bims-mascan 2024-09-29 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2024–09–29
23 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Data-Independent Acquisition Shortens the Analytical Window of Single-Cell Proteomics to Fifteen Minutes in Capillary Electrophoresis Mass Spectrometry.
Automated High-Throughput Biological Sex Identification from Archeological Human Dental Enamel Using Targeted Proteomics.
Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations.
Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.
Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states.
Navigating common pitfalls in metabolite identification and metabolomics bioinformatics.
Electrophoresis-Correlative Ion Mobility Deepens Single-cell Proteomics in Capillary Electrophoresis Mass Spectrometry.
High-Throughput Equine Doping Controls on a Trapped Ion Mobility Quadrupole-Time-of-Flight Mass Spectrometer: Technical Considerations of dia/slice/prmPASEF Applied to the Long-Term Detection of Monoclonal Antibodies.
Metabolic Reprogramming in Glioblastoma Multiforme: A Review of Pathways and Therapeutic Targets.
Advances and challenges in liquid chromatography-spectrometry (LC-MS) methodology for quantifying androgens and estrogens in human serum and plasma.
FAMS-A Targeted Fatty Acid Mass Spectrometry Method for Monitoring Free Fatty Acids from Polysorbate Hydrolysis.
Proteome of plasma extracellular vesicles as a source of colorectal cancer biomarkers.
Theoretical Assessment of Indistinguishable Peptides in Mass Spectrometry-Based Proteomics.
Impact of Phenylketonuria on the Serum Metabolome and Plasma Lipidome: A Study in Early-Treated Patients.
Linking metabolism and histone acetylation dynamics by integrated metabolic flux analysis of Acetyl-CoA and histone acetylation sites.
Specific activation of the integrated stress response uncovers regulation of central carbon metabolism and lipid droplet biogenesis.
NOVEL EICOSANOID SIGNATURE IN PLASMA PROVIDES DIAGNOSTIC FOR METABOLIC DYSFUNCTION-ASSOCIATED STEATOTIC LIVER DISEASE.
EXCRETE workflow enables deep proteomics of the microbial extracellular environment.
Metabolic landscape of the healthy pancreas and pancreatic tumor microenvironment.
Identifying targetable metabolic dependencies across colorectal cancer progression.
An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples.
Targeted MRM-analysis of plasma proteins in frozen whole blood samples from patients with COVID-19: a retrospective study.
A secondary β-hydroxybutyrate metabolic pathway linked to energy balance.

J Proteome Res. 2024 Sep 26.

Data-Independent Acquisition Shortens the Analytical Window of Single-Cell Proteomics to Fifteen Minutes in Capillary Electrophoresis Mass Spectrometry.

Bowen Shen, Leena R Pade, Peter Nemes.

  Separation in single-cell mass spectrometry (MS) improves molecular coverage and quantification; however, it also elongates measurements, thus limiting analytical throughput to study large populations of cells. Here, we advance the speed of bottom-up proteomics by capillary electrophoresis (CE) high-resolution mass spectrometry (MS) for single-cell proteomics. We adjust the applied electrophoresis potential to readily control the duration of electrophoresis. On the HeLa proteome standard, shorter separation times curbed proteome detection using data-dependent acquisition (DDA) but not data-independent acquisition (DIA) on an Orbitrap analyzer. This DIA method identified 1161 proteins vs 401 proteins by the reference DDA within a 15 min effective separation from single HeLa-cell-equivalent (∼200 pg) proteome digests. Label-free quantification found these exclusively DIA-identified proteins in the lower domain of the concentration range, revealing sensitivity improvement. The approach also significantly advanced the reproducibility of quantification, where ∼76% of the DIA-quantified proteins had <20% coefficient of variation vs ∼43% by DDA. As a proof of principle, the method allowed us to quantify 1242 proteins in subcellular niches in a single, neural-tissue fated cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. DIA integration enhanced throughput by ∼2-4 fold and sensitivity by a factor of ∼3 in single-cell (subcellular) CE-MS proteomics.

Keywords:  Cell; HeLa; Xenopus laevis; mass spectrometry; proteomics; subcellular

DOI:  https://doi.org/10.1021/acs.jproteome.4c00491
J Proteome Res. 2024 Sep 26.

Automated High-Throughput Biological Sex Identification from Archeological Human Dental Enamel Using Targeted Proteomics.

Claire Koenig, Patricia Bortel, Ryan S Paterson, Barbara Rendl, Palesa P Madupe, Gaudry B Troché, Nuno Vibe Hermann, Marina Martínez de Pinillos, María Martinón-Torres, Sandra Mularczyk, Marie Louise Schjellerup Jørkov, Christopher Gerner, Fabian Kanz, Ana Martinez-Val, Enrico Cappellini, Jesper V Olsen.

  Biological sex is key information for archeological and forensic studies, which can be determined by proteomics. However, the lack of a standardized approach for fast and accurate sex identification currently limits the reach of proteomics applications. Here, we introduce a streamlined mass spectrometry (MS)-based workflow for the determination of biological sex using human dental enamel. Our approach builds on a minimally invasive sampling strategy by acid etching, a rapid online liquid chromatography (LC) gradient coupled to a high-resolution parallel reaction monitoring (PRM) assay allowing for a throughput of 200 samples per day (SPD) with high quantitative performance enabling confident identification of both males and females. Additionally, we developed a streamlined data analysis pipeline and integrated it into a Shiny interface for ease of use. The method was first developed and optimized using modern teeth and then validated in an independent set of deciduous teeth of known sex. Finally, the assay was successfully applied to archeological material, enabling the analysis of over 300 individuals. We demonstrate unprecedented performance and scalability, speeding up MS analysis by 10-fold compared to conventional proteomics-based sex identification methods. This work paves the way for large-scale archeological or forensic studies enabling the investigation of entire populations rather than focusing on individual high-profile specimens. Data are available via ProteomeXchange with the identifier PXD049326.

Keywords:  archeology; biological sex; dental enamel; forensics; high-throughput; palaeoproteomics; parallel reaction monitoring (PRM); proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.4c00557
Cell Rep. 2024 Sep 20. pii: S2211-1247(24)01126-4. [Epub ahead of print]43(10): 114775

Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations.

Cara Abecunas, Audrey D Kidd, Ying Jiang, Hui Zong, Mohammad Fallahi-Sichani.

  Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges to identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tumor lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings via analysis of data from patient-derived tumors and pharmacological screens and by performing genetic and pharmacological experiments. Our analysis uncovers synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating the mechanisms underlying these relationships can inform the development of more precise and context-specific, metabolism-targeted cancer therapies.

Keywords:  CP: Cancer; CP: Metabolism; PTEN; cancer metabolism; cancer therapies; glioma; metabolic state vulnerabilities; mitochondrial electron transport chain; multivariate modeling; oxidative phosphorylation; synthetic lethality

DOI:  https://doi.org/10.1016/j.celrep.2024.114775
Nat Protoc. 2024 Sep 20.

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.

Abzer K Pakkir Shah, Axel Walter, Filip Ottosson, Francesco Russo, Marcelo Navarro-Diaz, Judith Boldt, Jarmo-Charles J Kalinski, Eftychia Eva Kontou, James Elofson, Alexandros Polyzois, Carolina González-Marín, Shane Farrell, Marie R Aggerbeck, Thapanee Pruksatrakul, Nathan Chan, Yunshu Wang, Magdalena Pöchhacker, Corinna Brungs, Beatriz Cámara, Andrés Mauricio Caraballo-Rodríguez, Andres Cumsille, Fernanda de Oliveira, Kai Dührkop, Yasin El Abiead, Christian Geibel, Lana G Graves, Martin Hansen, Steffen Heuckeroth, Simon Knoblauch, Anastasiia Kostenko, Mirte C M Kuijpers, Kevin Mildau, Stilianos Papadopoulos Lambidis, Paulo Wender Portal Gomes, Tilman Schramm, Karoline Steuer-Lodd, Paolo Stincone, Sibgha Tayyab, Giovanni Andrea Vitale, Berenike C Wagner, Shipei Xing, Marquis T Yazzie, Simone Zuffa, Martinus de Kruijff, Christine Beemelmanns, Hannes Link, Christoph Mayer, Justin J J van der Hooft, Tito Damiani, Tomáš Pluskal, Pieter Dorrestein, Jan Stanstrup, Robin Schmid, Mingxun Wang, Allegra Aron, Madeleine Ernst, Daniel Petras.

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

DOI: https://doi.org/10.1038/s41596-024-01046-3
Nat Commun. 2024 Sep 26. 15(1): 8262

Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states.

Karl K Krull, Syed Azmal Ali, Jeroen Krijgsveld.

Proteome analysis by data-independent acquisition (DIA) has become a powerful approach to obtain deep proteome coverage, and has gained recent traction for label-free analysis of single cells. However, optimal experimental design for DIA-based single-cell proteomics has not been fully explored, and performance metrics of subsequent data analysis tools remain to be evaluated. Therefore, we here formalize and comprehensively evaluate a DIA data analysis strategy that exploits the co-analysis of low-input samples with a so-called matching enhancer (ME) of higher input, to increase sensitivity, proteome coverage, and data completeness. We assess the matching specificity of DIA-ME by a two-proteome model, and demonstrate that false discovery and false transfer are maintained at low levels when using DIA-NN software, while preserving quantification accuracy. We apply DIA-ME to investigate the proteome response of U-2 OS cells to interferon gamma (IFN-γ) in single cells, and recapitulate the time-resolved induction of IFN-γ response proteins as observed in bulk material. Moreover, we uncover co- and anti-correlating patterns of protein expression within the same cell, indicating mutually exclusive protein modules and the co-existence of different cell states. Collectively our data show that DIA-ME is a powerful, scalable, and easy-to-implement strategy for single-cell proteomics.

DOI: https://doi.org/10.1038/s41467-024-52605-x
Metabolomics. 2024 Sep 21. 20(5): 103

Navigating common pitfalls in metabolite identification and metabolomics bioinformatics.

Elva María Novoa-Del-Toro, Michael Witting.

   BACKGROUND: Metabolomics, the systematic analysis of small molecules in a given biological system, emerged as a powerful tool for different research questions. Newer, better, and faster methods have increased the coverage of metabolites that can be detected and identified in a shorter amount of time, generating highly dense datasets. While technology for metabolomics is still advancing, another rapidly growing field is metabolomics data analysis including metabolite identification. Within the next years, there will be a high demand for bioinformaticians and data scientists capable of analyzing metabolomics data as well as chemists capable of using in-silico tools for metabolite identification. However, metabolomics is often not included in bioinformatics curricula, nor does analytical chemistry address the challenges associated with advanced in-silico tools.
AIM OF REVIEW: In this educational review, we briefly summarize some key concepts and pitfalls we have encountered in a collaboration between a bioinformatician (originally not trained for metabolomics) and an analytical chemist. We identified that many misunderstandings arise from differences in knowledge about metabolite annotation and identification, and the proper use of bioinformatics approaches for these tasks. We hope that this article helps other bioinformaticians (as well as other scientists) entering the field of metabolomics bioinformatics, especially for metabolite identification, to quickly learn the necessary concepts for a successful collaboration with analytical chemists.
KEY SCIENTIFIC CONCEPTS OF REVIEW: We summarize important concepts related to LC-MS/MS based non-targeted metabolomics and compare them with other data types bioinformaticians are potentially familiar with. Drawing these parallels will help foster the learning of key aspects of metabolomics.

Keywords:  Bioinformatics; Data Analysis; LC-MS/MS; Mass Spectrometry; Metabolite Identification; Metabolite databases; Metabolomics

DOI:  https://doi.org/10.1007/s11306-024-02167-2
bioRxiv. 2024 Sep 12. pii: 2024.09.11.612533. [Epub ahead of print]

Electrophoresis-Correlative Ion Mobility Deepens Single-cell Proteomics in Capillary Electrophoresis Mass Spectrometry.

Bowen Shen, Fei Zhou, Peter Nemes.

Detection of trace-sensitive signals is a current challenge is single-cell mass spectrometry (MS) proteomics. Separation prior to detection improves the fidelity and depth of proteome identification and quantification. We recently recognized capillary electrophoresis (CE) electrospray ionization (ESI) for ordering peptides into mass-to-charge (m/z)-dependent series, introducing electrophoresis-correlative (Eco) data-independent acquisition. Here, we demonstrate that these correlations based on electrophoretic mobility (µ ef ) in the liquid phase are transferred into the gas phase, essentially temporally ordering the peptide ions into charge-dependent ion mobility (IM, 1/K 0 ) trends (ρ > 0.97). Rather than sampling the entire IM region broadly, we pursued these predictable correlations to schedule narrower frames. Compared to classical ddaPASEF, Eco-framing significantly enhanced the resolution of IM on a trapped ion mobility mass spectrometer (timsTOF PRO). This approach returned ∼50% more proteins from HeLa proteome digests approximating to one-to-two cells, identifying ∼962 proteins from ∼200 pg in <20 min of effective electrophoresis, without match-between-runs. As a proof of principle, we deployed Eco-ddaPASEF on 1,157 proteins by analyzing <4% of the total proteome in single, yolk-laden embryonic stem cells (∼80-µm) that were isolated from the animal cap of the South African clawed frog ( Xenopus laevis ). Quantitative profiling of 9 different blastomeres revealed detectable differences among these cells, which are normally fated to form the ectoderm but retain pluripotentiality. Eco-framing effectively deepens the proteome sensitivity in IMS using ddaPASEF, raising the possibility of a proteome-driven classification of embryonic cell differentiation.

DOI: https://doi.org/10.1101/2024.09.11.612533
Drug Test Anal. 2024 Sep 22.

High-Throughput Equine Doping Controls on a Trapped Ion Mobility Quadrupole-Time-of-Flight Mass Spectrometer: Technical Considerations of dia/slice/prmPASEF Applied to the Long-Term Detection of Monoclonal Antibodies.

Vivian Delcourt, Justine Pinetre, Benjamin Chabot, Agnès Barnabé, Marie Cacault, Benoit Loup, François Becher, François Fenaille, Marie-Agnès Popot, Patrice Garcia, Ludovic Bailly-Chouriberry.

  Data-independent acquisition (DIA) methods employing a scanning quadrupole were recently described across multiple platforms. These strategies display remarkable performances in untargeted proteomics studies thanks to rapid duty cycles, leading to ultrashort liquid chromatography gradients while maintaining enough data points per peaks when coupled to fast-scanning mass analyzer. In this article, we perform the evaluation of three data acquisition strategies named diaPASEF,slicePASEF, and prmPASEF on a trapped ion mobility spectrometry quadrupole-time-of-flight (TIMS-Q-TOF) mass spectrometer for high-throughput doping control screening analyses. We report that slicePASEF outperforms diaPASEF and is almost as sensitive as prmPASEF in detecting humanized monoclonal antibodies for several weeks in equine plasma after administration. We observed that diaPASEF is still providing the best performances in untargeted proteomics studies employing high amounts of input materials, which is linked with the high complexity of slicePASEF data and current processing algorithms.

Keywords:  TIMS; equine doping controls; ion mobility; monoclonal antibodies

DOI:  https://doi.org/10.1002/dta.3797
Cells. 2024 Sep 19. pii: 1574. [Epub ahead of print]13(18):

Metabolic Reprogramming in Glioblastoma Multiforme: A Review of Pathways and Therapeutic Targets.

Ashley Irin Cortes Ballen, Maryam Amosu, Surya Ravinder, Joey Chan, Emre Derin, Hasan Slika, Betty Tyler.

  Glioblastoma (GBM) is an aggressive and highly malignant primary brain tumor characterized by rapid growth and a poor prognosis for patients. Despite advancements in treatment, the median survival time for GBM patients remains low. One of the crucial challenges in understanding and treating GBMs involves its remarkable cellular heterogeneity and adaptability. Central to the survival and proliferation of GBM cells is their ability to undergo metabolic reprogramming. Metabolic reprogramming is a process that allows cancer cells to alter their metabolism to meet the increased demands of rapid growth and to survive in the often oxygen- and nutrient-deficient tumor microenvironment. These changes in metabolism include the Warburg effect, alterations in several key metabolic pathways including glutamine metabolism, fatty acid synthesis, and the tricarboxylic acid (TCA) cycle, increased uptake and utilization of glutamine, and more. Despite the complexity and adaptability of GBM metabolism, a deeper understanding of its metabolic reprogramming offers hope for developing more effective therapeutic interventions against GBMs.

Keywords:  Warburg effect; glioblastoma multiforme; glycolysis; metabolic reprogramming; therapeutic drugs; tumor microenvironment

DOI:  https://doi.org/10.3390/cells13181574
J Steroid Biochem Mol Biol. 2024 Sep 21. pii: S0960-0760(24)00166-3. [Epub ahead of print]245 106618

Advances and challenges in liquid chromatography-spectrometry (LC-MS) methodology for quantifying androgens and estrogens in human serum and plasma.

Qingqing Wang, Clementina Mesaros.

  Accurate quantification of androgens and estrogens is critical for elucidating their roles in endocrine disorders and advancing research on their functions in human biology and pathophysiology. This review highlights recent advances and ongoing challenges in liquid chromatography- mass spectrometry (LC- MS) methodology for quantifying androgens and estrogens in human serum and plasma. We summarized current approaches for analyzing the different forms of androgens and estrogens, along with their reported levels in publications from 2010 to the present. These published levels pointed out the inconsistencies in reference intervals across studies. To address these issues, advances in derivatization methods and chromatographic separation techniques are reviewed. Future perspectives for improving the accuracy and consistency of hormone quantification in clinical and research settings were also proposed.

Keywords:  Androgens; Chromatographic separation; Derivatization; Estrogens; Liquid chromatography; Mass spectrometry; Steroid hormones bioanalysis

DOI:  https://doi.org/10.1016/j.jsbmb.2024.106618
Methods Protoc. 2024 Sep 07. pii: 71. [Epub ahead of print]7(5):

FAMS-A Targeted Fatty Acid Mass Spectrometry Method for Monitoring Free Fatty Acids from Polysorbate Hydrolysis.

Anja Bathke, Sina Hoelterhoff, Jan Wendler, Inn H Yuk, Christian H Bell.

  Polysorbates are the predominant surfactants used to stabilize protein formulations. Unfortunately, polysorbates can undergo hydrolytic degradation, which releases fatty acids that can accumulate to form visible particles. The detection and quantitation of these fatty acid degradation products are critical for assessing the extent of polysorbate degradation and the associated risks of particle formation. We previously developed a user-friendly mass spectrometric method called Fatty Acids by Mass Spectrometry (FAMS) to quantify the free fatty acids. The FAMS method was validated according to ICH Q2 (R1) guidelines and is suitable for a wide range of products, buffers and protein concentrations. The end-to-end workflow can be automated from sample preparation to data analysis. To broaden method accessibility, the QDa detector selected for fatty acid measurement does not require specific mass spectrometry experience. We provide here a detailed procedure for both manual and automated sample preparation for high-throughput analysis. In addition, we highlight in this protocol the critical operational details, procedural watchouts and troubleshooting tips to support the successful execution of this method in another laboratory.

Keywords:  automation; biopharmaceuticals; free fatty acid; high-throughput; liquid chromatography; mass spectrometry; method validation; polysorbate degradation; single quad

DOI:  https://doi.org/10.3390/mps7050071
Biomed Khim. 2024 Sep;70(5): 356-363

Proteome of plasma extracellular vesicles as a source of colorectal cancer biomarkers.

N A Soloveva, S E Novikova, T E Farafonova, O V Tikhonova, V G Zgoda, A I Archakov.

  The search for minimally invasive methods for diagnostics of colorectal cancer (CRC) is the most important task for early diagnostics of the disease and subsequent successful treatment. Human plasma represents the main type of biological material used in the clinical practice; however, the complex dynamic range of substances circulating in it complicates determination of CRC protein markers by the mass spectrometric (MS) method. Studying the proteome of extracellular vesicles (EVs) isolated from human plasma represents an attractive approach for the discovery of tissue-secreted CRC markers. We performed shotgun mass spectrometry analysis of EV samples obtained from plasma of CRC patients and healthy volunteers. This MS analysis resulted in identification of 370 proteins (which were registered by at least two peptides). Stable isotope-free relative quantitation identified 55 proteins with altered abundance in EV samples obtained from plasma samples of CRC patients as compared to healthy controls. Among the EV proteins isolated from blood plasma we found components involved in cell adhesion and the VEGFA-VEGFR2 signaling pathway (TLN1, HSPA8, VCL, MYH9, and others), as well as proteins expressed predominantly by gastrointestinal tissues (polymeric immunoglobulin receptor, PIGR). The data obtained using the shotgun proteomic profiling may be added to the panel for targeted MS analysis of EV-associated protein markers, previously developed using CRC cell models.

Keywords:  colorectal cancer; extracellular vesicles (EVs); human plasma; shotgun mass spectrometric analysis

DOI:  https://doi.org/10.18097/PBMC20247005356
Anal Chem. 2024 Sep 25.

Theoretical Assessment of Indistinguishable Peptides in Mass Spectrometry-Based Proteomics.

Zahra Elhamraoui, Eva Borràs, Mathias Wilhelm, Eduard Sabidó.

Mass-spectrometry-based proteomics has advanced with the integration of experimental and predicted spectral libraries, which have significantly improved peptide identification in complex search spaces. However, challenges persist in distinguishing some peptides with close retention times and nearly identical fragmentation patterns. In this study, we conducted a theoretical assessment to quantify the prevalence of indistinguishable peptides within the human canonical proteome and immunopeptidome using state-of-the-art retention time and spectrum prediction models. By quantifying the proportion of peptides posing challenges to unequivocal identification, we set the theoretical nonaccessible portion within a given proteome, and underscore the effectiveness of contemporary analytical methodologies in resolving the complexity of the human proteome and immunopeptidome via mass spectrometry.

DOI: https://doi.org/10.1021/acs.analchem.4c02803
Metabolites. 2024 Aug 30. pii: 479. [Epub ahead of print]14(9):

Impact of Phenylketonuria on the Serum Metabolome and Plasma Lipidome: A Study in Early-Treated Patients.

Jorine C van der Weerd, Annemiek M J van Wegberg, Theo S Boer, Udo F H Engelke, Karlien L M Coene, Ron A Wevers, Stephan J L Bakker, Pim de Blaauw, Joost Groen, Francjan J van Spronsen, M Rebecca Heiner-Fokkema.

   BACKGROUND: Data suggest that metabolites, other than blood phenylalanine (Phe), better and independently predict clinical outcomes in patients with phenylketonuria (PKU).
METHODS: To find new biomarkers, we compared the results of untargeted lipidomics and metabolomics in treated adult PKU patients to those of matched controls. Samples (lipidomics in EDTA-plasma (22 PKU and 22 controls) and metabolomics in serum (35 PKU and 20 controls)) were analyzed using ultra-high-performance liquid chromatography and high-resolution mass spectrometry. Data were subjected to multivariate (PCA, OPLS-DA) and univariate (Mann-Whitney U test, p < 0.05) analyses.
RESULTS: Levels of 33 (of 20,443) lipid features and 56 (of 5885) metabolite features differed statistically between PKU patients and controls. For lipidomics, findings include higher glycerolipids, glycerophospholipids, and sphingolipids species. Significantly lower values were found for sterols and glycerophospholipids species. Seven features had unknown identities. Total triglyceride content was higher. Higher Phe and Phe catabolites, tryptophan derivatives, pantothenic acid, and dipeptides were observed for metabolomics. Ornithine levels were lower. Twenty-six metabolite features were not annotated.
CONCLUSIONS: This study provides insight into the metabolic phenotype of PKU patients. Additional studies are required to establish whether the observed changes result from PKU itself, diet, and/or an unknown reason.

Keywords:  lipidomics; mass-spectrometry; metabolomics; phenylalanine; phenylketonuria; untargeted

DOI:  https://doi.org/10.3390/metabo14090479
Mol Metab. 2024 Sep 19. pii: S2212-8778(24)00163-7. [Epub ahead of print] 102032

Linking metabolism and histone acetylation dynamics by integrated metabolic flux analysis of Acetyl-CoA and histone acetylation sites.

Anna-Sophia Egger, Eva Rauch, Suraj Sharma, Tobias Kipura, Madlen Hotze, Thomas Mair, Alina Hohenegg, Philipp Kobler, Ines Heiland, Marcel Kwiatkowski.

  Histone acetylation is an important epigenetic modification that regulates various biological processes and cell homeostasis. Acetyl-CoA, a hub molecule of metabolism, is the substrate for histone acetylation, thus linking metabolism with epigenetic regulation. However, still relatively little is known about the dynamics of histone acetylation and its dependence on metabolic processes, due to the lack of integrated methods that can capture site-specific histone acetylation and deacetylation reactions together with the dynamics of acetyl-CoA synthesis. In this study, we present a novel proteo-metabo-flux approach that combines mass spectrometry-based metabolic flux analysis of acetyl-CoA and histone acetylation with computational modelling. We developed a mathematical model to describe metabolic label incorporation into acetyl-CoA and histone acetylation based on experimentally measured relative abundances. We demonstrate that our approach is able to determine acetyl-CoA synthesis dynamics and site-specific histone acetylation and deacetylation reaction rate constants, and that consideration of the metabolically labelled acetyl-CoA fraction is essential for accurate determination of histone acetylation dynamics. Furthermore, we show that without correction, changes in metabolic fluxes would be misinterpreted as changes in histone acetylation dynamics, whereas our proteo-metabo-flux approach allows to distinguish between the two processes.

Keywords:  Computational modelling; Epigenetics; Histone modifications; LC-MS; Metabolic flux analysis; Metabolism

DOI:  https://doi.org/10.1016/j.molmet.2024.102032
Nat Commun. 2024 Sep 27. 15(1): 8301

Specific activation of the integrated stress response uncovers regulation of central carbon metabolism and lipid droplet biogenesis.

Katherine Labbé, Lauren LeBon, Bryan King, Ngoc Vu, Emily H Stoops, Nina Ly, Austin E Y T Lefebvre, Phillip Seitzer, Swathi Krishnan, Jin-Mi Heo, Bryson Bennett, Carmela Sidrauski.

The integrated stress response (ISR) enables cells to cope with a variety of insults, but its specific contribution to downstream cellular outputs remains unclear. Using a synthetic tool, we selectively activate the ISR without co-activation of parallel pathways and define the resulting cellular state with multi-omics profiling. We identify time- and dose-dependent gene expression modules, with ATF4 driving only a small but sensitive subgroup that includes amino acid metabolic enzymes. This ATF4 response affects cellular bioenergetics, rerouting carbon utilization towards amino acid production and away from the tricarboxylic acid cycle and fatty acid synthesis. We also find an ATF4-independent reorganization of the lipidome that promotes DGAT-dependent triglyceride synthesis and accumulation of lipid droplets. While DGAT1 is the main driver of lipid droplet biogenesis, DGAT2 plays an essential role in buffering stress and maintaining cell survival. Together, we demonstrate the sufficiency of the ISR in promoting a previously unappreciated metabolic state.

DOI: https://doi.org/10.1038/s41467-024-52538-5
J Lipid Res. 2024 Sep 18. pii: S0022-2275(24)00152-4. [Epub ahead of print] 100647

NOVEL EICOSANOID SIGNATURE IN PLASMA PROVIDES DIAGNOSTIC FOR METABOLIC DYSFUNCTION-ASSOCIATED STEATOTIC LIVER DISEASE.

Oswald Quehenberger, Aaron M Armando, Tiffany H Cedeno, Rohit Loomba, Arun J Sanyal, Edward A Dennis.

  There is a clinical need for a simple test implementable at the primary point of care to identify individuals with metabolic dysfunction-associated steatotic liver disease (MASLD) in the population. Blood plasma samples from adult patients with varying phenotypes of MASLD were used to identify a minimal set of lipid analytes reflective of underlying histologically confirmed MASLD. Samples were obtained from the NIDDK Nonalcoholic Steatohepatitis Clinical Research Network (NASH CRN) NAFLD Database prospective cohort study (MASLD group; N = 301). Samples of control subjects were obtained from cohort studies at the University of California San Diego (control group; N = 48). Plasma samples were utilized for targeted quantitation of circulating eicosanoids, related bioactive metabolites, and polyunsaturated fatty acids by ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) lipidomics analysis. Bioinformatic approaches were used to discover a panel of bioactive lipids that can be used as a diagnostic tool to identify MASLD. The final panel of fifteen lipid metabolites consists of 12 eicosanoid metabolites and 3 free fatty acids that were identified to be predictive for MASLD by multivariate area under the receiver operating characteristics curve (AUROC) analysis. The panel was highly predictive for MASLD with an AUROC of 0.999 (95% CI = 0.986-1.0) with only one control misclassified. While a validation study is included, a prospective larger scale study with matched controls will be required to optimize the resulting MASLD LIPIDOMICS SCORE to become a non-invasive "point-of-care" test to identify MASLD patients requiring further evaluation for the presence of metabolic dysfunction-associated steatohepatitis (MASH).

Keywords:  Fatty Liver Disease; Lipidomics; MASH; MASLD; NAFL; NAFLD; NASH; arachidonic acid metabolism; eicosanoids; inflammation

DOI:  https://doi.org/10.1016/j.jlr.2024.100647
Commun Biol. 2024 Sep 25. 7(1): 1189

EXCRETE workflow enables deep proteomics of the microbial extracellular environment.

David A Russo, Denys Oliinyk, Georg Pohnert, Florian Meier, Julie A Z Zedler.

Extracellular proteins play a significant role in shaping microbial communities which, in turn, can impact ecosystem function, human health, and biotechnological processes. Yet, for many ubiquitous microbes, there is limited knowledge regarding the identity and function of secreted proteins. Here, we introduce EXCRETE (enhanced exoproteome characterization by mass spectrometry), a workflow that enables comprehensive description of microbial exoproteomes from minimal starting material. Using cyanobacteria as a case study, we benchmark EXCRETE and show a significant increase over current methods in the identification of extracellular proteins. Subsequently, we show that EXCRETE can be miniaturized and adapted to a 96-well high-throughput format. Application of EXCRETE to cyanobacteria from different habitats (Synechocystis sp. PCC 6803, Synechococcus sp. PCC 11901, and Nostoc punctiforme PCC 73102), and in different cultivation conditions, identified up to 85% of all potentially secreted proteins. Finally, functional analysis reveals that cell envelope maintenance and nutrient acquisition are central functions of the predicted cyanobacterial secretome. Collectively, these findings challenge the general belief that cyanobacteria lack secretory proteins and suggest that multiple functions of the secretome are conserved across freshwater, marine, and terrestrial species.

DOI: https://doi.org/10.1038/s42003-024-06910-2
JCI Insight. 2024 Aug 13. pii: e180114. [Epub ahead of print]9(18):

Metabolic landscape of the healthy pancreas and pancreatic tumor microenvironment.

Monica E Bonilla, Megan D Radyk, Matthew D Perricone, Ahmed M Elhossiny, Alexis C Harold, Paola I Medina-Cabrera, Padma Kadiyala, Jiaqi Shi, Timothy L Frankel, Eileen S Carpenter, Michael D Green, Cristina Mitrea, Costas A Lyssiotis, Marina Pasca di Magliano.

  Pancreatic cancer, one of the deadliest human malignancies, is characterized by a fibro-inflammatory tumor microenvironment and wide array of metabolic alterations. To comprehensively map metabolism in a cell type-specific manner, we harnessed a unique single-cell RNA-sequencing dataset of normal human pancreata. This was compared with human pancreatic cancer samples using a computational pipeline optimized for this study. In the cancer cells we observed enhanced biosynthetic programs. We identified downregulation of mitochondrial programs in several immune populations, relative to their normal counterparts in healthy pancreas. Although granulocytes, B cells, and CD8+ T cells all downregulated oxidative phosphorylation, the mechanisms by which this occurred were cell type specific. In fact, the expression pattern of the electron transport chain complexes was sufficient to identify immune cell types without the use of lineage markers. We also observed changes in tumor-associated macrophage (TAM) lipid metabolism, with increased expression of enzymes mediating unsaturated fatty acid synthesis and upregulation in cholesterol export. Concurrently, cancer cells exhibited upregulation of lipid/cholesterol receptor import. We thus identified a potential crosstalk whereby TAMs provide cholesterol to cancer cells. We suggest that this may be a new mechanism boosting cancer cell growth and a therapeutic target in the future.

Keywords:  Bioinformatics; Cancer; Macrophages; Oncology

DOI:  https://doi.org/10.1172/jci.insight.180114
Mol Metab. 2024 Sep 25. pii: S2212-8778(24)00168-6. [Epub ahead of print] 102037

Identifying targetable metabolic dependencies across colorectal cancer progression.

Danny N Legge, Tracey J Collard, Ewelina Stanko, Ashley J Hoskin, Amy K Holt, Caroline J Bull, Madhu Kollareddy, Jake Bellamy, Sarah Groves, Eric H Ma, Emma Hazelwood, David Qualtrough, Borko Amulic, Karim Malik, Ann C Williams, Nicholas Jones, Emma E Vincent.

  Colorectal cancer (CRC) is a multi-stage process initiated through the formation of a benign adenoma, progressing to an invasive carcinoma and finally metastatic spread. Tumour cells must adapt their metabolism to support the energetic and biosynthetic demands associated with disease progression. As such, targeting cancer cell metabolism is a promising therapeutic avenue in CRC. However, to identify tractable nodes of metabolic vulnerability specific to CRC stage, we must understand how metabolism changes during CRC development. Here, we use a unique model system - comprising human early adenoma to late adenocarcinoma. We show that adenoma cells transition to elevated glycolysis at the early stages of tumour progression but maintain oxidative metabolism. Progressed adenocarcinoma cells rely more on glutamine-derived carbon to fuel the TCA cycle, whereas glycolysis and TCA cycle activity remain tightly coupled in early adenoma cells. Adenocarcinoma cells are more flexible with respect to fuel source, enabling them to proliferate in nutrient-poor environments. Despite this plasticity, we identify asparagine (ASN) synthesis as a node of metabolic vulnerability in late-stage adenocarcinoma cells. We show that loss of asparagine synthetase (ASNS) blocks their proliferation, whereas early adenoma cells are largely resistant to ASN deprivation. Mechanistically, we show that late-stage adenocarcinoma cells are dependent on ASNS to support mTORC1 signalling and maximal glycolytic and oxidative capacity. Resistance to ASNS loss in early adenoma cells is likely due to a feedback loop, absent in late-stage cells, allowing them to sense and regulate ASN levels and supplement ASN by autophagy. Together, our study defines metabolic changes during CRC development and highlights ASN synthesis as a targetable metabolic vulnerability in later stage disease.

Keywords:  Colorectal cancer; adenocarcinoma; adenoma; asparagine; asparagine synthetase; oncometabolism

DOI:  https://doi.org/10.1016/j.molmet.2024.102037
Metabolites. 2024 Aug 28. pii: 474. [Epub ahead of print]14(9):

An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples.

Amy Thachil, Li Wang, Rupasri Mandal, David Wishart, Tom Blydt-Hansen.

  Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize variation, there is a need to create and adhere to standardized pre-analytical protocols for blood samples intended for use in metabolomics analyses. This will allow for reliable and reproducible findings within blood metabolomics research. In this review article, we provide an overview of the existing literature pertaining to pre-analytical factors that influence blood metabolite measurements. Pre-analytical factors including blood tube selection, pre- and post-processing time and temperature conditions, centrifugation conditions, freeze-thaw cycles, and long-term storage conditions are specifically discussed, with recommendations provided for best practices at each stage.

Keywords:  blood; metabolomics; pre-analytical; protocol; sample processing

DOI:  https://doi.org/10.3390/metabo14090474
Clin Chem Lab Med. 2024 Sep 26.

Targeted MRM-analysis of plasma proteins in frozen whole blood samples from patients with COVID-19: a retrospective study.

Anna E Bugrova, Polina A Strelnikova, Alexey S Kononikhin, Natalia V Zakharova, Elizaveta O Diyachkova, Alexander G Brzhozovskiy, Maria I Indeykina, Ilya N Kurochkin, Alexander V Averyanov, Evgeny N Nikolaev.

   OBJECTIVES: The COVID-19 pandemic has exposed a number of key challenges that need to be urgently addressed. Mass spectrometric studies of blood plasma proteomics provide a deep understanding of the relationship between the severe course of infection and activation of specific pathophysiological pathways. Analysis of plasma proteins in whole blood may also be relevant for the pandemic as it requires minimal sample preparation.
METHODS: The frozen whole blood samples were used to analyze 203 plasma proteins using multiple reaction monitoring (MRM) mass spectrometry and stable isotope-labeled peptide standards (SIS). A total of 131 samples (FRCC, Russia) from patients with mild (n=41), moderate (n=39) and severe (n=19) COVID-19 infection and healthy controls (n=32) were analyzed.
RESULTS: Levels of 94 proteins were quantified and compared. Significant differences between all of the groups were revealed for 44 proteins. Changes in the levels of 61 reproducible COVID-19 markers (SERPINA3, SERPING1, ORM1, HRG, LBP, APOA1, AHSG, AFM, ITIH2, etc.) were consistent with studies performed with serum/plasma samples. The best-performing classifier built with 10 proteins achieved the best combination of ROC-AUC (0.97-0.98) and accuracy (0.90-0.93) metrics and distinguished patients from controls, as well as patients by severity.
CONCLUSIONS: Here, for the first time, frozen whole blood samples were used for proteomic analysis and assessment of the status of patients with COVID-19. The results obtained with frozen whole blood samples are consistent with those from plasma and serum.

Keywords:  COVID-19; blood proteomics; markers; mass spectrometry; pandemic; severity

DOI:  https://doi.org/10.1515/cclm-2024-0800
bioRxiv. 2024 Sep 09. pii: 2024.09.09.612087. [Epub ahead of print]

A secondary β-hydroxybutyrate metabolic pathway linked to energy balance.

Maria Dolores Moya-Garzon, Mengjie Wang, Veronica L Li, Xuchao Lyu, Wei Wei, Alan Sheng-Hwa Tung, Steffen H Raun, Meng Zhao, Laetitia Coassolo, Hashim Islam, Barbara Oliveira, Yuqin Dai, Jan Spaas, Antonio Delgado-Gonzalez, Kenyi Donoso, Aurora Alvarez-Buylla, Francisco Franco-Montalban, Anudari Letian, Catherine Ward, Lichao Liu, Katrin J Svensson, Emily L Goldberg, Christopher D Gardner, Jonathan P Little, Steven M Banik, Yong Xu, Jonathan Z Long.

β-hydroxybutyrate (BHB) is an abundant ketone body. To date, all known pathways of BHB metabolism involve interconversion of BHB and primary energy intermediates. Here we show that CNDP2 controls a previously undescribed secondary BHB metabolic pathway via enzymatic conjugation of BHB and free amino acids. This BHB-ylation reaction produces a family of endogenous ketone metabolites, the BHB-amino acids. Genetic ablation of CNDP2 in mice eliminates tissue amino acid BHB-ylation activity and reduces BHB-amino acid levels. Administration of BHB-Phe, the most abundant BHB-amino acid, to obese mice activates neural populations in the hypothalamus and brainstem and suppresses feeding and body weight. Conversely, CNDP2-KO mice exhibit increased food intake and body weight upon ketosis stimuli. CNDP2-dependent amino acid BHB-ylation and BHB-amino acid metabolites are also conserved in humans. Therefore, the metabolic pathways of BHB extend beyond primary metabolism and include secondary ketone metabolites linked to energy balance.

DOI: https://doi.org/10.1101/2024.09.09.612087