bims-mascan 2023-07-02 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2023–07–02
39 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Intensity-dependent mass search for improving metabolite database matches in chemical isotope labeling LC-QTOF-MS-based metabolomics.
Assessing the Phagosome Proteome by Quantitative Mass Spectrometry.
Single-cell omic molecular profiling using capillary electrophoresis-mass spectrometry.
Evaluation of the Orbitrap Ascend Tribrid Mass Spectrometer for Shotgun Proteomics.
Protocol to identify human subcellular alternative protein interactions using cross-linking mass spectrometry.
TopNEXt: Automatic DDA Exclusion Framework for Multi-Sample Mass Spectrometry Experiments.
Targeting Fatty Acid Reprogramming Suppresses CARM1-expressing Ovarian Cancer.
Cancer metabolites: promising biomarkers for cancer liquid biopsy.
Assessment of a 60-Biomarker Health Surveillance Panel (HSP) on Whole Blood from Remote Sampling Devices by Targeted LC/MRM-MS and Discovery DIA-MS Analysis.
Protocol for deep proteomic profiling of formalin-fixed paraffin-embedded specimens using a spectral library-free approach.
Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer.
Potential Diagnostic Biomarker Detection for Prostate Cancer Using Untargeted and Targeted Metabolomic Profiling.
Ferroptosis of immune cells in the tumor microenvironment.
Critical role of antioxidant programs in enzalutamide-resistant prostate cancer.
Utilization of LC-MS/MS and Drift Tube Ion Mobility for Characterizing Intact Oxidized Arachidonate-Containing Glycerophosphatidylethanolamine.
An integrated deep learning framework for the interpretation of untargeted metabolomics data.
The Lipogenic Enzyme Acetoacetyl-Coenzyme A Synthetase and Ketone Body Utilization for Denovo Lipid Synthesis, a Review.
Metabolomics and lipidomics studies of parasitic helminths: molecular diversity and identification levels achieved by using different characterisation tools.
Set up and validation of a sensitive method to quantify prostaglandins, prostaglandin-glycerol esters and prostaglandin-ethanolamides, as well as their respective precursors.
Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent versus Infrequent Gout Flares.
Fatty Acid Metabolites and the Tumor Microenvironment as Potent Regulators of Cancer Stem Cell Signaling.
Assisted Reductive Amination for Quantitation of Tryptophan, 5-Hydroxytryptophan, and Serotonin by Ultraperformance Liquid Chromatography Coupled with Tandem Mass Spectrometry.
Spatial lipidomics and metabolomics of multicellular tumor spheroids using MALDI-2 and trapped ion mobility imaging.
The Role of CD36 in Cancer Progression and Its Value as a Therapeutic Target.
Single-cell proteomics: quantifying post-transcriptional regulation during development with mass-spectrometry.
Novel Metabolomic Approach for Identifying Pathology-Specific Biomarkers in Rare Diseases: A Case Study in Oculopharyngeal Muscular Dystrophy (OPMD).
Unveiling the MUFA-Cancer Connection: Insights from Endogenous and Exogenous Perspectives.
Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics.
Impact of renal tubular Cpt1a overexpression on the kidney metabolome in the folic acid-induced fibrosis mouse model.
Exercise Metabolome: Insights for Health and Performance.
Accelerating Open Modification Spectral Library Searching on Tensor Core in High-dimensional Space.
Lipidomics Profiling and Risk of Coronary Artery Disease in the BioHEART-CT Discovery Cohort.
Aurora kinase A/AURKA functionally interacts with the mitochondrial ATP synthase to regulate energy metabolism and cell death.
Shutting off the fuel supply to target metabolic vulnerabilities in multiple myeloma.
Unified workflow for the rapid and in-depth characterization of bacterial proteomes.
Omics-Based Investigations of Breast Cancer.
Beggars banquet: Metabolism in the tumor immune microenvironment and cancer therapy.
Affi-BAMS™: A Robust Targeted Proteomics Microarray Platform to Measure Histone Post-Translational Modifications.
Data-Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics.

Anal Chim Acta. 2023 Sep 01. pii: S0003-2670(23)00688-8. [Epub ahead of print]1272 341467

Intensity-dependent mass search for improving metabolite database matches in chemical isotope labeling LC-QTOF-MS-based metabolomics.

Yunong Li, Hao Li, Taibo Luo, Guohui Lin, Liang Li.

  Liquid chromatography mass spectrometry (LC-MS) has been increasingly used for metabolome analysis. One of the critical steps in the LC-MS metabolome analysis workflow is related to metabolite identification. Among the measured parameters, peak mass is commonly used to search against a database for potential metabolite matches. Higher accuracy mass measurement allows the use of a narrower mass tolerance window for mass search. While various types of mass analyzers can routinely measure a peak mass with an error of less than a few ppm, mass measurement accuracy is not uniform for peaks with different intensities, particularly for quadrupole time-of-flight (QTOF) MS. Herein we present a simple and convenient method to determine the relation between peak intensity and mass error in LC-QTOF-MS-based metabolome analysis, followed by intensity-dependent mass search (IDMS) of a database for metabolite matches. This method is based on running a series of sodium formate mass calibrants, as part of the standard operating procedure (SOP) in LC-MS data acquisition, and then curve-fitting the measured mass errors and peak intensities. We show that, in two different quadrupole time-of-flight (QTOF) mass analyzers, mass accuracy is generally reduced as peak intensity decreases, which is independent of m/z values in the range commonly used for metabolite detection (e.g., m/z < 1000). We demonstrate the improvement in metabolite matches using IDMS in the analyses of dansyl labeled standards and human urine samples. We have implemented the IDMS method in the freely available MCID database at www.mycompoundid.org, which is composed of 8021 known human endogenous metabolites and their predicted metabolic products (375,809 compounds from one metabolic reaction and 10,583,901 compounds from two reactions).

Keywords:  Chemical derivatization; Database search; Mass spectrometry; Metabolite identification; Metabolomics; QTOF

DOI:  https://doi.org/10.1016/j.aca.2023.341467
Methods Mol Biol. 2023 ;2692 361-374

Assessing the Phagosome Proteome by Quantitative Mass Spectrometry.

Maria Emilia Dueñas, José Luis Marín-Rubio, Julien Peltier-Heap, Anetta Hartlova, Matthias Trost.

  The process of phagocytosis involves a series of defined steps, including the formation of a new intracellular organelle, i.e., the phagosome, and the maturation of the phagosome by fusion with endosomes and lysosomes to produce an acidic and proteolytic environment in which the pathogens are degraded. Phagosome maturation is associated with significant changes in the proteome of phagosomes due to the acquisition of new proteins or enzymes, post-translational modifications of existing proteins, as well as other biochemical changes that ultimately lead to the degradation or processing of the phagocytosed particle. Phagosomes are highly dynamic organelles formed by the uptake of particles through phagocytic innate immune cells; thus characterization of the phagosomal proteome is essential to understand the mechanisms controlling innate immunity, as well as vesicle trafficking. In this chapter, we describe how novel quantitative proteomics methods, such as using tandem mass tag (TMT) labelling or acquiring label-free data using data-independent acquisition (DIA), can be applied for the characterization of protein composition of phagosomes in macrophages.

Keywords:  Data-dependent acquisition (DDA); Data-independent acquisition (DIA); Macrophages; Mass spectrometry (MS); Phagosomes; Quantitative proteomics; Tandem mass tag (TMT)

DOI:  https://doi.org/10.1007/978-1-0716-3338-0_23
Trends Analyt Chem. 2023 Aug;pii: 117117. [Epub ahead of print]165

Single-cell omic molecular profiling using capillary electrophoresis-mass spectrometry.

Ketki Bagwe, Noah Gould, Kendall R Johnson, Alexander R Ivanov.

  Tissues and other cell populations are highly heterogeneous at the cellular level, owing to differences in expression and modifications of proteins, polynucleotides, metabolites, and lipids. The ability to assess this heterogeneity is crucial in understanding numerous biological phenomena, including various pathologies. Traditional analyses apply bulk-cell sampling, which masks the potentially subtle differences between cells that can be important in understanding of biological processes. These limitations due to cell heterogeneity inspired significant efforts and interest toward the analysis of smaller sample sizes, down to the level of individual cells. Among the emerging techniques, the unique capabilities of capillary electrophoresis coupled with mass spectrometry (CE-MS) made it a prominent technique for proteomics and metabolomics analysis at the single-cell level. In this review, we focus on the application of CE-MS in the proteomic and metabolomic profiling of single cells and highlight the recent advances in sample preparation, separation, MS acquisition, and data analysis.

Keywords:  capillary electrophoresis; capillary electrophoresis-mass spectrometry; limited sample; mass spectrometry; metabolomics; proteomics; single-cell proteomics

DOI:  https://doi.org/10.1016/j.trac.2023.117117
Anal Chem. 2023 Jun 30.

Evaluation of the Orbitrap Ascend Tribrid Mass Spectrometer for Shotgun Proteomics.

Yuchen He, Evgenia Shishkova, Trenton M Peters-Clarke, Dain R Brademan, Michael S Westphall, David Bergen, Jingjing Huang, Romain Huguet, Michael W Senko, Vlad Zabrouskov, Graeme C McAlister, Joshua J Coon.

Mass spectrometry (MS)-based proteomics is a powerful technology to globally profile protein abundances, activities, interactions, and modifications. The extreme complexity of proteomics samples, which often contain hundreds of thousands of analytes, necessitates continuous development of MS techniques and instrumentation to improve speed, sensitivity, precision, and accuracy, among other analytical characteristics. Here, we systematically evaluated the Orbitrap Ascend Tribrid mass spectrometer in the context of shotgun proteomics, and we compared its performance to that of the previous generation of Tribrid instruments─the Orbitrap Eclipse. The updated architecture of the Orbitrap Ascend includes a second ion-routing multipole (IRM) in front of the redesigned C-trap/Orbitrap and a new ion funnel that allows gentler ion introduction, among other changes. These modifications in Ascend hardware configuration enabled an increase in parallelizable ion injection time during higher-energy collisional dissociation (HCD) Orbitrap tandem MS (FTMS2) analysis of ∼5 ms. This enhancement was particularly valuable in the analyses of limited sample amounts, where improvements in sensitivity resulted in up to 140% increase in the number of identified tryptic peptides. Further, analysis of phosphorylated peptides enriched from the K562 human cell line yielded up to ∼50% increase in the number of unique phosphopeptides and localized phosphosites. Strikingly, we also observed a ∼2-fold boost in the number of detected N-glycopeptides, likely owing to the improvements in ion transmission and sensitivity. In addition, we performed the multiplexed quantitative proteomics analyses of TMT11-plex labeled HEK293T tryptic peptides and observed 9-14% increase in the number of quantified peptides. In conclusion, the Orbitrap Ascend consistently outperformed its predecessor the Orbitrap Eclipse in various bottom-up proteomic analyses, and we anticipate that it will generate reproducible and in-depth datasets for numerous proteomic applications.

DOI: https://doi.org/10.1021/acs.analchem.3c01155
STAR Protoc. 2023 Jun 28. pii: S2666-1667(23)00347-7. [Epub ahead of print]4(3): 102380

Protocol to identify human subcellular alternative protein interactions using cross-linking mass spectrometry.

Diego Fernando Garcia-Del Rio, Isabelle Fournier, Tristan Cardon, Michel Salzet.

  Since the start of mass-spectrometry-based proteomics, proteins from non-referenced open reading frames or alternative proteins (AltProts) have been overlooked. Here, we present a protocol to identify human subcellular AltProt and decipher some interactions using cross-linking mass spectrometry. We describe steps for cell culture, in cellulo cross-link, subcellular extraction, and sequential digestion. We then detail both liquid chromatography-tandem mass spectrometry and cross-link data analyses. The implementation of a single workflow allows the non-targeted identification of signaling pathways involving AltProts. For complete details on the use and execution of this protocol, please refer to Garcia-del Rio et al.1.

Keywords:  Bioinformatics; Mass Spectrometry; Proteomics; Systems Biology

DOI:  https://doi.org/10.1016/j.xpro.2023.102380
Bioinformatics. 2023 Jun 26. pii: btad406. [Epub ahead of print]

TopNEXt: Automatic DDA Exclusion Framework for Multi-Sample Mass Spectrometry Experiments.

Ross McBride, Joe Wandy, Stefan Weidt, Simon Rogers, Vinny Davies, Rónán Daly, Kevin Bryson.

MOTIVATION: Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) experiments aim to produce high quality fragmentation spectra which can be used to annotate metabolites. However, current Data-Dependent Acquisition (DDA) approaches may fail to collect spectra of sufficient quality and quantity for experimental outcomes, and extend poorly across multiple samples by failing to share information across samples or by requiring manual expert input.
RESULTS: We present TopNEXt, a real-time scan prioritisation framework that improves data acquisition in multi-sample LC-MS/MS metabolomics experiments. TopNEXt extends traditional DDA exclusion methods across multiple samples by using a Region of Interest (RoI) and intensity-based scoring system. Through both simulated and lab experiments we show that methods incorporating these novel concepts acquire fragmentation spectra for an additional 10% of our set of target peaks and with an additional 20% of acquisition intensity. By increasing the quality and quantity of fragmentation spectra, TopNEXt can help improve metabolite identification with a potential impact across a variety of experimental contexts.
AVAILABILITY: TopNEXt is implemented as part of the ViMMS framework and the latest version can be found at https://github.com/glasgowcompbio/vimms. A stable version used to produce our results can be found at 10.5281/zenodo.7468914. Data can be found at 10.5525/gla.researchdata.1382.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btad406
Cancer Res Commun. 2023 Jun;3(6): 1067-1077

Targeting Fatty Acid Reprogramming Suppresses CARM1-expressing Ovarian Cancer.

Simona Lombardi, Aaron R Goldman, Hsin-Yao Tang, Andrew V Kossenkov, Heng Liu, Wei Zhou, Meenhard Herlyn, Jianhuang Lin, Rugang Zhang.

The arginine methyltransferase CARM1 exhibits high expression levels in several human cancers, with the trend also observed in ovarian cancer. However, therapeutic approaches targeting tumors that overexpress CARM1 have not been explored. Cancer cells exploit metabolic reprogramming such as fatty acids for their survival. Here we report that CARM1 promotes monounsaturated fatty acid synthesis and fatty acid reprogramming represents a metabolic vulnerability for CARM1-expressing ovarian cancer. CARM1 promotes the expression of genes encoding rate-limiting enzymes of de novo fatty acid metabolism such as acetyl-CoA carboxylase 1 (ACC1) and fatty acid synthase (FASN). In addition, CARM1 upregulates stearoyl-CoA desaturase 1 (SCD1) that produces monounsaturated fatty acid by desaturation. Thus, CARM1 enhances de novo fatty acids synthesis which was subsequently utilized for synthesis of monounsaturated fatty acids. Consequently, inhibition of SCD1 suppresses the growth of ovarian cancer cells in a CARM1 status-dependent manner, which was rescued by the addition of monounsaturated fatty acids. Consistently, CARM1-expressing cells were more tolerant to the addition of saturated fatty acids. Indeed, SCD1 inhibition demonstrated efficacy against ovarian cancer in both orthotopic xenograft and syngeneic mouse models in a CARM1-dependent manner. In summary, our data show that CARM1 reprograms fatty acid metabolism and targeting SCD1 through pharmacological inhibition can serve as a potent therapeutic approach for CARM1-expressing ovarian cancers.
Significance: CARM1 reprograms fatty acid metabolism transcriptionally to support ovarian cancer growth by producing monounsaturated fatty acids, supporting SCD1 inhibition as a rational strategy for treating CARM1-expressing ovarian cancer.

DOI: https://doi.org/10.1158/2767-9764.CRC-23-0030
Biomark Res. 2023 Jun 30. 11(1): 66

Cancer metabolites: promising biomarkers for cancer liquid biopsy.

Wenxiang Wang, Zhiwei Rong, Guangxi Wang, Yan Hou, Fan Yang, Mantang Qiu.

  Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

Keywords:  Biomarkers; Cancer diagnosis; Circulating metabolites; Liquid biopsy

DOI:  https://doi.org/10.1186/s40364-023-00507-3
Anal Chem. 2023 Jun 30.

Assessment of a 60-Biomarker Health Surveillance Panel (HSP) on Whole Blood from Remote Sampling Devices by Targeted LC/MRM-MS and Discovery DIA-MS Analysis.

Stephen A Whelan, Nathan Hendricks, Zachary L Dwight, Qin Fu, Annie Moradian, Jennifer E Van Eyk, Susan M Mockus.

Telehealth, accessing healthcare and wellness remotely, should be a cost-effective and efficient way for individuals to receive care. The convenience of having a reliable remote collection device for blood tests will facilitate access to precision medicine and healthcare. Herein, we tested a 60-biomarker health surveillance panel (HSP), containing 35 FDA/LDT assays and covering at least 14 pathological states, on 8 healthy individuals' ability to collect their own capillary blood from a lancet finger prick and directly compared it to the traditional phlebotomist venous blood and plasma collection methods. All samples were spiked with 114 stable-isotope-labeled (SIL) HSP peptides and quantitatively analyzed by liquid chromatography-multiple reaction monitoring-mass spectrometry (LC/MRM-MS) scheduled method targeting 466 transitions from 114 HSP peptides and by a discovery data-independent acquisition mass spectrometry (DIA-MS) method. The average peak area ratio (PAR) of the HSP quantifier peptide transitions from all 8 volunteers' capillary blood (n = 48), venous blood (n = 48), and matched plasma (n = 24) was <20% coefficients of variation (CV). Heat map analysis of all 8 volunteers demonstrated that each individual had a unique biosignature. Biological replicates from capillary blood and venous blood clustered within each volunteer in k-means clustering analysis. Pearson statistical analysis of the three biofluids indicated that there was >90% similarity. Discovery DIA-MS analysis of the same samples using a plasma spectral library and a pan-human spectral library identified 1121 and 4661 total proteins, respectively. In addition, at least 122 FDA-approved biomarkers were identified. DIA-MS analysis reproducibly quantitated (<30% CV) ∼600-700 proteins in capillary blood, ∼800 proteins in venous blood, and ∼300-400 proteins in plasma, demonstrating that an expansive biomarker panel is possible with current mass spectrometry technology. Both targeted LC/MRM-MS and discovery DIA-MS analysis of whole blood collected on remote sampling devices are viable options for personal proteome biosignature stratification in precision medicine and precision health.

DOI: https://doi.org/10.1021/acs.analchem.3c01189
STAR Protoc. 2023 Jun 24. pii: S2666-1667(23)00348-9. [Epub ahead of print]4(3): 102381

Protocol for deep proteomic profiling of formalin-fixed paraffin-embedded specimens using a spectral library-free approach.

Rajesh Kumar Soni.

  Formalin-fixed paraffin-embedded (FFPE) samples are valuable archived bio-specimens of individuals and are commonly used in biomedical research. Here, we present a protocol for deep proteomic profiling of FFPE specimens using a spectral library-free approach. We describe steps for FFPE tissue collection, tissue lysis, homogenization, protein lysate cleanup, on-beads digestion, and de-salting. We then detail data acquisition and statistical analysis. This protocol is highly sensitive, reproducible, and applicable for high-throughput proteomic profiling and can be used on various types of specimens.

Keywords:  Clinical Protocol; High-throughput Screening; Mass Spectrometry; Protein Biochemistry; Proteomics

DOI:  https://doi.org/10.1016/j.xpro.2023.102381
Front Oncol. 2023 ;13 1117810

Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer.

Grace Yang, Shaoyang Huang, Kevin Hu, Alex Lu, Jonathan Yang, Noah Meroueh, Pengtao Dang, Yijie Wang, Haiqi Zhu, Sha Cao, Chi Zhang.

   Introduction: Glucose and glutamine are major carbon and energy sources that promote the rapid proliferation of cancer cells. Metabolic shifts observed on cell lines or mouse models may not reflect the general metabolic shifts in real human cancer tissue.
Method: In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including the glycolytic pathway, production of lactate, tricarboxylic acid (TCA) cycle, nucleic acid synthesis, glutaminolysis, glutamate, glutamine, and glutathione metabolism, and amino acid synthesis, in 11 cancer subtypes and nine matched adjacent normal tissue types using TCGA transcriptomics data.
Result: Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of the TCA cycle, i.e., the Warburg effect, in almost all the analyzed cancer. However, increased lactate production and the second half of the TCA cycle were only seen in certain cancer types. More interestingly, we failed to detect significantly altered glutaminolysis in cancer tissues compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes; (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls; and (3) the different shifts in tissue-specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression.
Discussion: This study strongly suggests the possibility of having a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.

Keywords:  TCA cycle; cancer metabolism; flux estimation; glutaminolysis; systems biology

DOI:  https://doi.org/10.3389/fonc.2023.1117810
Curr Issues Mol Biol. 2023 Jun 08. 45(6): 5036-5051

Potential Diagnostic Biomarker Detection for Prostate Cancer Using Untargeted and Targeted Metabolomic Profiling.

Diana Nitusca, Carmen Socaciu, Andreea Iulia Socaciu, Ioan Ovidiu Sirbu, Razvan Bardan, Alin Adrian Cumpanas, Edward Seclaman, Catalin Marian.

  Prostate cancer (PCa) remains one of the leading causes of cancer mortality in men worldwide, currently lacking specific, early detection and staging biomarkers. In this regard, modern research focuses efforts on the discovery of novel molecules that could represent potential future non-invasive biomarkers for the diagnosis of PCa, as well as therapeutic targets. Mounting evidence shows that cancer cells express an altered metabolism in their early stages, making metabolomics a promising tool for the discovery of altered pathways and potential biomarker molecules. In this study, we first performed untargeted metabolomic profiling on 48 PCa plasma samples and 23 healthy controls using ultra-high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-[ESI+]-MS) for the discovery of metabolites with altered profiles. Secondly, we selected five molecules (L-proline, L-tryptophan, acetylcarnitine, lysophosphatidylcholine C18:2 and spermine) for the downstream targeted metabolomics and found out that all the molecules, regardless of the PCa stage, were decreased in the PCa plasma samples when compared to the controls, making them potential biomarkers for PCa detection. Moreover, spermine, acetylcarnitine and L-tryptophan had very high diagnostic accuracy, with AUC values of 0.992, 0.923 and 0.981, respectively. Consistent with other literature findings, these altered metabolites could represent future specific and non-invasive candidate biomarkers for PCa detection, which opens novel horizons in the field of metabolomics.

Keywords:  biomarkers; diagnosis; metabolomics; prostate cancer

DOI:  https://doi.org/10.3390/cimb45060320
Trends Pharmacol Sci. 2023 Jun 26. pii: S0165-6147(23)00132-3. [Epub ahead of print]

Ferroptosis of immune cells in the tumor microenvironment.

Rina Kim, Devon Taylor, Robert H Vonderheide, Dmitry I Gabrilovich.

  Ferroptosis is a distinct form of cell death driven by the accumulation of peroxidized lipids. Characterized by alterations in redox lipid metabolism, ferroptosis has been implicated in a variety of cellular processes, including cancer. Induction of ferroptosis is considered a novel way to kill tumor cells, especially cells resistant to radiation and chemotherapy. However, in recent years, a new paradigm has emerged. In addition to promoting tumor cell death, ferroptosis causes potent immune suppression in the tumor microenvironment (TME) by affecting both innate and adaptive immune responses. In this review, we discuss the dual role of ferroptosis in the antitumor and protumorigenic functions of immune cells in cancer. We suggest strategies for targeting ferroptosis, taking onto account its ambiguous role in cancer.

Keywords:  MDSC; cancer; ferroptosis; macrophages; monocytes; neutrophils

DOI:  https://doi.org/10.1016/j.tips.2023.06.005
Oncogene. 2023 Jun 24.

Critical role of antioxidant programs in enzalutamide-resistant prostate cancer.

Eliot B Blatt, Karla Parra, Antje Neeb, Lorenzo Buroni, Denisa Bogdan, Wei Yuan, Yunpeng Gao, Collin Gilbreath, Alec Paschalis, Suzanne Carreira, Ralph J DeBerardinis, Ram S Mani, Johann S de Bono, Ganesh V Raj.

Therapy resistance to second-generation androgen receptor (AR) antagonists, such as enzalutamide, is common in patients with advanced prostate cancer (PCa). To understand the metabolic alterations involved in enzalutamide resistance, we performed metabolomic, transcriptomic, and cistromic analyses of enzalutamide-sensitive and -resistant PCa cells, xenografts, patient-derived organoids, patient-derived explants, and tumors. We noted dramatically higher basal and inducible levels of reactive oxygen species (ROS) in enzalutamide-resistant PCa and castration-resistant PCa (CRPC), in comparison to enzalutamide-sensitive PCa cells or primary therapy-naive tumors respectively. Unbiased metabolomic evaluation identified that glutamine metabolism was consistently upregulated in enzalutamide-resistant PCa cells and CRPC tumors. Stable isotope tracing studies suggest that this enhanced glutamine metabolism drives an antioxidant program that allows these cells to tolerate higher basal levels of ROS. Inhibition of glutamine metabolism with either a small-molecule glutaminase inhibitor or genetic knockout of glutaminase enhanced ROS levels, and blocked the growth of enzalutamide-resistant PCa. The critical role of compensatory antioxidant pathways in maintaining enzalutamide-resistant PCa cells was validated by targeting another antioxidant program driver, ferredoxin 1. Taken together, our data identify a metabolic need to maintain antioxidant programs and a potentially targetable metabolic vulnerability in enzalutamide-resistant PCa.

DOI: https://doi.org/10.1038/s41388-023-02756-w
J Am Soc Mass Spectrom. 2023 Jun 27.

Utilization of LC-MS/MS and Drift Tube Ion Mobility for Characterizing Intact Oxidized Arachidonate-Containing Glycerophosphatidylethanolamine.

Yulemni Morel, Jace W Jones.

  Lipid peroxidation is a key component in the pathogenesis of numerous disease states, where the oxidative damage of lipids frequently leads to membrane dysfunction and subsequent cellular death. Glycerophosphoethanolamine (PE) is the second most abundant phospholipid found in cellular membranes and, when oxidized, has been identified as an executor of ferroptotic cell death. PE commonly exists in the plasmalogen form, where the presence of the vinyl ether bond and its enrichment in polyunsaturated fatty acids make it especially susceptible to oxidative degradation. This results in a multitude of oxidized products complicating identification and often requiring several analytical techniques for interpretation. In the present study, we outline an analytical approach for the structural characterization of intact oxidized products of arachidonate-containing diacyl and plasmalogen PE. Intact oxidized PE structures, including structural and positional isomers, were identified using complementary liquid chromatography techniques, drift tube ion mobility, and high-resolution tandem mass spectrometry. This work establishes a comprehensive method for the analysis of intact lipid peroxidation products and provides an important pathway to investigate how lipid peroxidation initially impacts glycerophospholipids and their role in redox biology.

Keywords:  lipid peroxidation; mass spectrometry; oxidized lipid structures; plasmalogen

DOI:  https://doi.org/10.1021/jasms.3c00083
Brief Bioinform. 2023 Jun 27. pii: bbad244. [Epub ahead of print]

An integrated deep learning framework for the interpretation of untargeted metabolomics data.

Leqi Tian, Tianwei Yu.

  Untargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathways to reveal biological mechanisms. One of the key roadblocks in data analysis is not well-addressed, which is the problem of matching uncertainty between data features and known metabolites. Given the limitations of the experimental technology, the identities of data features cannot be directly revealed in the data. The predominant approach for mapping features to metabolites is to match the mass-to-charge ratio (m/z) of data features to those derived from theoretical values of known metabolites. The relationship between features and metabolites is not one-to-one since some metabolites share molecular composition, and various adduct ions can be derived from the same metabolite. This matching uncertainty causes unreliable metabolite selection and functional analysis results. Here we introduce an integrated deep learning framework for metabolomics data that take matching uncertainty into consideration. The model is devised with a gradual sparsification neural network based on the known metabolic network and the annotation relationship between features and metabolites. This architecture characterizes metabolomics data and reflects the modular structure of biological system. Three goals can be achieved simultaneously without requiring much complex inference and additional assumptions: (1) evaluate metabolite importance, (2) infer feature-metabolite matching likelihood and (3) select disease sub-networks. When applied to a COVID metabolomics dataset and an aging mouse brain dataset, our method found metabolic sub-networks that were easily interpretable.

Keywords:  deep learning; feature selection; uncertainty matching; untargeted metabolomics

DOI:  https://doi.org/10.1093/bib/bbad244
J Lipid Res. 2023 Jun 23. pii: S0022-2275(23)00080-9. [Epub ahead of print] 100407

The Lipogenic Enzyme Acetoacetyl-Coenzyme A Synthetase and Ketone Body Utilization for Denovo Lipid Synthesis, a Review.

James D Bergstrom.

  Acetoacetyl-CoA synthetase (AACS) is the key enzyme in the anabolic utilization of ketone bodies (KBs) for denovo lipid synthesis, a process that bypasses citrate and ATP citrate lyase. This review shows that AACS is a highly regulated, cytosolic and lipogenic enzyme and that many tissues can readily use KBs for denovo lipid synthesis. AACS has a low, micromolar Km for acetoacetate (AcAc) and supply of AcAc should not limit its activity in the fed state. In many tissues AACS appears to be regulated in conjunction with the need for cholesterol, but in adipose tissue it seems tied to fatty acid synthesis. KBs are readily utilized as substrates for lipid synthesis in lipogenic tissues including liver, adipose tissue, lactating mammary gland, skin, intestinal mucosa, adrenals and developing brain. In numerous studied cases, KBs served several-fold better than glucose as substrates for lipid synthesis and when present, KBs suppressed the utilization of glucose for lipid synthesis. Here it is hypothesized that a physiological role for the utilization of KBs for lipid synthesis is a metabolic process of lipid interconversion. Fatty acids are converted to KBs in liver and then the KBs are utilized to synthesize cholesterol and other long-chainfatty acids in liver and non-hepatic tissues. The conversion of fatty acids to cholesterol via the KBs may be a particularly important example of lipid interconversion. Utilizing KBs for lipid synthesis is glucose sparing and probably is important with low carbohydrate diets. Metabolic situations and tissues where lipid interconversion may be important are discussed.

Keywords:  ATP citrate lyase; Acetoacetate; Denovo lipid synthesis; anabolic role for ketone bodies; cholesterol synthesis; glucose sparing; lipid interconversion

DOI:  https://doi.org/10.1016/j.jlr.2023.100407
Metabolomics. 2023 06 25. 19(7): 63

Metabolomics and lipidomics studies of parasitic helminths: molecular diversity and identification levels achieved by using different characterisation tools.

Phurpa Wangchuk, Karma Yeshi, Alex Loukas.

   INTRODUCTION: Helminths are parasitic worms that infect millions of people worldwide and secrete a variety of excretory-secretory products (ESPs), including proteins, peptides, and small molecules. Despite this, there is currently no comprehensive review article on cataloging small molecules from helminths, particularly focusing on the different classes of metabolites (polar and lipid molecules) identified from the ESP and somatic tissue extracts of helminths that were studied in isolation from their hosts.
OBJECTIVE: This review aims to provide a comprehensive assessment of the metabolomics and lipidomics studies of parasitic helminths using all available analytical platforms.
METHOD: To achieve this objective, we conducted a meta-analysis of the identification and characterization tools, metabolomics approaches, metabolomics standard initiative (MSI) levels, software, and databases commonly applied in helminth metabolomics studies published until November 2021.
RESULT: This review analyzed 29 studies reporting the metabolomic assessment of ESPs and somatic tissue extracts of 17 helminth species grown under ex vivo/in vitro culture conditions. Of these 29 studies, 19 achieved the highest level of metabolite identification (MSI level-1), while the remaining studies reported MSI level-2 identification. Only 155 small molecule metabolites, including polar and lipids, were identified using MSI level-1 characterization protocols from various helminth species. Despite the significant advances made possible by the 'omics' technology, standardized software and helminth-specific metabolomics databases remain significant challenges in this field. Overall, this review highlights the potential for future studies to better understand the diverse range of small molecules that helminths produce and leverage their unique metabolomic features to develop novel treatment options.

Keywords:  Helminths; Lipidomics; Metabolomics; Parasites

DOI:  https://doi.org/10.1007/s11306-023-02019-5
Prostaglandins Other Lipid Mediat. 2023 Jun 28. pii: S1098-8823(23)00060-6. [Epub ahead of print] 106763

Set up and validation of a sensitive method to quantify prostaglandins, prostaglandin-glycerol esters and prostaglandin-ethanolamides, as well as their respective precursors.

Adrien Paquot, Juan Bestard-Escalas, Giulio G Muccioli.

  Arachidonic acid-derived prostaglandins are widely studied for their role in inflammation. However, besides arachidonic acid, other arachidonic moiety-containing lipids can be metabolized by COX-2. Indeed, the endocannabinoids 2-arachidonoylglycerol (2-AG) and N-arachidonoylethanolamine (anandamide, AEA) can follow the same biochemical pathways than arachidonic acid leading to the formation of prostaglandin-glycerol esters (PG-G) and prostaglandin-ethanolamides (or prostamides, PG-EA), respectively. The data reported so far support the interest of these bioactive lipids in inflammatory conditions. However, there is only a handful of methods described for their quantification in biological matrices. Moreover, given the shared biochemical pathways for arachidonic acid, 2-AG and AEA, a method allowing for the quantification of these precursors and the corresponding prostaglandin derivatives appears as largely needed. Thus, we report here the development and validation of a single run UPLC-MS/MS quantification method allowing the quantification of these endocannabinoids-derived mediators together with the classical prostaglandin. Moreover, we applied the method to the quantification of these lipids in vitro (using lipopolysaccharides-activated J774 macrophage cells) and in vivo in several tissues from DSS-induced colitis mice. This femtomole-range method should improve the understanding of the interaction between these lipid mediators and inflammation.

Keywords:  LC-MS; PGD(2)-G; PGE(2)-G; eicosanoid; fatty acid

DOI:  https://doi.org/10.1016/j.prostaglandins.2023.106763
Arthritis Rheumatol. 2023 Jun 30.

Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent versus Infrequent Gout Flares.

Ming Wang, Rui Li, Han Qi, Lei Pang, Lingling Cui, Zhen Liu, Jie Lu, Rong Wang, Shuhui Hu, Ningning Liang, Yongzhen Tao, Nicola Dalbeth, Tony R Merriman, Robert Terkeltaub, Huiyong Yin, Changgui Li.

OBJECTIVES: To discover differential metabolites and pathways underlying infrequent gout flares (InGF) and frequent gout flares (FrGF) using metabolomics and establish a predictive model by machine learning (ML) algorithms.
METHODS: Serum samples from a discovery cohort with 163 InGF and 239 FrGF patients were analyzed by mass spectrometry-based untargeted metabolomics to profile differential metabolites and explore dysregulated metabolic pathways using pathway enrichment analysis and network propagation-based algorithms. ML algorithms were performed to establish a predictive model based on selected metabolites, which was further optimized by a quantitative targeted metabolomics method and validated in an independent validation cohort with 97 participants with InGF and 139 participants with FrGF.
RESULTS: 439 differential metabolites between InGF and FrGF groups were identified. Top dysregulated pathways included carbohydrates, amino acids, bile acids, and nucleotide metabolism. Subnetworks with maximum disturbances in the global metabolic networks featured cross-talk between purine metabolism and caffeine metabolism, as well as interactions among pathways involving primary bile acid biosynthesis, taurine and hypotaurine metabolism, alanine, aspartate and glutamate metabolism, suggesting epigenetic modifications and gut microbiome in metabolic alterations underlying InGF and FrGF. Potential metabolite biomarkers were identified using ML-based multivariable selection and further validated by targeted metabolomics. Area under receiver operating characteristics curve for differentiating InGF and FrGF achieved 0.88 and 0.67 for the discovery and validation cohorts, respectively.
CONCLUSIONS: Systematic metabolic alterations underlie InGF and FrGF, and distinct profiles are associated with differences in gout flare frequencies. Predictive modeling based on selected metabolites from metabolomics can differentiate InGF and FrGF.

DOI: https://doi.org/10.1002/art.42635
Metabolites. 2023 May 31. pii: 709. [Epub ahead of print]13(6):

Fatty Acid Metabolites and the Tumor Microenvironment as Potent Regulators of Cancer Stem Cell Signaling.

Toshiyuki Murai, Satoru Matsuda.

  Individual cancer cells are not equal but are organized into a cellular hierarchy in which only a rare few leukemia cells can self-renew in a manner reminiscent of the characteristic stem cell properties. The PI3K/AKT pathway functions in a variety of cancers and plays a critical role in the survival and proliferation of healthy cells under physiologic conditions. In addition, cancer stem cells might exhibit a variety of metabolic reprogramming phenotypes that cannot be completely attributed to the intrinsic heterogeneity of cancer. Given the heterogeneity of cancer stem cells, new strategies with single-cell resolution will become a powerful tool to eradicate the aggressive cell population harboring cancer stem cell phenotypes. Here, this article will provide an overview of the most important signaling pathways of cancer stem cells regarding their relevance to the tumor microenvironment and fatty acid metabolism, suggesting valuable strategies among cancer immunotherapies to inhibit the recurrence of tumors.

Keywords:  PI3K/AKT; cancer stem cells; epithelial to mesenchymal transition; lipid metabolites; metabolic reprogramming; phosphoinositide 3-kinase; tumor microenvironment

DOI:  https://doi.org/10.3390/metabo13060709
Molecules. 2023 Jun 06. pii: 4580. [Epub ahead of print]28(12):

Assisted Reductive Amination for Quantitation of Tryptophan, 5-Hydroxytryptophan, and Serotonin by Ultraperformance Liquid Chromatography Coupled with Tandem Mass Spectrometry.

Shih-Shin Liang, Po-Tsun Shen, Yu-Qing Liang, Yi-Wen Ke, Chieh-Wen Cheng, Yi-Reng Lin.

  Herein, we used isotopic formaldehyde and sodium cyanoborohydride via reductive amination to label two methyl groups on primary amine to arrange the standards (h2-formaldehyde-modified) and internal standards (ISs, d2-formaldehyde-modified) of tryptophan and its metabolites, such as serotonin (5-hydroxytryptamine) and 5-hydroxytryptophan. These derivatized reactions with a high yield are very satisfactory for manufacturing standards and ISs. This strategy will generate one or two methyl groups on amine to create different mass unit shifts with 14 vs. 16 or 28 vs. 32 in individual compounds for biomolecules with amine groups. In other words, multiples of two mass units shift are created using this derivatized method with isotopic formaldehyde. Serotonin, 5-hydroxytryptophan, and tryptophan were used as examples to demonstrate isotopic formaldehyde-generating standards and ISs. h2-formaldehyde-modified serotonin, 5-hydroxytryptophan, and tryptophan are standards to construct calibration curves, and d2-formaldehyde-modified analogs such as ISs spike into samples to normalize the signal of each detection. We utilized multiple reaction monitoring modes and triple quadrupole mass spectrometry to demonstrate the derivatized method suitable for these three nervous biomolecules. The derivatized method demonstrated a linearity range of the coefficient of determinations between 0.9938 to 0.9969. The limits of detection and quantification ranged from 1.39 to 15.36 ng/mL.

Keywords:  5-hydroxytryptophan; multiple reaction monitoring; reductive amination; serotonin; tryptophan

DOI:  https://doi.org/10.3390/molecules28124580
Talanta. 2023 Jun 20. pii: S0039-9140(23)00546-5. [Epub ahead of print]265 124795

Spatial lipidomics and metabolomics of multicellular tumor spheroids using MALDI-2 and trapped ion mobility imaging.

Jing Chen, Peisi Xie, Qingyuan Dai, Pengfei Wu, Yu He, Zian Lin, Zongwei Cai.

  Lipids and metabolites are small biological molecules that act major roles in cellular functions. Multicellular tumor spheroids (MCTS) are a highly beneficial three-dimensional cellular model for cancer research due to their ability to imitate numerous characteristics of tumor tissues. Increasing studies have performed spatial lipidomics and metabolomics in MCTS using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). However, these approaches often lack the sensitivity and specificity to offer a comprehensive characterization of lipids and metabolites within MCTS. In this study, we addressed this challenge by utilizing MALDI combined with laser-induced postionization (MALDI-2) and trapped ion mobility spectrometry (TIMS) imaging in H295R adrenocortical MCTS. Our results showed that MALDI-2 could detect more lipids and metabolites in MCTS than the traditional MALDI. TIMS data revealed a successful separation of many isomeric and isobaric ions of lipids and metabolites with different locations (e.g., proliferative region and necrotic region) within MCTS, suggesting an enhanced peak capacity for spatial lipidomics and metabolomics. To further identify these isomeric and isobaric ions, we performed MS/MS imaging experiments to compare the differences in signal intensities and spatial distributions of product ions. Our data highlight the strong potential of MALDI-2 and TIMS imaging for analyzing lipids and metabolites in MCTS, which may serve as valuable tools for numerous fields of biological and medical research.

Keywords:  MALDI combined with laser-induced postionization; Mass spectrometry imaging; Matrix-assisted laser desorption/ionization (MALDI); Multicellular tumor spheroids; Trapped ion mobility spectrometry

DOI:  https://doi.org/10.1016/j.talanta.2023.124795
Cells. 2023 06 11. pii: 1605. [Epub ahead of print]12(12):

The Role of CD36 in Cancer Progression and Its Value as a Therapeutic Target.

William W Feng, Hannah T Zuppe, Manabu Kurokawa.

  Cluster of differentiation 36 (CD36) is a cell surface scavenger receptor that plays critical roles in many different types of cancer, notably breast, brain, and ovarian cancers. While it is arguably most well-known for its fatty acid uptake functions, it is also involved in regulating cellular adhesion, immune response, and apoptosis depending on the cellular and environmental contexts. Here, we discuss the multifaceted role of CD36 in cancer biology, such as its role in mediating metastasis, drug resistance, and immune evasion to showcase its potential as a therapeutic target. We will also review existing approaches to targeting CD36 in pre-clinical studies, as well as discuss the only CD36-targeting drug to advance to late-stage clinical trials, VT1021. Given the roles of CD36 in the etiology of metabolic disorders, such as atherosclerosis, diabetes, and non-alcoholic fatty liver disease, the clinical implications of CD36-targeted therapy are wide-reaching, even beyond cancer.

Keywords:  cancer stem cells; drug resistance; immune evasion; lipid metabolism; metastasis; targeted therapy; tumor microenvironment

DOI:  https://doi.org/10.3390/cells12121605
Development. 2023 Jul 01. pii: dev201492. [Epub ahead of print]150(13):

Single-cell proteomics: quantifying post-transcriptional regulation during development with mass-spectrometry.

Nikolai Slavov.

  Many developmental processes are regulated post-transcriptionally. Such post-transcriptional regulatory mechanisms can now be analyzed by robust single-cell mass spectrometry methods that allow accurate quantification of proteins and their modification in single cells. These methods can enable quantitative exploration of protein synthesis and degradation mechanisms that contribute to developmental cell fate specification. Furthermore, they may support functional analysis of protein conformations and activities in single cells, and thus link protein functions to developmental processes. This Spotlight provides an accessible introduction to single-cell mass spectrometry methods and suggests initial biological questions that are ripe for investigation.

Keywords:  Post-transcriptional regulation; Protein degradation; Protein synthesis; Proteomics

DOI:  https://doi.org/10.1242/dev.201492
Metabolites. 2023 Jun 19. pii: 769. [Epub ahead of print]13(6):

Novel Metabolomic Approach for Identifying Pathology-Specific Biomarkers in Rare Diseases: A Case Study in Oculopharyngeal Muscular Dystrophy (OPMD).

Pradeep Harish, Alberto Malerba, Rosemarie H M J M Kroon, Milad Shademan, Baziel van Engelan, Vered Raz, Linda Popplewell, Stuart G Snowden.

  The identification of metabolomic biomarkers relies on the analysis of large cohorts of patients compared to healthy controls followed by the validation of markers in an independent sample set. Indeed, circulating biomarkers should be causally linked to pathology to ensure that changes in the marker precede changes in the disease. However, this approach becomes unfeasible in rare diseases due to the paucity of samples, necessitating the development of new methods for biomarker identification. The present study describes a novel approach that combines samples from both mouse models and human patients to identify biomarkers of OPMD. We initially identified a pathology-specific metabolic fingerprint in murine dystrophic muscle. This metabolic fingerprint was then translated into (paired) murine serum samples and then to human plasma samples. This study identified a panel of nine candidate biomarkers that could predict muscle pathology with a sensitivity of 74.3% and specificity of 100% in a random forest model. These findings demonstrate that the proposed approach can identify biomarkers with good predictive performance and a higher degree of confidence in their relevance to pathology than markers identified in a small cohort of human samples alone. Therefore, this approach has a high potential utility for identifying circulating biomarkers in rare diseases.

Keywords:  LC-MS; biomarker; metabolomics; oculopharyngeal muscular dystrophy; random forest

DOI:  https://doi.org/10.3390/metabo13060769
Int J Mol Sci. 2023 Jun 08. pii: 9921. [Epub ahead of print]24(12):

Unveiling the MUFA-Cancer Connection: Insights from Endogenous and Exogenous Perspectives.

Zhiqiang Guo, Karl-Frédérik Bergeron, Marine Lingrand, Catherine Mounier.

  Monounsaturated fatty acids (MUFAs) have been the subject of extensive research in the field of cancer due to their potential role in its prevention and treatment. MUFAs can be consumed through the diet or endogenously biosynthesized. Stearoyl-CoA desaturases (SCDs) are key enzymes involved in the endogenous synthesis of MUFAs, and their expression and activity have been found to be increased in various types of cancer. In addition, diets rich in MUFAs have been associated with cancer risk in epidemiological studies for certain types of carcinomas. This review provides an overview of the state-of-the-art literature on the associations between MUFA metabolism and cancer development and progression from human, animal, and cellular studies. We discuss the impact of MUFAs on cancer development, including their effects on cancer cell growth, migration, survival, and cell signaling pathways, to provide new insights on the role of MUFAs in cancer biology.

Keywords:  cancer; monounsaturated fatty acids (MUFAs); oleic acid (OA); stearoyl-CoA desaturases (SCD)

DOI:  https://doi.org/10.3390/ijms24129921
Research (Wash D C). 2023 ;6 0179

Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics.

Qingzu He, Chuan-Qi Zhong, Xiang Li, Huan Guo, Yiming Li, Mingxuan Gao, Rongshan Yu, Xianming Liu, Fangfei Zhang, Donghui Guo, Fangfu Ye, Tiannan Guo, Jianwei Shuai, Jiahuai Han.

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIAXMBD, for direct analysis of DIA data. Dear-DIAXMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIAXMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIAXMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.

DOI: https://doi.org/10.34133/research.0179
Front Mol Biosci. 2023 ;10 1161036

Impact of renal tubular Cpt1a overexpression on the kidney metabolome in the folic acid-induced fibrosis mouse model.

Paula Cuevas-Delgado, Verónica Miguel, Francisco J Rupérez, Santiago Lamas, Coral Barbas.

  Background: Chronic kidney disease (CKD) is characterized by the progressive and irreversible deterioration of kidney function and structure with the appearance of renal fibrosis. A significant decrease in mitochondrial metabolism, specifically a reduction in fatty acid oxidation (FAO) in tubular cells, is observed in tubulointerstitial fibrosis, whereas FAO enhancement provides protection. Untargeted metabolomics offers the potential to provide a comprehensive analysis of the renal metabolome in the context of kidney injury. Methodology: Renal tissue from a carnitine palmitoyl transferase 1a (Cpt1a) overexpressing mouse model, which displays enhanced FAO in the renal tubule, subjected to folic acid nephropathy (FAN) was studied through a multiplatform untargeted metabolomics approach based on LC-MS, CE-MS and GC-MS analysis to achieve the highest coverage of the metabolome and lipidome affected by fibrosis. The expression of genes related to the biochemical routes showing significant changes was also evaluated. Results: By combining different tools for signal processing, statistical analysis and feature annotation, we were able to identify variations in 194 metabolites and lipids involved in many metabolic routes: TCA cycle, polyamines, one-carbon metabolism, amino acid metabolism, purine metabolism, FAO, glycerolipids and glycerophospholipids synthesis and degradation, glycosphingolipids interconversion, and sterol metabolism. We found several metabolites strongly altered by FAN, with no reversion induced by Cpt1a overexpression (v.g. citric acid), whereas other metabolites were influenced by CPT1A-induced FAO (v.g. glycine-betaine). Conclusion: It was implemented a successful multiplatform metabolomics approach for renal tissue analysis. Profound metabolic changes accompany CKD-associated fibrosis, some associated with tubular FAO failure. These results highlight the importance of addressing the crosstalk between metabolism and fibrosis when undertaking studies attempting to elucidate the mechanism of CKD progression.

Keywords:  biochemical pathways; kidney; mass spectrometry; metabolic phenotyping; metabolomics fingerprinting; mitochondria; murine model

DOI:  https://doi.org/10.3389/fmolb.2023.1161036
Metabolites. 2023 May 26. pii: 694. [Epub ahead of print]13(6):

Exercise Metabolome: Insights for Health and Performance.

Aayami Jaguri, Asmaa A Al Thani, Mohamed A Elrayess.

  Exercise has many benefits for physical and mental well-being. Metabolomics research has allowed scientists to study the impact of exercise on the body by analyzing metabolites released by tissues such as skeletal muscle, bone, and the liver. Endurance training increases mitochondrial content and oxidative enzymes, while resistance training increases muscle fiber and glycolytic enzymes. Acute endurance exercise affects amino acid metabolism, fat metabolism, cellular energy metabolism, and cofactor and vitamin metabolism. Subacute endurance exercise alters amino acid metabolism, lipid metabolism, and nucleotide metabolism. Chronic endurance exercise improves lipid metabolism and changes amino acid metabolism. Acute resistance exercise changes several metabolic pathways, including anaerobic processes and muscular strength. Chronic resistance exercise affects metabolic pathways, resulting in skeletal muscle adaptations. Combined endurance-resistance exercise alters lipid metabolism, carbohydrate metabolism, and amino acid metabolism, increasing anaerobic metabolic capacity and fatigue resistance. Studying exercise-induced metabolites is a growing field, and further research can uncover the underlying metabolic mechanisms and help tailor exercise programs for optimal health and performance.

Keywords:  combined endurance–resistance exercise; endurance exercise; exercise; metabolites; metabolomics; resistance exercise

DOI:  https://doi.org/10.3390/metabo13060694
Bioinformatics. 2023 Jun 27. pii: btad404. [Epub ahead of print]

Accelerating Open Modification Spectral Library Searching on Tensor Core in High-dimensional Space.

Jaeyoung Kang, Weihong Xu, Wout Bittremieux, Niema Moshiri, Tajana Rosing.

MOTIVATION: Driven by technological advances, the throughput and cost of mass spectrometry proteomics experiments have improved by orders of magnitude in recent decades. Spectral library searching is a common approach to annotating experimental mass spectra by matching them against large libraries of reference spectra corresponding to known peptides. An important disadvantage, however, is that only peptides included in the spectral library can be found, whereas novel peptides, such as those with unexpected post-translational modifications, will remain unknown. Open modification searching is an increasingly popular approach to annotate modified peptides based on partial matches against their unmodified counterparts. Unfortunately, this leads to very large search spaces and excessive runtimes, which is especially problematic considering the continuously increasing sizes of mass spectrometry proteomics datasets.
RESULTS: We propose an open modification searching algorithm, called HOMS-TC, that fully exploits parallelism in the entire pipeline of spectral library searching. We designed a new highly parallel encoding method based on the principle of hyperdimensional computing to encode mass spectral data to hypervectors while minimizing information loss. This process can be easily parallelized since each dimension is calculated independently. HOMS-TC processes two stages of existing cascade search in parallel and selects the most similar spectra while considering post-translational modifications. We accelerate HOMS-TC on NVIDIA's Tensor Core Units, which is emerging and readily available in the recent graphics processing unit (GPU). Our evaluation shows that HOMS-TC is 31 × faster on average than alternative search engines and provides comparable accuracy to competing search tools.
AVAILABILITY: HOMS-TC is freely available under the Apache 2.0 license as an open-source software project at https://github.com/tycheyoung/homs-tc.

DOI: https://doi.org/10.1093/bioinformatics/btad404
Biomolecules. 2023 05 31. pii: 917. [Epub ahead of print]13(6):

Lipidomics Profiling and Risk of Coronary Artery Disease in the BioHEART-CT Discovery Cohort.

Dantong Zhu, Stephen T Vernon, Zac D'Agostino, Jingqin Wu, Corey Giles, Adam S Chan, Katharine A Kott, Michael P Gray, Alireza Gholipour, Owen Tang, Habtamu B Beyene, Ellis Patrick, Stuart M Grieve, Peter J Meikle, Gemma A Figtree, Jean Y H Yang.

  The current coronary artery disease (CAD) risk scores for predicting future cardiovascular events rely on well-recognized traditional cardiovascular risk factors derived from a population level but often fail individuals, with up to 25% of first-time heart attack patients having no risk factors. Non-invasive imaging technology can directly measure coronary artery plaque burden. With an advanced lipidomic measurement methodology, for the first time, we aim to identify lipidomic biomarkers to enable intervention before cardiovascular events. With 994 participants from BioHEART-CT Discovery Cohort, we collected clinical data and performed high-performance liquid chromatography with mass spectrometry to determine concentrations of 683 plasma lipid species. Statin-naive participants were selected based on subclinical CAD (sCAD) categories as the analytical cohort (n = 580), with sCAD+ (n = 243) compared to sCAD- (n = 337). Through a machine learning approach, we built a lipid risk score (LRS) and compared the performance of the existing Framingham Risk Score (FRS) in predicting sCAD+. We obtained individual classifiability scores and determined Body Mass Index (BMI) as the modifying variable. FRS and LRS models achieved similar areas under the receiver operating characteristic curve (AUC) in predicting the validation cohort. LRS enhanced the prediction of sCAD+ in the healthy-weight group (BMI < 25 kg/m2), where FRS performed poorly and identified individuals at risk that FRS missed. Lipid features have strong potential as biomarkers to predict CAD plaque burden and can identify residual risk not captured by traditional risk factors/scores. LRS compliments FRS in prediction and has the most significant benefit in healthy-weight individuals.

Keywords:  CAD; imaging technology; lipidomics; traditional risk factor

DOI:  https://doi.org/10.3390/biom13060917
Cell Death Discov. 2023 Jun 29. 9(1): 203

Aurora kinase A/AURKA functionally interacts with the mitochondrial ATP synthase to regulate energy metabolism and cell death.

Rakesh Kumar Sharma, Abderrahman Chafik, Giulia Bertolin.

Cancer cells often hijack metabolic pathways to obtain the energy required to sustain their proliferation. Understanding the molecular mechanisms underlying cancer cell metabolism is key to fine-tune the metabolic preference of specific tumors, and potentially offer new therapeutic strategies. Here, we show that the pharmacological inhibition of mitochondrial Complex V delays the cell cycle by arresting breast cancer cell models in the G0/G1 phase. Under these conditions, the abundance of the multifunctional protein Aurora kinase A/AURKA is specifically lowered. We then demonstrate that AURKA functionally interacts with the mitochondrial Complex V core subunits ATP5F1A and ATP5F1B. Altering the AURKA/ATP5F1A/ATP5F1B nexus is sufficient to trigger G0/G1 arrest, and this is accompanied by decreased glycolysis and mitochondrial respiration rates. Last, we discover that the roles of the AURKA/ATP5F1A/ATP5F1B nexus depend on the specific metabolic propensity of triple-negative breast cancer cell lines, where they correlate with cell fate. On one hand, the nexus induces G0/G1 arrest in cells relying on oxidative phosphorylation as the main source of energy. On the other hand, it allows to bypass cell cycle arrest and it triggers cell death in cells with a glycolytic metabolism. Altogether, we provide evidence that AURKA and mitochondrial Complex V subunits cooperate to maintain cell metabolism in breast cancer cells. Our work paves the way to novel anti-cancer therapies targeting the AURKA/ATP5F1A/ATP5F1B nexus to lower cancer cell metabolism and proliferation.

DOI: https://doi.org/10.1038/s41420-023-01501-2
Front Oncol. 2023 ;13 1141851

Shutting off the fuel supply to target metabolic vulnerabilities in multiple myeloma.

Priyanka S Rana, Krishna Goparaju, James J Driscoll.

  Pathways that govern cellular bioenergetics are deregulated in tumor cells and represent a hallmark of cancer. Tumor cells have the capacity to reprogram pathways that control nutrient acquisition, anabolism and catabolism to enhance their growth and survival. Tumorigenesis requires the autonomous reprogramming of key metabolic pathways that obtain, generate and produce metabolites from a nutrient-deprived tumor microenvironment to meet the increased bioenergetic demands of cancer cells. Intra- and extracellular factors also have a profound effect on gene expression to drive metabolic pathway reprogramming in not only cancer cells but also surrounding cell types that contribute to anti-tumor immunity. Despite a vast amount of genetic and histologic heterogeneity within and between cancer types, a finite set of pathways are commonly deregulated to support anabolism, catabolism and redox balance. Multiple myeloma (MM) is the second most common hematologic malignancy in adults and remains incurable in the vast majority of patients. Genetic events and the hypoxic bone marrow milieu deregulate glycolysis, glutaminolysis and fatty acid synthesis in MM cells to promote their proliferation, survival, metastasis, drug resistance and evasion of immunosurveillance. Here, we discuss mechanisms that disrupt metabolic pathways in MM cells to support the development of therapeutic resistance and thwart the effects of anti-myeloma immunity. A better understanding of the events that reprogram metabolism in myeloma and immune cells may reveal unforeseen vulnerabilities and advance the rational design of drug cocktails that improve patient survival.

Keywords:  fatty acid synthesis; glycolysis; metabolism; multiple myeloma; oxidative phosphorylation; proteasome inhibitor

DOI:  https://doi.org/10.3389/fonc.2023.1141851
Mol Cell Proteomics. 2023 Jun 28. pii: S1535-9476(23)00123-8. [Epub ahead of print] 100612

Unified workflow for the rapid and in-depth characterization of bacterial proteomes.

Miriam Abele, Etienne Doll, Florian P Bayer, Chen Meng, Nina Lomp, Klaus Neuhaus, Siegfried Scherer, Bernhard Kuster, Christina Ludwig.

Bacteria are the most abundant and diverse organisms among the kingdoms of life. Due to this excessive variance, finding a unified, comprehensive, and safe workflow for quantitative bacterial proteomics is challenging. In this study, we have systematically evaluated and optimized sample preparation, mass spectrometric data acquisition, and data analysis strategies in bacterial proteomics. We investigated workflow performances on six representative species with highly different physiologic properties to mimic bacterial diversity. The best sample preparation strategy was a cell lysis protocol in 100% trifluoroacetic acid followed by an in-solution digest. Peptides were separated on a 30-minute linear microflow liquid chromatography gradient and analyzed in data-independent acquisition mode. Data analysis was performed with DIA-NN using a predicted spectral library. Performance was evaluated according to the number of identified proteins, quantitative precision, throughput, costs, and biological safety. With this rapid workflow, over 40% of all encoded genes were detected per bacterial species. We demonstrated the general applicability of our workflow on a set of 23 taxonomically and physiologically diverse bacterial species. We could confidently identify over 45,000 proteins in the combined dataset, of which 30,000 have not been experimentally validated. Our work thereby provides a valuable resource for the microbial scientific community. Finally, we grew Escherichia coli and Bacillus cereus in replicates under twelve different cultivation conditions to demonstrate the high-throughput suitability of the workflow. The proteomic workflow we present in this manuscript does not require any specialized equipment or commercial software and can be easily applied by other laboratories to support and accelerate the proteomic exploration of the bacterial kingdom.

DOI: https://doi.org/10.1016/j.mcpro.2023.100612
Molecules. 2023 Jun 14. pii: 4768. [Epub ahead of print]28(12):

Omics-Based Investigations of Breast Cancer.

Anca-Narcisa Neagu, Danielle Whitham, Pathea Bruno, Hailey Morrissiey, Celeste A Darie, Costel C Darie.

  Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

Keywords:  breast cancer (BC); genomics; metabolomics; new omics; onco-breastomics; proteomics; transcriptomics

DOI:  https://doi.org/10.3390/molecules28124768
Cell Metab. 2023 Jun 20. pii: S1550-4131(23)00213-9. [Epub ahead of print]

Beggars banquet: Metabolism in the tumor immune microenvironment and cancer therapy.

Weiping Zou, Douglas R Green.

  Metabolic programming in the tumor microenvironment (TME) alters tumor immunity and immunotherapeutic response in tumor-bearing mice and patients with cancer. Here, we review immune-related functions of core metabolic pathways, key metabolites, and crucial nutrient transporters in the TME, discuss their metabolic, signaling, and epigenetic impact on tumor immunity and immunotherapy, and explore how these insights can be applied to the development of more effective modalities to potentiate the function of T cells and sensitize tumor cell receptivity to immune attack, thereby overcoming therapeutic resistance.

Keywords:  T cell; checkpoint; immunotherapy; metabolism; metabolite; tumor microenvironment

DOI:  https://doi.org/10.1016/j.cmet.2023.06.003
Int J Mol Sci. 2023 Jun 13. pii: 10060. [Epub ahead of print]24(12):

Affi-BAMS™: A Robust Targeted Proteomics Microarray Platform to Measure Histone Post-Translational Modifications.

Ghaith M Hamza, Eric Miele, Don M Wojchowski, Paul Toran, Camilla R Worsfold, Tamil S Anthonymuthu, Vladislav B Bergo, Andrew X Zhang, Jeffrey C Silva.

  For targeted protein panels, the ability to specifically assay post-translational modifications (PTMs) in a quantitative, sensitive, and straightforward manner would substantially advance biological and pharmacological studies. The present study highlights the effectiveness of the Affi-BAMS™ epitope-directed affinity bead capture/MALDI MS platform for quantitatively defining complex PTM marks of H3 and H4 histones. Using H3 and H4 histone peptides and isotopically labelled derivatives, this affinity bead and MALDI MS platform achieves a range of >3 orders of magnitude with a technical precision CV of <5%. Using nuclear cellular lysates, Affi-BAMS PTM-peptide capture resolves heterogeneous histone N-terminal PTMs with as little as 100 µg of starting material. In an HDAC inhibitor and MCF7 cell line model, the ability to monitor dynamic histone H3 acetylation and methylation events is further demonstrated (including SILAC quantification). Affi-BAMS (and its capacity for the multiplexing of samples and target PTM-proteins) thus provides a uniquely efficient and effective approach for analyzing dynamic epigenetic histone marks, which is critical for the regulation of chromatin structure and gene expression.

Keywords:  BAMS; MALDI MS; PTMs; histones; immunoaffinity peptide capture; proteomics

DOI:  https://doi.org/10.3390/ijms241210060
Angew Chem Int Ed Engl. 2023 Jun 28. e202303415

Data-Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics.

Thy Truong, Kei Webber, Madisyn Johnston, Hannah Boekweg, Caleb Lindgren, Yiran Liang, Alissia Nydegger, Xiaofeng Xie, Tsz-Ming Tsang, Dasun Jayatunge, Joshua Andersen, Samuel Payne, Ryan T Kelly.

  We combined efficient sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to quantify >3,000 proteins from single cells in rapid label-free analyses. WWA employs large isolation windows to intentionally co-isolate and co-fragment adjacent precursors along with the selected precursor. Optimized WWA increases proteome coverage by ~40% relative to standard approaches. For a 40-min LC gradient operated at ~15 nL/min, we identified an average of 3,524 proteins per single-cell-sized aliquot of protein digest. Reducing the active gradient to 20 min resulted in a modest 10% decrease in proteome coverage. Using this platform, we compared protein expression between single HeLa cells having an essential autophagy gene, atg9a, knocked out, with their isogenic WT parental line. Similar proteome coverage was observed, and 268 proteins were significantly up- or downregulated. Protein upregulation primarily related to innate immunity, vesicle trafficking and protein degradation.

Keywords:  LFQ; autophagy; nanoLC; nanoPOTS; single-cell proteomics

DOI:  https://doi.org/10.1002/anie.202303415