywords:  ROS; albumin; cancer; cathepsin; cell death; cysteine; ferroptosis; glutathione; lysosome; mTOR

DOI:  https://doi.org/10.1016/j.chembiol.2022.10.006
Proteomics. 2022 Oct 27. e2200032

Ion mobility-resolved phosphoproteomics with dia-PASEF and short gradients.

Denys Oliinyk, Florian Meier.

  Mass spectrometry-based phosphoproteomics has identified > 150,000 post-translational phosphorylation sites in the human proteome. To disentangle their functional relevance, complex experimental designs that require increased throughput are now coming into focus. Here, we apply dia-PASEF on a trapped ion mobility (TIMS) mass spectrometer to analyze the phosphoproteome of a human cancer cell line in short liquid chromatography gradients. At low sample amounts equivalent to ∼20 ug protein digest per analysis, we quantified over 13,000 phosphopeptides including ∼8,700 class I phosphosites in one hour without a spectral library. Decreasing the gradient time to 15 min yielded virtually identical coverage of the phosphoproteome, and with 7 min gradients we still quantified about 80% of the class I sites with a median coefficient of variation < 10% in quadruplicates. We attribute this in part to the increased peak capacity, which effectively compensates for the higher peptide density per time unit in shorter gradients. Our data shows a five-fold reduction in the number of co-isolated peptides with TIMS. In the most extreme case, these were positional isomers of nearby phosphosites that remained unresolved with fast chromatography. In summary, our study demonstrates how key features of dia-PASEF translate to phosphoproteomics. This article is protected by copyright. All rights reserved.

Keywords:  PASEF; Phosphoproteomics; TIMS; data-independent acquisition; ion mobility

DOI:  https://doi.org/10.1002/pmic.202200032