bims-mascan 2023-11-26 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2023–11–26
24 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Computational methods for processing and interpreting mass spectrometry-based metabolomics.
Protocol for mapping the metabolome and lipidome of medulloblastoma cells using liquid chromatography-mass spectrometry.
On-tissue dataset-dependent MALDI-TIMS-MS2 bioimaging.
The Metabolomic Characteristics and Dysregulation of Fatty Acid Esters of Hydroxy Fatty Acids in Breast Cancer.
APEX: an Annotation Propagation Workflow through Multiple Experimental Networks to Improve the Annotation of New Metabolite Classes in Caenorhabditis elegans.
Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns.
Variability analysis of LC-MS experimental factors and their impact on machine learning.
Simple Tool for Rapidly Assessing the Quality of Multiplexed Single Cell Proteomics Data.
Expanding Lipidomic Coverage in Multisegment Injection-Nonaqueous Capillary Electrophoresis-Mass Spectrometry via a Convenient and Quantitative Methylation Strategy.
Unveiling Disrupted Lipid Metabolism in Benign Prostate Hyperplasia, Prostate Cancer, and Metastatic Patients: Insights from a Colombian Nested Case-Control Study.
Cardinal v.3: a versatile open-source software for mass spectrometry imaging analysis.
6-Plex mdSUGAR Isobaric-Labeling Guide Fingerprint Embedding for Glycomics Analysis.
Inferring mitochondrial and cytosolic metabolism by coupling isotope tracing and deconvolution.
Molecular and Metabolic Phenotyping of Hepatocellular Carcinoma for Biomarker Discovery: A Meta-Analysis.
Proline and dihydroorotate dehydrogenase promote a hyper-proliferative state and dampen ferroptosis in cancer cells by rewiring mitochondrial redox metabolism.
Mitochondrial phosphoproteomes are functionally specialized across tissues.
Vitamin B5 Supports Oncogenic Metabolism in MYC-Driven Breast Cancer.
Benefits of FAIMS to Improve the Proteome Coverage of Deteriorated and/or Cross-Linked TMT 10-Plex FFPE Tissue and Plasma-Derived Exosomes Samples.
Metabolic reprogramming driven by EZH2 inhibition depends on cell-matrix interactions.
Mass Spectrometry-Based Multiomics Identifies Metabolic Signatures of Sarcopenia in Rhesus Monkey Skeletal Muscle.
Lipolysis supports bone formation by providing osteoblasts with endogenous fatty acid substrates to maintain bioenergetic status.
On-line targeted metabolomics for real-time monitoring of relevant compounds in fermentation processes.
Characterization of Gla proteoforms and non-Gla peptides of gamma carboxylated proteins: Application to quantification of prothrombin proteoforms in human plasma.
Inborn errors of amino acid metabolism - from underlying pathophysiology to therapeutic advances.

Essays Biochem. 2023 Nov 24. pii: EBC20230019. [Epub ahead of print]

Computational methods for processing and interpreting mass spectrometry-based metabolomics.

Leonardo Perez de Souza, Alisdair R Fernie.

  Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.

Keywords:  bioinformatics; machine learning; mass spectrometry; metabolomics

DOI:  https://doi.org/10.1042/EBC20230019
STAR Protoc. 2023 Nov 23. pii: S2666-1667(23)00703-7. [Epub ahead of print]4(4): 102736

Protocol for mapping the metabolome and lipidome of medulloblastoma cells using liquid chromatography-mass spectrometry.

Jeremy K Chan, William D Gwynne, Brandon Y Lieng, Andrew T Quaile, Chitra Venugopal, Sheila K Singh, J Rafael Montenegro-Burke.

  Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics and lipidomics have recently been used to show that MYC-amplified group 3 medulloblastoma tumors are driven by metabolic reprogramming. Here, we present a protocol to extract metabolites and lipids from human medulloblastoma brain tumor-initiating cells and normal neural stem cells. We describe untargeted LC-MS methods that can be used to achieve extensive coverage of the polar metabolome and lipidome. Finally, we detail strategies for metabolite identification and data analysis. For complete details on the use and execution of this protocol, please refer to Gwynne et al.1.

Keywords:  Cancer; Mass Spectrometry; Metabolomics

DOI:  https://doi.org/10.1016/j.xpro.2023.102736
Nat Commun. 2023 11 18. 14(1): 7495

On-tissue dataset-dependent MALDI-TIMS-MS2 bioimaging.

Steffen Heuckeroth, Arne Behrens, Carina Wolf, Arne Fütterer, Ilona D Nordhorn, Katharina Kronenberg, Corinna Brungs, Ansgar Korf, Henning Richter, Astrid Jeibmann, Uwe Karst, Robin Schmid.

Trapped ion mobility spectrometry (TIMS) adds an additional separation dimension to mass spectrometry (MS) imaging, however, the lack of fragmentation spectra (MS2) impedes confident compound annotation in spatial metabolomics. Here, we describe spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF), a dataset-dependent acquisition strategy that augments TIMS-MS imaging datasets with MS2 spectra. The fragmentation experiments are systematically distributed across the sample and scheduled for multiple collision energies per precursor ion. Extendable data processing and evaluation workflows are implemented into the open source software MZmine. The workflow and annotation capabilities are demonstrated on rat brain tissue thin sections, measured by matrix-assisted laser desorption/ionisation (MALDI)-TIMS-MS, where SIMSEF enables on-tissue compound annotation through spectral library matching and rule-based lipid annotation within MZmine and maps the (un)known chemical space by molecular networking. The SIMSEF algorithm and data analysis pipelines are open source and modular to provide a community resource.

DOI: https://doi.org/10.1038/s41467-023-43298-9
Metabolites. 2023 Oct 24. pii: 1108. [Epub ahead of print]13(11):

The Metabolomic Characteristics and Dysregulation of Fatty Acid Esters of Hydroxy Fatty Acids in Breast Cancer.

Linlin Qin, Na An, Bifeng Yuan, Quanfei Zhu, Yuqi Feng.

  Lipid reprogramming metabolism is crucial for supporting tumor growth in breast cancer and investigating potential tumor biomarkers. Fatty acid esters of hydroxy fatty acids (FAHFAs) are a class of endogenous lipid metabolites with anti-diabetic and anti-inflammatory properties that have been discovered in recent years. Our previous targeted analysis of sera from breast cancer patients revealed a significant down-regulation of several FAHFAs. In this study, we aimed to further explore the relationship between FAHFAs and breast cancer by employing chemical isotope labeling combined with liquid chromatography-mass spectrometry (CIL-LC-MS) for profiling of FAHFAs in tumors and adjacent normal tissues from breast cancer patients. Statistical analysis identified 13 altered isomers in breast cancer. These isomers showed the potential to distinguish breast cancer tissues with an area under the curve (AUC) value above 0.9 in a multivariate receiver operating curve model. Furthermore, the observation of up-regulated 9-oleic acid ester of hydroxy stearic acid (9-OAHSA) and down-regulated 9-hydroxystearic acid (9-HSA) in tumors suggests that breast cancer shares similarities with colorectal cancer, and their potential mechanism is to attenuate the effects of pro-apoptotic 9-HSA by enhancing the synthesis of FAHFAs, thereby promoting tumor survival and progression through this buffering system.

Keywords:  FAHFA; breast cancer; chemical isotope labeling; liquid chromatography; mass spectrometry

DOI:  https://doi.org/10.3390/metabo13111108
Anal Chem. 2023 Nov 20.

APEX: an Annotation Propagation Workflow through Multiple Experimental Networks to Improve the Annotation of New Metabolite Classes in Caenorhabditis elegans.

Liesa Salzer, Elva María Novoa-Del-Toro, Clément Frainay, Kohar Annie B Kissoyan, Fabien Jourdan, Katja Dierking, Michael Witting.

Spectral similarity networks, also known as molecular networks, are crucial in non-targeted metabolomics to aid identification of unknowns aiming to establish a potential structural relation between different metabolite features. However, too extensive differences in compound structures can lead to separate clusters, complicating annotation. To address this challenge, we developed an automated Annotation Propagation through multiple EXperimental Networks (APEX) workflow, which integrates spectral similarity networks with mass difference networks and homologous series. The incorporation of multiple network tools improved annotation quality, as evidenced by high matching rates of the molecular formula derived by SIRIUS. The selection of manual annotations as the Seed Nodes Set (SNS) significantly influenced APEX annotations, with a higher number of seed nodes enhancing the annotation process. We applied APEX to different Caenorhabditis elegans metabolomics data sets as a proof-of-principle for the effective and comprehensive annotation of glycerophospho N-acyl ethanolamides (GPNAEs) and their glyco-variants. Furthermore, we demonstrated the workflow's applicability to two other, well-described metabolite classes in C. elegans, specifically ascarosides and modular glycosides (MOGLs), using an additional publicly available data set. In summary, the APEX workflow presents a powerful approach for metabolite annotation and identification by leveraging multiple experimental networks. By refining the SNS selection and integrating diverse networks, APEX holds promise for comprehensive annotation in metabolomics research, enabling a deeper understanding of the metabolome.

DOI: https://doi.org/10.1021/acs.analchem.3c02797
bioRxiv. 2023 Nov 08. pii: 2023.11.06.565907. [Epub ahead of print]

Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns.

Richard C Law, Samantha O'Keeffe, Glenn Nurwono, Rachel Ki, Aliya Lakhani, Pin-Kuang Lai, Junyoung O Park.

Fluxomics offers a direct readout of metabolic state but relies on indirect measurement. Stable isotope tracers imprint flux-dependent isotope labeling patterns on metabolites we measure; however, the relationship between labeling patterns and fluxes remains elusive. Here we innovate a two-stage machine learning framework termed ML-Flux that streamlines metabolic flux quantitation from isotope tracing. We train machine learning models by simulating atom transitions across five universal metabolic models starting from 26 13 C-glucose, 2 H-glucose, and 13 C-glutamine tracers within feasible flux space. ML-Flux employs deep-learning-based imputation to take variable measurements of labeling patterns as input and successive neural networks to convert the ensuing comprehensive labeling information into metabolic fluxes. Using ML-Flux with multi-isotope tracing, we obtain fluxes through central carbon metabolism that are comparable to those from a least-squares method but orders-of-magnitude faster. ML-Flux is deployed as a webtool to expand the accessibility of metabolic flux quantitation and afford actionable information on metabolism.

DOI: https://doi.org/10.1101/2023.11.06.565907
Gigascience. 2022 Dec 28. pii: giad096. [Epub ahead of print]12

Variability analysis of LC-MS experimental factors and their impact on machine learning.

Tobias Greisager Rehfeldt, Konrad Krawczyk, Simon Gregersen Echers, Paolo Marcatili, Pawel Palczynski, Richard Röttger, Veit Schwämmle.

   BACKGROUND: Machine learning (ML) technologies, especially deep learning (DL), have gained increasing attention in predictive mass spectrometry (MS) for enhancing the data-processing pipeline from raw data analysis to end-user predictions and rescoring. ML models need large-scale datasets for training and repurposing, which can be obtained from a range of public data repositories. However, applying ML to public MS datasets on larger scales is challenging, as they vary widely in terms of data acquisition methods, biological systems, and experimental designs.
RESULTS: We aim to facilitate ML efforts in MS data by conducting a systematic analysis of the potential sources of variability in public MS repositories. We also examine how these factors affect ML performance and perform a comprehensive transfer learning to evaluate the benefits of current best practice methods in the field for transfer learning.
CONCLUSIONS: Our findings show significantly higher levels of homogeneity within a project than between projects, which indicates that it is important to construct datasets most closely resembling future test cases, as transferability is severely limited for unseen datasets. We also found that transfer learning, although it did increase model performance, did not increase model performance compared to a non-pretrained model.

Keywords:  bioinformatics; data mining; deep learning; machine learning; mass spectrometry; proteomics; statistics; transfer learning

DOI:  https://doi.org/10.1093/gigascience/giad096
J Am Soc Mass Spectrom. 2023 Nov 22.

Simple Tool for Rapidly Assessing the Quality of Multiplexed Single Cell Proteomics Data.

Conor Jenkins, Benjamin C Orsburn.

Recent advances in the sensitivity and speed of mass spectrometers coupled with improved sample preparation methods have enabled the field of single cell proteomics to proliferate. While heavy development is occurring in the label free space, dramatic improvements in throughput are provided by multiplexing with tandem mass tags. Hundreds or thousands of single cells can be analyzed with this method, yielding large data sets which may contain poor data arising from loss of material during cell sorting or poor digestion, labeling, and lysis. To date, no tools have been described that can assess data quality prior to data processing. We present herein a lightweight python script and accompanying graphic user interface that can rapidly quantify reporter ion peaks within each MS/MS spectrum in a file. With simple summary reports, we can identify single cell samples that fail to pass a set quality threshold, thus reducing analysis time waste. In addition, this tool, Diagnostic Ion Data Analysis Reduction (DIDAR), will create reduced MGF files containing only spectra possessing a user-specified number of single cell reporter ions. By reducing the number of spectra that have excessive zero values, we can speed up sample processing with little loss in data completeness as these spectra are removed in later stages in data processing workflows. DIDAR and the DIDAR GUI are compatible with all modern operating systems and are available at: https://github.com/orsburn/DIDARSCPQC. All files described in this study are available at www.massive.ucsd.edu as accession MSV000088887.

DOI: https://doi.org/10.1021/jasms.3c00238
Anal Chem. 2023 Nov 22.

Expanding Lipidomic Coverage in Multisegment Injection-Nonaqueous Capillary Electrophoresis-Mass Spectrometry via a Convenient and Quantitative Methylation Strategy.

Ritchie Ly, Lucas Christian Torres, Nicholas Ly, Philip Britz-McKibbin.

Orthogonal separation techniques coupled to high-resolution mass spectrometry are required for characterizing the human lipidome, given its inherent chemical and structural complexity. However, electrophoretic separations remain largely unrecognized in contemporary lipidomics research compared to established chromatographic and ion mobility methods. Herein, we introduce a novel derivatization protocol based on 3-methyl-1-p-tolyltriazene (MTT) as a safer alternative to diazomethane for quantitative phospholipid (PL) methylation (∼90%), which enables their rapid analysis by multisegment injection-nonaqueous capillary electrophoresis-mass spectrometry (MSI-NACE-MS). Isobaric interferences and ion suppression effects were minimized by performing an initial reaction using 9-fluorenylmethyoxycarbonyl chloride prior to MTT and a subsequent back extraction in hexane. This charge-switch derivatization strategy expands lipidome coverage when using MSI-NACE-MS under positive ion mode with improved resolution, greater sensitivity, and higher throughput (∼3.5 min/sample), notably for zwitterionic PLs that are analyzed as their cationic phosphate methyl esters. Our method was validated by analyzing methyl-tert-butyl ether extracts of reference human plasma, which enabled a direct comparison of 48 phosphatidylcholine and 27 sphingomyelin species previously reported in an interlaboratory lipidomics harmonization study. The potential for plasma PL quantification by MSI-NACE-MS via a serial dilution of NIST SRM-1950 was also demonstrated based on estimation of relative response factors using their reported consensus concentrations. Moreover, lipid identification was supported by modeling predictable changes in the electrophoretic mobility for cationic PLs in conjunction with MS/MS. Overall, this work offers a practical derivatization protocol to expand lipidome coverage in CE-MS beyond the analysis of hydrophilic/polar metabolites under aqueous buffer conditions.

DOI: https://doi.org/10.1021/acs.analchem.3c02605
Cancers (Basel). 2023 Nov 18. pii: 5465. [Epub ahead of print]15(22):

Unveiling Disrupted Lipid Metabolism in Benign Prostate Hyperplasia, Prostate Cancer, and Metastatic Patients: Insights from a Colombian Nested Case-Control Study.

Daniel Pardo-Rodriguez, Mary Santamaría-Torres, Angela Salinas, Eliécer Jiménez-Charris, Mildrey Mosquera, Mónica P Cala, Herney Andrés García-Perdomo.

  Prostate cancer is a significant global health concern, and its prevalence is increasing worldwide. Despite extensive research efforts, the complexity of the disease remains challenging with respect to fully understanding it. Metabolomics has emerged as a powerful approach to understanding prostate cancer by assessing comprehensive metabolite profiles in biological samples. In this study, metabolic profiles of patients with benign prostatic hyperplasia (BPH), prostate cancer (PCa), and metastatic prostate cancer (Met) were characterized using an untargeted approach that included metabolomics and lipidomics via liquid chromatography and gas chromatography coupled with high-resolution mass spectrometry. Comparative analysis among these groups revealed distinct metabolic profiles, primarily associated with lipid biosynthetic pathways, such as biosynthesis of unsaturated fatty acids, fatty acid degradation and elongation, and sphingolipid and linoleic acid metabolism. PCa patients showed lower levels of amino acids, glycerolipids, glycerophospholipids, sphingolipids, and carnitines compared to BPH patients. Compared to Met patients, PCa patients had reduced metabolites in the glycerolipid, glycerophospholipid, and sphingolipid groups, along with increased amino acids and carbohydrates. These altered metabolic profiles provide insights into the underlying pathways of prostate cancer's progression, potentially aiding the development of new diagnostic, and therapeutic strategies.

Keywords:  Colombia; benign prostatic hyperplasia; lipidic alteration; metastasis; prostate cancer; untargeted metabolomics analysis

DOI:  https://doi.org/10.3390/cancers15225465
Nat Methods. 2023 Nov 23.

Cardinal v.3: a versatile open-source software for mass spectrometry imaging analysis.

Kylie Ariel Bemis, Melanie Christine Föll, Dan Guo, Sai Srikanth Lakkimsetty, Olga Vitek.

Cardinal v.3 is an open-source software for reproducible analysis of mass spectrometry imaging experiments. A major update from its previous versions, Cardinal v.3 supports most mass spectrometry imaging workflows. Its analytical capabilities include advanced data processing such as mass recalibration, advanced statistical analyses such as single-ion segmentation and rough annotation-based classification, and memory-efficient analyses of large-scale multitissue experiments.

DOI: https://doi.org/10.1038/s41592-023-02070-z
Anal Chem. 2023 Nov 20.

6-Plex mdSUGAR Isobaric-Labeling Guide Fingerprint Embedding for Glycomics Analysis.

Min Ma, Miyang Li, Yinlong Zhu, Yingyi Zhao, Feixuan Wu, Zicong Wang, Yu Feng, Hung-Yu Chiang, Manish S Patankar, Cheng Chang, Lingjun Li.

Glycans are vital biomolecules with diverse functions in biological processes. Mass spectrometry (MS) has become the most widely employed technology for glycomics studies. However, in the traditional data-dependent acquisition mode, only a subset of the abundant ions during MS1 scans are isolated and fragmented in subsequent MS2 events, which reduces reproducibility and prevents the measurement of low-abundance glycan species. Here, we reported a new method termed 6-plex mdSUGAR isobaric-labeling guide fingerprint embedding (MAGNI), to achieve multiplexed, quantitative, and targeted glycan analysis. The glycan peak signature was embedded by a triplicate-labeling strategy with a 6-plex mdSUGAR tag, and using ultrahigh-resolution mass spectrometers, the low-abundance glycans that carry the mass fingerprints can be recognized on the MS1 spectra through an in-house developed software tool, MAGNIFinder. These embedded unique fingerprints can guide the selection and fragmentation of targeted precursor ions and further provide rich information on glycan structures. Quantitative analysis of two standard glycoproteins demonstrated the accuracy and precision of MAGNI. Using this approach, we identified 304 N-glycans in two ovarian cancer cell lines. Among them, 65 unique N-glycans were found differentially expressed, which indicates a distinct glycosylation pattern for each cell line. Remarkably, 31 N-glycans can be quantified in only 1 × 103 cells, demonstrating the high sensitivity of our method. Taken together, our MAGNI method offers a useful tool for low-abundance N-glycan characterization and is capable of determining small quantitative differences in N-glycan profiling. Therefore, it will be beneficial to the field of glycobiology and will expand our understanding of glycosylation.

DOI: https://doi.org/10.1021/acs.analchem.3c03342
Nat Commun. 2023 11 18. 14(1): 7525

Inferring mitochondrial and cytosolic metabolism by coupling isotope tracing and deconvolution.

Alon Stern, Mariam Fokra, Boris Sarvin, Ahmad Abed Alrahem, Won Dong Lee, Elina Aizenshtein, Nikita Sarvin, Tomer Shlomi.

The inability to inspect metabolic activities within distinct subcellular compartments has been a major barrier to our understanding of eukaryotic cell metabolism. Previous work addressed this challenge by analyzing metabolism in isolated organelles, which grossly bias metabolic activity. Here, we describe a method for inferring physiological metabolic fluxes and metabolite concentrations in mitochondria and cytosol based on isotope tracing experiments performed with intact cells. This is made possible by computational deconvolution of metabolite isotopic labeling patterns and concentrations into cytosolic and mitochondrial counterparts, coupled with metabolic and thermodynamic modelling. Our approach lowers the uncertainty regarding compartmentalized fluxes and concentrations by one and three orders of magnitude compared to existing modelling approaches, respectively. We derive a quantitative view of mitochondrial and cytosolic metabolic activities in central carbon metabolism across cultured cell lines without performing cell fractionation, finding major variability in compartmentalized malate-aspartate shuttle fluxes. We expect our approach for inferring metabolism at a subcellular resolution to be instrumental for a variety of studies of metabolic dysfunction in human disease and for bioengineering.

DOI: https://doi.org/10.1038/s41467-023-42824-z
Metabolites. 2023 Oct 27. pii: 1112. [Epub ahead of print]13(11):

Molecular and Metabolic Phenotyping of Hepatocellular Carcinoma for Biomarker Discovery: A Meta-Analysis.

Nguyen Hoang Anh, Nguyen Phuoc Long, Young Jin Min, Yujin Ki, Sun Jo Kim, Cheol Woon Jung, Seongoh Park, Sung Won Kwon, Seul Ji Lee.

  Identifying and translating hepatocellular carcinoma (HCC) biomarkers from bench to bedside using mass spectrometry-based metabolomics and lipidomics is hampered by inconsistent findings. Here, we investigated HCC at systemic and metabolism-centric multiomics levels by conducting a meta-analysis of quantitative evidence from 68 cohorts. Blood transcript biomarkers linked to the HCC metabolic phenotype were externally validated and prioritized. In the studies under investigation, about 600 metabolites were reported as putative HCC-associated biomarkers; 39, 20, and 10 metabolites and 52, 12, and 12 lipids were reported in three or more studies in HCC vs. Control, HCC vs. liver cirrhosis (LC), and LC vs. Control groups, respectively. Amino acids, fatty acids (increased 18:1), bile acids, and lysophosphatidylcholine were the most frequently reported biomarkers in HCC. BAX and RAC1 showed a good correlation and were associated with poor prognosis. Our study proposes robust HCC biomarkers across diverse cohorts using a data-driven knowledge-based approach that is versatile and affordable for studying other diseases.

Keywords:  hepatocellular carcinoma; lipidomics; meta-analysis; metabolomics; transcriptomics

DOI:  https://doi.org/10.3390/metabo13111112
Biochim Biophys Acta Mol Cell Res. 2023 Nov 21. pii: S0167-4889(23)00212-4. [Epub ahead of print] 119639

Proline and dihydroorotate dehydrogenase promote a hyper-proliferative state and dampen ferroptosis in cancer cells by rewiring mitochondrial redox metabolism.

Ryan J Mailloux.

  Redox realignment is integral to the initiation, progression, and metastasis of cancer. This requires considerable metabolic rewiring to induce aberrant shifts in redox homeostasis that favor high hydrogen peroxide (H2O2) generation for the induction of a hyper-proliferative state. The ability of tumor cells to thrive under the oxidative burden imposed by this high H2O2 is achieved by increasing antioxidant defenses. This shift in the redox stress signaling threshold (RST) also dampens ferroptosis, an iron (Fe)-dependent form of cell death activated by oxidative distress and lipid peroxidation reactions. Mitochondria are central to the malignant transformation of normal cells to cancerous ones since these organelles supply building blocks for anabolism, govern ferroptosis, and serve as the major source of cell H2O2. This review summarizes advances in understanding the rewiring of redox reactions in mitochondria to promote carcinogenesis, focusing on how cancer cells hijack the electron transport chain (ETC) to promote proliferation and evasion of ferroptosis. I then apply emerging concepts in redox homeodynamics to discuss how the rewiring of the Krebs cycle and ETC promotes shifts in the RST to favor high rates of H2O2 generation for cell signaling. This discussion then focuses on proline dehydrogenase (PRODH) and dihydroorotate dehydrogenase (DHODH), two enzymes over expressed in cancers, and how their link to one another through the coenzyme Q10 (CoQ) pool generates a redox connection that forms a H2O2 signaling platform and pyrimidine synthesome that favors a hyper-proliferative state and disables ferroptosis.

Keywords:  Dihydroorotate dehydrogenase;; Ferroptosis; Hydrogen peroxide; Proline dehydrogense; Redox stress signaling threshold

DOI:  https://doi.org/10.1016/j.bbamcr.2023.119639
Life Sci Alliance. 2024 Feb;pii: e202302147. [Epub ahead of print]7(2):

Mitochondrial phosphoproteomes are functionally specialized across tissues.

Fynn M Hansen, Laura S Kremer, Ozge Karayel, Isabell Bludau, Nils-Göran Larsson, Inge Kühl, Matthias Mann.

Mitochondria are essential organelles whose dysfunction causes human pathologies that often manifest in a tissue-specific manner. Accordingly, mitochondrial fitness depends on versatile proteomes specialized to meet diverse tissue-specific requirements. Increasing evidence suggests that phosphorylation may play an important role in regulating tissue-specific mitochondrial functions and pathophysiology. Building on recent advances in mass spectrometry (MS)-based proteomics, we here quantitatively profile mitochondrial tissue proteomes along with their matching phosphoproteomes. We isolated mitochondria from mouse heart, skeletal muscle, brown adipose tissue, kidney, liver, brain, and spleen by differential centrifugation followed by separation on Percoll gradients and performed high-resolution MS analysis of the proteomes and phosphoproteomes. This in-depth map substantially quantifies known and predicted mitochondrial proteins and provides a resource of core and tissue-specific mitochondrial proteins (mitophos.de). Predicting kinase substrate associations for different mitochondrial compartments indicates tissue-specific regulation at the phosphoproteome level. Illustrating the functional value of our resource, we reproduce mitochondrial phosphorylation events on dynamin-related protein 1 responsible for its mitochondrial recruitment and fission initiation and describe phosphorylation clusters on MIGA2 linked to mitochondrial fusion.

DOI: https://doi.org/10.26508/lsa.202302147
Cancer Discov. 2023 Nov 22. OF1

Vitamin B5 Supports Oncogenic Metabolism in MYC-Driven Breast Cancer.

Pantothetic acid is required for metabolic activity that supports MYC-driven breast tumor growth.

DOI: https://doi.org/10.1158/2159-8290.CD-RW2023-185
Proteomes. 2023 Oct 24. pii: 35. [Epub ahead of print]11(4):

Benefits of FAIMS to Improve the Proteome Coverage of Deteriorated and/or Cross-Linked TMT 10-Plex FFPE Tissue and Plasma-Derived Exosomes Samples.

Ana Montero-Calle, María Garranzo-Asensio, Raquel Rejas-González, Jaime Feliu, Marta Mendiola, Alberto Peláez-García, Rodrigo Barderas.

  The proteome characterization of complex, deteriorated, or cross-linked protein mixtures as paired clinical FFPE or exosome samples isolated from low plasma volumes (250 µL) might be a challenge. In this work, we aimed at investigating the benefits of FAIMS technology coupled to the Orbitrap Exploris 480 mass spectrometer for the TMT quantitative proteomics analyses of these complex samples in comparison to the analysis of protein extracts from cells, frozen tissue, and exosomes isolated from large volume plasma samples (3 mL). TMT experiments were performed using a two-hour gradient LC-MS/MS with or without FAIMS and two compensation voltages (CV = -45 and CV = -60). In the TMT experiments of cells, frozen tissue, or exosomes isolated from large plasma volumes (3 mL) with FAIMS, a limited increase in the number of identified and quantified proteins accompanied by a decrease in the number of peptides identified and quantified was observed. However, we demonstrated here a noticeable improvement (>100%) in the number of peptide and protein identifications and quantifications for the plasma exosomes isolated from low plasma volumes (250 µL) and FFPE tissue samples in TMT experiments with FAIMS in comparison to the LC-MS/MS analysis without FAIMS. Our results highlight the potential of mass spectrometry analyses with FAIMS to increase the depth into the proteome of complex samples derived from deteriorated, cross-linked samples and/or those where the material was scarce, such as FFPE and plasma-derived exosomes from low plasma volumes (250 µL), which might aid in the characterization of their proteome and proteoforms and in the identification of dysregulated proteins that could be used as biomarkers.

Keywords:  FAIMS; FFPE tissue; Orbitrap Exploris 480; exosomes; mass spectrometry; protein coverage; protein identification and quantification; proteomics

DOI:  https://doi.org/10.3390/proteomes11040035
J Biol Chem. 2023 Nov 20. pii: S0021-9258(23)02513-9. [Epub ahead of print] 105485

Metabolic reprogramming driven by EZH2 inhibition depends on cell-matrix interactions.

Teresa W-M Fan, Jahid M M Islam, Richard M Higashi, Penghui Lin, Christine F Brainson, Andrew N Lane.

  EZH2 (Enhancer of Zeste Homolog 2), a subunit of Polycomb Repressive Complex 2 (PRC2), catalyzes the trimethylation of histone H3 at lysine 27 (H3K27me3), which represses expression of genes. It also has PRC2-independent functions, including transcriptional coactivation of oncogenes, and is frequently overexpressed in lung cancers. Clinically, EZH2 inhibition can be achieved with the FDA-approved drug EPZ-6438 (tazemetostat). To realize the full potential of EZH2 blockade, it is critical to understand how cell-cell/cell-matrix interactions present in three-dimensional (3D) tissue and cell culture systems influences this blockade in terms of growth-related metabolic functions. Here, we show that EZH2 suppression reduced growth of human lung adenocarcinoma A549 cells in two-dimensional (2D) cultures but stimulated growth in 3D culture. To understand the metabolic underpinnings, we employed [13C6]-glucose Stable Isotope-Resolved Metabolomics (SIRM) to determine the effect of EZH2 suppression on metabolic networks in 2D versus 3D A549 cultures. The Krebs cycle, neoribogenesis, γ-aminobutyrate (GAB) metabolism, and salvage synthesis of purine nucleotides were activated by EZH2 suppression in 3D spheroids but not in 2D cells, consistent with the growth effect. Using simultaneous 2H7-glucose + 13C5,15N2-Gln tracers and EPZ-6438 inhibition of H3 trimethylation, we delineated the effects on the Krebs cycle, γ-aminobutyrate metabolism, gluconeogenesis, and purine salvage to be PRC2 dependent. Furthermore, the growth/metabolic effects differed for mouse Matrigel versus self-produced A549 extracellular matrix. Thus, our findings highlight the importance of the presence and nature of extracellular matrix in studying the function of EZH2 and its inhibitors in cancer cells for modeling the in vivo outcomes.

Keywords:  EZH2; Stable Isotope-Resolved Metabolomics; extracellular matrix; glucose/glutamine metabolism; spheroids

DOI:  https://doi.org/10.1016/j.jbc.2023.105485
J Proteome Res. 2023 Nov 22.

Mass Spectrometry-Based Multiomics Identifies Metabolic Signatures of Sarcopenia in Rhesus Monkey Skeletal Muscle.

Melissa R Pergande, Katie J Osterbauer, Kevin M Buck, David S Roberts, Nina N Wood, Priya Balasubramanian, Morgan W Mann, Kalina J Rossler, Gary M Diffee, Ricki J Colman, Rozalyn M Anderson, Ying Ge.

  Sarcopenia is a progressive disorder characterized by age-related loss of skeletal muscle mass and function. Although significant progress has been made over the years to identify the molecular determinants of sarcopenia, the precise mechanisms underlying the age-related loss of contractile function remains unclear. Advances in "omics" technologies, including mass spectrometry-based proteomic and metabolomic analyses, offer great opportunities to better understand sarcopenia. Herein, we performed mass spectrometry-based analyses of the vastus lateralis from young, middle-aged, and older rhesus monkeys to identify molecular signatures of sarcopenia. In our proteomic analysis, we identified proteins that change with age, including those involved in adenosine triphosphate and adenosine monophosphate metabolism as well as fatty acid beta oxidation. In our untargeted metabolomic analysis, we identified metabolites that changed with age largely related to energy metabolism including fatty acid beta oxidation. Pathway analysis of age-responsive proteins and metabolites revealed changes in muscle structure and contraction as well as lipid, carbohydrate, and purine metabolism. Together, this study discovers new metabolic signatures and offers new insights into the molecular mechanisms underlying sarcopenia for the evaluation and monitoring of a therapeutic treatment of sarcopenia.

Keywords:  bottom-up proteomics; multiomics; nonhuman primate; sarcopenia; skeletal muscle; untargeted metabolomics

DOI:  https://doi.org/10.1021/acs.jproteome.3c00474
Bone Res. 2023 Nov 24. 11(1): 62

Lipolysis supports bone formation by providing osteoblasts with endogenous fatty acid substrates to maintain bioenergetic status.

Ananya Nandy, Ron C M Helderman, Santosh Thapa, Shobana Jayapalan, Alison Richards, Nikita Narayani, Michael P Czech, Clifford J Rosen, Elizabeth Rendina-Ruedy.

Bone formation is a highly energy-demanding process that can be impacted by metabolic disorders. Glucose has been considered the principal substrate for osteoblasts, although fatty acids are also important for osteoblast function. Here, we report that osteoblasts can derive energy from endogenous fatty acids stored in lipid droplets via lipolysis and that this process is critical for bone formation. As such, we demonstrate that osteoblasts accumulate lipid droplets that are highly dynamic and provide the molecular mechanism by which they serve as a fuel source for energy generation during osteoblast maturation. Inhibiting cytoplasmic lipolysis leads to both an increase in lipid droplet size in osteoblasts and an impairment in osteoblast function. The fatty acids released by lipolysis from these lipid droplets become critical for cellular energy production as cellular energetics shifts towards oxidative phosphorylation during nutrient-depleted conditions. In vivo, conditional deletion of the ATGL-encoding gene Pnpla2 in osteoblast progenitor cells reduces cortical and trabecular bone parameters and alters skeletal lipid metabolism. Collectively, our data demonstrate that osteoblasts store fatty acids in the form of lipid droplets, which are released via lipolysis to support cellular bioenergetic status when nutrients are limited. Perturbations in this process result in impairment of bone formation, specifically reducing ATP production and overall osteoblast function.

DOI: https://doi.org/10.1038/s41413-023-00297-2
Biotechnol Bioeng. 2023 Nov 22.

On-line targeted metabolomics for real-time monitoring of relevant compounds in fermentation processes.

Joan Cortada-Garcia, Jennifer Haggarty, Stefan Weidt, Rónán Daly, S Alison Arnold, Karl Burgess.

  Fermentation monitoring is a powerful tool for bioprocess development and optimization. On-line metabolomics is a technology that is starting to gain attention as a bioprocess monitoring tool, allowing the direct measurement of many compounds in the fermentation broth at a very high time resolution. In this work, targeted on-line metabolomics was used to monitor 40 metabolites of interest during three Escherichia coli succinate production fermentation experiments every 5 min with a triple quadrupole mass spectrometer. This allowed capturing high-time resolution biological data that can provide critical information for process optimization. For nine of these metabolites, simple univariate regression models were used to model compound concentration from their on-line mass spectrometry peak area. These on-line metabolomics univariate models performed comparably to vibrational spectroscopy multivariate partial least squares regressions models reported in the literature, which typically are much more complex and time consuming to build. In conclusion, this work shows how on-line metabolomics can be used to directly monitor many bioprocess compounds of interest and obtain rich biological and bioprocess data.

Keywords:  bioprocess monitoring; fermentation monitoring; on-line metabolomics; process analytical technologies; real-time metabolomics

DOI:  https://doi.org/10.1002/bit.28599
Anal Chim Acta. 2023 Dec 15. pii: S0003-2670(23)01193-5. [Epub ahead of print]1284 341972

Characterization of Gla proteoforms and non-Gla peptides of gamma carboxylated proteins: Application to quantification of prothrombin proteoforms in human plasma.

Dilip Kumar Singh, Abdul Basit, Allan E Rettie, Nathan Alade, Kenneth Thummel, Bhagwat Prasad.

  Gamma (γ) carboxylation is an essential post-translational modification in vitamin K-dependent proteins (VKDPs), involved in maintaining critical biological homeostasis. Alterations in the abundance or activity of these proteins have pharmacological and pathological consequences. Importantly, low levels of fully γ-carboxylated clotting factors increase plasma des-γ-carboxy precursors resulting in little or no biological activity. Therefore, it is important to characterize the levels of γ-carboxylation that reflect the active state of these proteins. The conventional enzyme-linked immunosorbent assay for protein induced by vitamin K absence or antagonist II (PIVKA-II) quantification uses an antibody that is not applicable to distinguish different γ-carboxylation states. Liquid chromatography-mass spectrometry (LC-MS) approaches have been utilized to distinguish different γ-carboxylated proteoforms, however, these attempts were impeded by poor sensitivity due to spontaneous neutral loss of CO2 and simultaneous cleavage of the backbone bond in the collision cell. In this study, we utilized an alkaline mobile phase in combination with polarity switching (positive and negative ionization modes) to simultaneously identify and quantify γ-carboxylated VKDPs. The method was applied to compare Gla proteomics of prothrombin (FII) in 10 μL plasma samples of healthy control and warfarin-treated adults. We also identified surrogate non-Gla peptides for seven other VKDPs to quantify total (active plus inactive) protein levels. The total protein approach (TPA) was used to quantify absolute levels of the VKDPs in human plasma.

Keywords:  Gamma carboxylation; Global proteomics; LC-MS; Prothrombin; Targeted proteomics; Vitamin K-dependent proteins

DOI:  https://doi.org/10.1016/j.aca.2023.341972
Dis Model Mech. 2023 Nov 01. pii: dmm050233. [Epub ahead of print]16(11):

Inborn errors of amino acid metabolism - from underlying pathophysiology to therapeutic advances.

Shira G Ziegler, Jiyoung Kim, Jeffrey T Ehmsen, Hilary J Vernon.

  Amino acids are organic molecules that serve as basic substrates for protein synthesis and have additional key roles in a diverse array of cellular functions, including cell signaling, gene expression, energy production and molecular biosynthesis. Genetic defects in the synthesis, catabolism or transport of amino acids underlie a diverse class of diseases known as inborn errors of amino acid metabolism. Individually, these disorders are rare, but collectively, they represent an important group of potentially treatable disorders. In this Clinical Puzzle, we discuss the pathophysiology, clinical features and management of three disorders that showcase the diverse clinical presentations of disorders of amino acid metabolism: phenylketonuria, lysinuric protein intolerance and homocystinuria due to cystathionine β-synthase (CBS) deficiency. Understanding the biochemical perturbations caused by defects in amino acid metabolism will contribute to ongoing development of diagnostic and management strategies aimed at improving the morbidity and mortality associated with this diverse group of disorders.

Keywords:  Amino acids; Inborn errors of metabolism; Newborn screen

DOI:  https://doi.org/10.1242/dmm.050233