bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2023–08–27
27 papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh
  1. Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel.
  2. Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography-Mass Spectrometry.
  3. Optimization Strategies for Mass Spectrometry-Based Untargeted Metabolomics Analysis of Small Polar Molecules in Human Plasma.
  4. Multiplexed targeted analysis of polyunsaturated fatty acids and oxylipins using liquid chromatography-tandem mass spectrometry.
  5. Novel Peak Shift Correction Method Based on the Retention Index for Peak Alignment in Untargeted Metabolomics.
  6. Polarity-extended liquid chromatography-triple quadrupole mass spectrometry for simultaneous hydrophilic and hydrophobic metabolite analysis.
  7. An integrated view of lipid metabolism in ferroptosis revisited via lipidomic analysis.
  8. Genetics of enzymatic dysfunctions in metabolic disorders and cancer.
  9. Differences in the Stool Metabolome between Vegans and Omnivores: Analyzing the NIST Stool Reference Material.
  10. Metabolism Study of Anamorelin, a GHSR1a Receptor Agonist Potentially Misused in Sport, with Human Hepatocytes and LC-HRMS/MS.
  11. De Novo Cleaning of Chimeric MS/MS Spectra for LC-MS/MS-Based Metabolomics.
  12. Metabolic reprogramming contributes to radioprotection by protein kinase Cδ.
  13. The Impact of Pigment-Epithelium-Derived Factor on MCF-7 Cell Metabolism in the Context of Glycaemic Condition.
  14. The Intersection of Metabolomics and Data Science.
  15. A Solid-Phase Microextraction-Liquid Chromatography-Mass Spectrometry Method for Analyzing Serum Lipids in Psoriatic Disease.
  16. Whole Blood and Plasma-Based Lipid Profiling Reveals Distinctive Metabolic Changes in Systemic Lupus Erythematosus and Systemic Sclerosis.
  17. SMetaS: A Sample Metadata Standardizer for Metabolomics.
  18. DEP2: an upgraded comprehensive analysis toolkit for quantitative proteomics data.
  19. Comprehensive isotopomer analysis of glutamate and aspartate in small tissue samples.
  20. Evaluation of Metabolomics as Diagnostic Targets in Oral Squamous Cell Carcinoma: A Systematic Review.
  21. Right in two: capabilities of ion mobility spectrometry for untargeted metabolomics.
  22. Fatty acids in cancer chemoresistance.
  23. Metabolomics Approaches for the Diagnosis, Treatment, and Better Disease Management of Viral Infections.
  24. Isoniazid derivatization strategy of carboxyl-containing metabolites for LC-MS/MS-based targeted metabolomics.
  25. Collision cross section measurement and prediction methods in omics.
  26. Plasma Lipids Profile in the Prediction of Non-Alcoholic Steatohepatitis in Adults: A Case-Control Study.
  27. Tips And Tricks for Proteome Sample Preparation.
  1. Mol Syst Biol. 2023 Aug 21. e11503
    Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel.
    Marvin Thielert, Ericka Cm Itang, Constantin Ammar, Florian A Rosenberger, Isabell Bludau, Lisa Schweizer, Thierry M Nordmann, Patricia Skowronek, Maria Wahle, Wen-Feng Zeng, Xie-Xuan Zhou, Andreas-David Brunner, Sabrina Richter, Mitchell P Levesque, Fabian J Theis, Martin Steger, Matthias Mann.
      Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.
    Keywords:  DIA; dimethyl labeling; multiplexing; single cells; spatial proteomics
    DOI:  https://doi.org/10.15252/msb.202211503
  2. Metabolites. 2023 Aug 13. pii: 944. [Epub ahead of print]13(8):
    Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography-Mass Spectrometry.
    Ying Zhang, Sili Fan, Gert Wohlgemuth, Oliver Fiehn.
      Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography-mass spectrometry (GC-MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC-MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC-MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations.
    Keywords:  GC–MS; data normalization; derivatization; primary metabolism; statistics
    DOI:  https://doi.org/10.3390/metabo13080944
  3. Metabolites. 2023 Aug 07. pii: 923. [Epub ahead of print]13(8):
    Optimization Strategies for Mass Spectrometry-Based Untargeted Metabolomics Analysis of Small Polar Molecules in Human Plasma.
    Michał Kaczmarek, Nanyun Zhang, Ludmila Buzhansky, Sharon Gilead, Ehud Gazit.
      The untargeted approach to mass spectrometry-based metabolomics has a wide potential to investigate health and disease states, identify new biomarkers for diseases, and elucidate metabolic pathways. All this holds great promise for many applications in biological and chemical research. However, the complexity of instrumental parameters on advanced hybrid mass spectrometers can make the optimization of the analytical method immensely challenging. Here, we report a strategy to optimize the selected settings of a hydrophilic interaction liquid chromatography-tandem mass spectrometry method for untargeted metabolomics studies of human plasma, as a sample matrix. Specifically, we evaluated the effects of the reconstitution solvent in the sample preparation procedure, the injection volume employed, and different mass spectrometry-related operating parameters including mass range, the number of data-dependent fragmentation scans, collision energy mode, duration of dynamic exclusion time, and mass resolution settings on the metabolomics data quality and output. This study highlights key instrumental variables influencing the detection of metabolites along with suggested settings for the IQ-X tribrid system and proposes a new methodological framework to ensure increased metabolome coverage.
    Keywords:  LC-MS; liquid chromatography; mass spectrometry; method optimization; plasma; untargeted metabolomics
    DOI:  https://doi.org/10.3390/metabo13080923
  4. STAR Protoc. 2023 Aug 17. pii: S2666-1667(23)00184-3. [Epub ahead of print]4(3): 102226
    Multiplexed targeted analysis of polyunsaturated fatty acids and oxylipins using liquid chromatography-tandem mass spectrometry.
    Evgenia Turtoi, Jeremy Jeudy, Gilles Valette, Christine Enjalbal, Isabelle K Vila, Nadine Laguette, Andrei Turtoi.
      Polyunsaturated fatty acids (PUFAs) and their oxidized products (oxylipins) are important mediators in intra- and extra-cellular signaling. We describe here the simultaneous quantification of 163 PUFAs and oxylipins using liquid chromatography-mass spectrometry (LC-MS). The protocol details steps for PUFA purification from various biological materials, the conditions for LC-MS analysis, as well as quantitative approaches for data evaluation. We provide an example of PUFA quantification in animal tissue along with the bioinformatic protocol, enabling efficient inter-sample comparison and statistical analysis. For complete details on the use and execution of this protocol, please refer to Vila et al.,1 Costanza et al.,2 Blomme et al.,3 and Blomme et al.4.
    Keywords:  Chemistry; Mass Spectrometry; Metabolomics
    DOI:  https://doi.org/10.1016/j.xpro.2023.102226
  5. Anal Chem. 2023 Aug 23.
    Novel Peak Shift Correction Method Based on the Retention Index for Peak Alignment in Untargeted Metabolomics.
    Jun-Di Hao, Yao-Yu Chen, Yan-Zhen Wang, Na An, Pei-Rong Bai, Quan-Fei Zhu, Yu-Qi Feng.
      Peak alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based large-scale untargeted metabolomics workflows, as it enables the integration of metabolite peaks across multiple samples, which is essential for accurate data interpretation. Slight differences or fluctuations in chromatographic separation conditions, however, can cause the chromatographic retention time (RT) shift between consecutive analyses, ultimately affecting the accuracy of peak alignment between samples. Here, we introduce a novel RT shift correction method based on the retention index (RI) and apply it to peak alignment. We synthesized a series of N-acyl glycine (C2-C23) homologues via the amidation reaction between glycine with normal saturated fatty acids (C2-C23) as calibrants able to respond proficiently in both mass spectrometric positive- and negative-ion modes. Using these calibrants, we established an N-acyl glycine RI system. This RI system is capable of covering a broad chromatographic space and addressing chromatographic RT shift caused by variations in flow rate, gradient elution, instrument systems, and LC separation columns. Moreover, based on the RI system, we developed a peak shift correction model to enhance peak alignment accuracy. Applying the model resulted in a significant improvement in the accuracy of peak alignment from 15.5 to 80.9% across long-term data spanning a period of 157 days. To facilitate practical application, we developed a Python-based program, which is freely available at https://github.com/WHU-Fenglab/RI-based-CPSC.
    DOI:  https://doi.org/10.1021/acs.analchem.3c02583
  6. Anal Chim Acta. 2023 Oct 09. pii: S0003-2670(23)00876-0. [Epub ahead of print]1277 341655
    Polarity-extended liquid chromatography-triple quadrupole mass spectrometry for simultaneous hydrophilic and hydrophobic metabolite analysis.
    Rui Guo, Qisheng Zhong, Jiaqi Liu, Peiming Bai, Zongpeng Wang, Jieling Kou, Peijie Chen, Jie Zhang, Bo Zhang.
      Although various metabolomic methods have been reported in recent years, simultaneous detection of hydrophilic and hydrophobic metabolites in a single analysis remains a technical challenge. In this study, based on the combination of hydrophilic interaction liquid chromatography (HILIC) and reversed phase liquid chromatography (RPLC), an online two-dimensional liquid chromatography/triple quadrupole mass spectrometry method (2D-LC/TQMS) was developed for the simultaneous analysis of hydrophilic and hydrophobic metabolites of various biological samples. The method can measure 417 biologically important metabolites (e.g., amino acids and peptides, pyrimidines, purines, monosaccharides, fatty acids and conjugates, organic dicarboxylic acids, and others) with logP values ranging from -10.3 to 21.9. The metabolites are involved in a variety of metabolic pathways (e.g., purine metabolism, pyrimidine metabolism, tyrosine metabolism, galactose metabolism, gluconeogenesis, and TCA cycle). The developed method has good intra- and inter-day reproducibility (RSD of retention time <2%, RSD of peak area <30%), good linearity (R2 > 0.9) and wide linear range (from 0.0025 μg/mL to 5 μg/mL). The applicability of the method was tested using different biological samples (i.e., plasma, serum, urine, fecal, seminal plasma and liver) and it was found that 208 (out of 417) identical metabolites were detected in all biological samples. Furthermore, the metabolomic method was applied to a case/control study of urinary of bladder cancer. Thirty differential metabolites were identified that were involved in carbohydrate and amino acid metabolism.
    Keywords:  Biological sample; Bladder cancer; Targeted metabolomics; Triple quadrupole mass spectrometry; Two-dimensional liquid chromatography
    DOI:  https://doi.org/10.1016/j.aca.2023.341655
  7. Exp Mol Med. 2023 Aug 23.
    An integrated view of lipid metabolism in ferroptosis revisited via lipidomic analysis.
    Jong Woo Kim, Ji-Yoon Lee, Mihee Oh, Eun-Woo Lee.
      Ferroptosis is a form of regulated cell death characterized by iron-dependent lipid peroxidation. This process contributes to cellular and tissue damage in various human diseases, such as cardiovascular diseases, neurodegeneration, liver disease, and cancer. Although polyunsaturated fatty acids (PUFAs) in membrane phospholipids are preferentially oxidized, saturated/monounsaturated fatty acids (SFAs/MUFAs) also influence lipid peroxidation and ferroptosis. In this review, we first explain how cells differentially synthesize SFA/MUFAs and PUFAs and how they control fatty acid pools via fatty acid uptake and β-oxidation, impacting ferroptosis. Furthermore, we discuss how fatty acids are stored in different lipids, such as diacyl or ether phospholipids with different head groups; triglycerides; and cholesterols. Moreover, we explain how these fatty acids are released from these molecules. In summary, we provide an integrated view of the diverse and dynamic metabolic processes in the context of ferroptosis by revisiting lipidomic studies. Thus, this review contributes to the development of therapeutic strategies for ferroptosis-related diseases.
    DOI:  https://doi.org/10.1038/s12276-023-01077-y
  8. Front Oncol. 2023 ;13 1230934
    Genetics of enzymatic dysfunctions in metabolic disorders and cancer.
    Mélanie Mahé, Tiffany J Rios-Fuller, Andrea Karolin, Robert J Schneider.
      Inherited metabolic disorders arise from mutations in genes involved in the biogenesis, assembly, or activity of metabolic enzymes, leading to enzymatic deficiency and severe metabolic impairments. Metabolic enzymes are essential for the normal functioning of cells and are involved in the production of amino acids, fatty acids and nucleotides, which are essential for cell growth, division and survival. When the activity of metabolic enzymes is disrupted due to mutations or changes in expression levels, it can result in various metabolic disorders that have also been linked to cancer development. However, there remains much to learn regarding the relationship between the dysregulation of metabolic enzymes and metabolic adaptations in cancer cells. In this review, we explore how dysregulated metabolism due to the alteration or change of metabolic enzymes in cancer cells plays a crucial role in tumor development, progression, metastasis and drug resistance. In addition, these changes in metabolism provide cancer cells with a number of advantages, including increased proliferation, resistance to apoptosis and the ability to evade the immune system. The tumor microenvironment, genetic context, and different signaling pathways further influence this interplay between cancer and metabolism. This review aims to explore how the dysregulation of metabolic enzymes in specific pathways, including the urea cycle, glycogen storage, lysosome storage, fatty acid oxidation, and mitochondrial respiration, contributes to the development of metabolic disorders and cancer. Additionally, the review seeks to shed light on why these enzymes represent crucial potential therapeutic targets and biomarkers in various cancer types.
    Keywords:  cancer; enzymatic dysregulation; fatty acid oxidation; glycogen storage; inherited metabolic disorders; lysosome storage; mitochondrial respiration; urea cycle
    DOI:  https://doi.org/10.3389/fonc.2023.1230934
  9. Metabolites. 2023 Aug 07. pii: 921. [Epub ahead of print]13(8):
    Differences in the Stool Metabolome between Vegans and Omnivores: Analyzing the NIST Stool Reference Material.
    Raquel Cumeras, Tong Shen, Luis Valdiviez, Zakery Tippins, Bennett D Haffner, Oliver Fiehn.
      To gain confidence in results of omic-data acquisitions, methods must be benchmarked using validated quality control materials. We report data combining both untargeted and targeted metabolomics assays for the analysis of four new human fecal reference materials developed by the U.S. National Institute of Standards and Technologies (NIST) for metagenomics and metabolomics measurements. These reference grade test materials (RGTM) were established by NIST based on two different diets and two different samples treatments, as follows: firstly, homogenized fecal matter from subjects eating vegan diets, stored and submitted in either lyophilized (RGTM 10162) or aqueous form (RGTM 10171); secondly, homogenized fecal matter from subjects eating omnivore diets, stored and submitted in either lyophilized (RGTM 10172) or aqueous form (RGTM 10173). We used four untargeted metabolomics assays (lipidomics, primary metabolites, biogenic amines and polyphenols) and one targeted assay on bile acids. A total of 3563 compounds were annotated by mass spectrometry, including 353 compounds that were annotated in more than one assay. Almost half of all compounds were annotated using hydrophilic interaction chromatography/accurate mass spectrometry, followed by the lipidomics and the polyphenol assays. In total, 910 metabolites were found in at least 4-fold different levels in fecal matter from vegans versus omnivores, specifically for peptides, amino acids and lipids. In comparison, only 251 compounds showed 4-fold differences between lyophilized and aqueous fecal samples, including DG O-34:0 and methionine sulfoxide. A range of diet-specific metabolites were identified to be significantly different between vegans and omnivores, exemplified by citrinin and C17:0-acylcarnitine for omnivores, and curcumin and lenticin for vegans. Bioactive molecules like acyl alpha-hydroxy-fatty acids (AAHFA) were differentially regulated in vegan versus omnivore fecal materials, highlighting the importance of diet-specific reference materials for dietary biomarker studies.
    Keywords:  diet; mass spectrometry; metabolomics; omnivore; reference material; stool; vegan
    DOI:  https://doi.org/10.3390/metabo13080921
  10. Metabolites. 2023 Aug 15. pii: 949. [Epub ahead of print]13(8):
    Metabolism Study of Anamorelin, a GHSR1a Receptor Agonist Potentially Misused in Sport, with Human Hepatocytes and LC-HRMS/MS.
    Prince Sellase Gameli, Omayema Taoussi, Giuseppe Basile, Jeremy Carlier, Francesco Paolo Busardò.
      Anamorelin, developed for the treatment of cancer cachexia, is an orally active medication that improves appetite and food intake, thereby increasing body mass and physical functioning. It is classified as a growth hormone secretagogue and strictly monitored by the World Anti-Doping Agency (WADA), owing to its anabolic enhancing potential. Identifying anamorelin and/or metabolite biomarkers of consumption is critical in doping controls. However, there are currently no data available on anamorelin human metabolic fate. The aim of this study was to investigate and identify biomarkers characteristic of anamorelin intake using in silico metabolite predictions with GLORYx, in vitro incubation with 10-donor-pooled human hepatocytes, liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS) analysis, and data processing with Thermo Scientific's Compound Discoverer. In silico prediction resulted in N-acetylation at the methylalanyl group as the main transformation (score, 88%). Others including hydroxylation at the indole substructure, and oxidation and N-demethylation at the trimethylhydrazino group were predicted (score, ≤36%). Hepatocyte incubations resulted in 14 phase I metabolites formed through N-demethylation at the trimethylhydrazino group, N-dealkylation at the piperidine ring, and oxidation at the indole and methylalanyl groups; and two phase II glucuronide conjugates occurring at the indole. We propose four metabolites detected as specific biomarkers for toxicological screening.
    Keywords:  anamorelin; doping; ghrelin receptor agonist; high-resolution mass spectrometry; human hepatocyte; liquid chromatography; metabolism; metabolite prediction
    DOI:  https://doi.org/10.3390/metabo13080949
  11. Anal Chem. 2023 Aug 21.
    De Novo Cleaning of Chimeric MS/MS Spectra for LC-MS/MS-Based Metabolomics.
    Tingting Zhao, Shipei Xing, Huaxu Yu, Tao Huan.
      The purity of tandem mass spectrometry (MS/MS) is essential to MS/MS-based metabolite annotation and unknown exploration. This work presents a de novo approach to cleaning chimeric MS/MS spectra generated in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics. The assumption is that true fragments and their precursors are well correlated across the samples in a study, while false or contamination fragments are rather independent. Using data simulation, this work starts with an investigation of the negative effects of chimeric MS/MS spectra on spectral similarity analysis and molecular networking. Next, the characteristics of true and false fragments in chimeric MS/MS spectra were investigated using MS/MS of chemical standards. We recognized three fragment peak attributes indicative of whether a peak is a false fragment, including (1) intensity ratio fluctuation, (2) appearance rate, and (3) relative intensity. Using these attributes, we tested three machine learning models and identified XGBoost as the best model to achieve an area under the precision-recall curve of 0.98 for a clear separation between true and false fragments. Based on the trained model, we constructed an automated bioinformatic platform, DNMS2Purifier (short for de novo MS2Purifier), for metabolic features from metabolomics studies. DNMS2Purifier recognizes and processes chimeric MS/MS spectra without additional sample analysis or library confirmation. DNMS2Purifer was evaluated on a metabolomics data set generated with different MS/MS precursor isolation windows. It successfully captured the increase in the number of false fragments from the increased isolation window. DNMS2Purifier was also compared to MS2Purifier, an existing MS/MS spectral cleaning tool based on the addition of data-independent acquisition (DIA) analysis. Results indicated that DNMS2Purifier uniquely recognizes false fragments, which complements the previous DIA-based approach. Finally, DNMS2Purifier was demonstrated using a real experimental metabolomics study, showing improved MS/MS spectral quality and leading to an improved spectral match ratio and molecular networking outcome.
    DOI:  https://doi.org/10.1021/acs.analchem.3c00736
  12. J Biol Chem. 2023 Aug 21. pii: S0021-9258(23)02214-7. [Epub ahead of print] 105186
    Metabolic reprogramming contributes to radioprotection by protein kinase Cδ.
    Angela M Ohm, Trisiani Affandi, Julie A Reisz, M Cecilia Caino, Angelo D'Alessandro, Mary E Reyland.
      Loss of protein kinase Cδ (PKCδ) activity renders cells resistant to DNA damaging agents, including irradiation, however the mechanism(s) underlying resistance is poorly understood. Here we have asked if metabolic reprogramming by PKCδ contributes to radioprotection. Analysis of global metabolomics showed that depletion of PKCδ affects metabolic pathways that control energy production, and antioxidant, nucleotide, and amino acid biosynthesis. Increased NADPH and nucleotide production in PKCδ depleted cells is associated with upregulation of the pentose phosphate pathway (PPP) as evidenced by increased activation of G6PD and an increase in the nucleotide precursor, 5-phosphoribosyl-1-pyrophosphate. Stable isotope tracing with U-[13C6] glucose showed reduced utilization of glucose for glycolysis in PKCδ depleted cells, and no increase in U-[13C6] glucose incorporation into purines or pyrimidines. In contrast, isotope tracing with [13C5, 15N2] glutamine showed increased utilization of glutamine for synthesis of nucleotides, glutathione and TCA intermediates, and increased incorporation of labelled glutamine into pyruvate and lactate. Using a glycolytic rate assay, we confirmed that anaerobic glycolysis is increased in PKCδ depleted cells; this was accompanied by a reduction in oxidative phosphorylation, as assayed using a mitochondrial stress assay. Importantly, pretreatment of cells with specific inhibitors of the PPP or glutaminase prior to irradiation reversed radioprotection in PKCδ depleted cells, indicating that these cells have acquired co-dependency on the PPP and glutamine for survival. Our studies demonstrate that metabolic reprogramming to increase utilization of glutamine and nucleotide synthesis contributes to radioprotection in the context of PKCδ inhibition.
    Keywords:  apoptosis; metabolism; protein kinase Cδ; radioprotection; salivary gland
    DOI:  https://doi.org/10.1016/j.jbc.2023.105186
  13. Pharmaceutics. 2023 Aug 14. pii: 2140. [Epub ahead of print]15(8):
    The Impact of Pigment-Epithelium-Derived Factor on MCF-7 Cell Metabolism in the Context of Glycaemic Condition.
    Raziyeh Abooshahab, Kourosh Hooshmand, Hani-Al Salami, Crispin R Dass.
      Studies have demonstrated that pigment-epithelium-derived factor (PEDF) is a robust inhibitor of tumour growth and development, implying that this may serve as a promising target for therapeutic intervention. However, the precise impact of PEDF on cancerous cell metabolic pathways remains uncertain despite ongoing research. In this light, this study aimed to employ a metabolomics approach for understanding the metabolic reprogramming events in breast cancer across different glycaemic loads and their response to PEDF. Gas chromatography-quadrupole mass spectrometry (GC/Q-MS) analysis revealed metabolic alterations in ER+ human cell line MCF-7 cells treated with PEDF under varying glycaemic conditions. The identification of significantly altered metabolites was accomplished through MetaboAnalyst (v.5.0) and R packages, which enabled both multivariate and univariate analyses. Out of the 48 metabolites identified, 14 were chosen based on their significant alterations in MCF-7 cells under different glycaemic conditions and PEDF treatment (p < 0.05, VIP > 0.8). Dysregulation in pathways associated with amino acid metabolism, intermediates of the TCA cycle, nucleotide metabolism, and lipid metabolism were detected, and they exhibited different responses to PEDF. Our results suggest that PEDF has a diverse influence on the metabolism of MCF-7 cells in both normo- and hyperglycaemic environments, thereby warranting studies using patient samples to correlate our findings with clinical response in the future.
    Keywords:  PEDF; breast cancer; cancer metabolism; glycaemia; metabolomics
    DOI:  https://doi.org/10.3390/pharmaceutics15082140
  14. Metabolites. 2023 Aug 04. pii: 915. [Epub ahead of print]13(8):
    The Intersection of Metabolomics and Data Science.
    Seongho Kim.
      Metabolomics generates a vast amount of data and heavily relies on data science for biological interpretation [...].
    DOI:  https://doi.org/10.3390/metabo13080915
  15. Metabolites. 2023 Aug 20. pii: 963. [Epub ahead of print]13(8):
    A Solid-Phase Microextraction-Liquid Chromatography-Mass Spectrometry Method for Analyzing Serum Lipids in Psoriatic Disease.
    John Koussiouris, Nikita Looby, Vathany Kulasingam, Vinod Chandran.
      Approximately 25% of psoriasis patients have an inflammatory arthritis termed psoriatic arthritis (PsA). There is strong interest in identifying and validating biomarkers that can accurately and reliably predict conversion from psoriasis to PsA using novel technologies such as metabolomics. Lipids, in particular, are of key interest in psoriatic disease. We sought to develop a liquid chromatography-mass spectrometry (LC-MS) method to be used in conjunction with solid-phase microextraction (SPME) for analyzing fatty acids and similar molecules. A total of 25 chromatographic methods based on published lipid studies were tested on two LC columns. As a proof of concept, serum samples from psoriatic disease patients (n = 27 psoriasis and n = 26 PsA) were processed using SPME and run on the selected LC-MS method. The method that was best for analyzing fatty acids and fatty acid-like molecules was optimized and applied to serum samples. A total of 18 tentatively annotated features classified as fatty acids and other lipid compounds were statistically significant between psoriasis and PsA groups using both multivariate and univariate approaches. The SPME-LC-MS method developed and optimized was capable of detecting fatty acids and similar lipids that may aid in differentiating psoriasis and PsA patients.
    Keywords:  lipids; liquid chromatography; mass spectrometry; metabolomics; psoriatic disease; solid-phase microextraction
    DOI:  https://doi.org/10.3390/metabo13080963
  16. J Proteome Res. 2023 Aug 22.
    Whole Blood and Plasma-Based Lipid Profiling Reveals Distinctive Metabolic Changes in Systemic Lupus Erythematosus and Systemic Sclerosis.
    Helena Beatriz Ferreira, Tânia Melo, Inês M S Guerra, Ana S P Moreira, Paula Laranjeira, Artur Paiva, Laura Goracci, Stefano Bonciarelli, Pedro Domingues, M Rosário Domingues.
      Autoimmune diseases (AID), such as systemic lupus erythematosus (SLE) and systemic sclerosis (SS), are complex conditions involving immune system dysregulation. Diagnosis is challenging, requiring biomarkers for improved detection and prediction of relapses. Lipids have emerged as potential biomarkers due to their role in inflammation and immune response. This study uses an untargeted C18 RP-LC-MS lipidomics approach to comprehensively assess changes in lipid profiles in patients with SLE and SS. By analyzing whole blood and plasma, the study aims to simplify the lipidomic analysis, explore cellular-level lipids, and compare lipid signatures of SLE and SS with healthy controls. Our findings showed variations in the lipid profile of SLE and SS. Sphingomyelin and ceramide molecular species showed significant increases in plasma samples from SS patients, suggesting an atherosclerotic profile and potentially serving as lipid biomarkers. Phosphatidylserine species in whole blood from SLE patients exhibited elevated levels supporting previously reported dysregulated processes of cell death and defective clearance of dying cells in this AID. Moreover, decreased phospholipids bearing PUFA were observed, potentially attributed to the degradation of these species through lipid peroxidation processes. Further studies are needed to better understand the role of lipids in the pathological mechanisms underlying SLE and SS.
    Keywords:  autoimmune diseases; lipid metabolism; lipidomic signature; lipidomics; mass spectrometry; systemic lupus erythematosus; systemic sclerosis
    DOI:  https://doi.org/10.1021/acs.jproteome.3c00321
  17. Metabolites. 2023 Aug 12. pii: 941. [Epub ahead of print]13(8):
    SMetaS: A Sample Metadata Standardizer for Metabolomics.
    Parker Ladd Bremer, Oliver Fiehn.
      Metabolomics has advanced to an extent where it is desired to standardize and compare data across individual studies. While past work in standardization has focused on data acquisition, data processing, and data storage aspects, metabolomics databases are useless without ontology-based descriptions of biological samples and study designs. We introduce here a user-centric tool to automatically standardize sample metadata. Using such a tool in frontends for metabolomic databases will dramatically increase the FAIRness (Findability, Accessibility, Interoperability, and Reusability) of data, specifically for data reuse and for finding datasets that share comparable sets of metadata, e.g., study meta-analyses, cross-species analyses or large scale metabolomic atlases. SMetaS (Sample Metadata Standardizer) combines a classic database with an API and frontend and is provided in a containerized environment. The tool has two user-centric components. In the first component, the user designs a sample metadata matrix and fills the cells using natural language terminology. In the second component, the tool transforms the completed matrix by replacing freetext terms with terms from fixed vocabularies. This transformation process is designed to maximize simplicity and is guided by, among other strategies, synonym matching and typographical fixing in an n-grams/nearest neighbors model approach. The tool enables downstream analysis of submitted studies and samples via string equality for FAIR retrospective use.
    Keywords:  FAIR; meta-analysis; metadata; repository; standardization
    DOI:  https://doi.org/10.3390/metabo13080941
  18. Bioinformatics. 2023 Aug 25. pii: btad526. [Epub ahead of print]
    DEP2: an upgraded comprehensive analysis toolkit for quantitative proteomics data.
    Zhenhuan Feng, Peiyang Fang, Hui Zheng, Xiaofei Zhang.
       SUMMARY: Mass spectrometry (MS)-based proteomics has become the most powerful approach to study the proteome of given biological and clinical samples. Advancements in sample preparation and MS detection have extended the application of proteomics, but have also brought new demands on data analysis. Appropriate proteomics data analysis workflow mainly requires quality control, hypothesis testing, functional mining, and visualization. Although there are numerous tools for each process, an efficient and universal tandem analysis toolkit to obtain a quick overall view of various proteomics data is still urgently needed. Here, we present DEP2, an updated version of DEP we previously established, for proteomics data analysis. We amended the analysis workflow by incorporating alternative approaches to accommodate diverse proteomics data, introducing peptide-protein summarization and coupling biological function exploration. In summary, DEP2 is a well-rounded toolkit designed for protein- and peptide-level quantitative proteomics data. It features a more flexible differential analysis workflow and includes a user-friendly Shiny application to facilitate data analysis.
    AVAILABILITY AND IMPLEMENTATION: DEP2 is available at https://github.com/mildpiggy/DEP2, released under the MIT license. For further information and usage details, please refer to the package website at https://mildpiggy.github.io/DEP2/.
    DOI:  https://doi.org/10.1093/bioinformatics/btad526
  19. Cell Metab. 2023 Aug 15. pii: S1550-4131(23)00272-3. [Epub ahead of print]
    Comprehensive isotopomer analysis of glutamate and aspartate in small tissue samples.
    Feng Cai, Divya Bezwada, Ling Cai, Rohit Mahar, Zheng Wu, Mario C Chang, Panayotis Pachnis, Chendong Yang, Sherwin Kelekar, Wen Gu, Bailey Brooks, Bookyung Ko, Hieu S Vu, Thomas P Mathews, Lauren G Zacharias, Misty Martin-Sandoval, Duyen Do, K Celeste Oaxaca, Eunsook S Jin, Vitaly Margulis, Craig R Malloy, Matthew E Merritt, Ralph J DeBerardinis.
      Stable isotopes are powerful tools to assess metabolism. 13C labeling is detected using nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS). MS has excellent sensitivity but generally cannot discriminate among different 13C positions (isotopomers), whereas NMR is less sensitive but reports some isotopomers. Here, we develop an MS method that reports all 16 aspartate and 32 glutamate isotopomers while requiring less than 1% of the sample used for NMR. This method discriminates between pathways that result in the same number of 13C labels in aspartate and glutamate, providing enhanced specificity over conventional MS. We demonstrate regional metabolic heterogeneity within human tumors, document the impact of fumarate hydratase (FH) deficiency in human renal cancers, and investigate the contributions of tricarboxylic acid (TCA) cycle turnover and CO2 recycling to isotope labeling in vivo. This method can accompany NMR or standard MS to provide outstanding sensitivity in isotope-labeling experiments, particularly in vivo.
    Keywords:  aspartate; cancer; glutamate; isotopomer; mass spectrometry; nuclear magnetic resonance; pyruvate carboxylase; stable isotope; tricarboxylic acid cycle
    DOI:  https://doi.org/10.1016/j.cmet.2023.07.013
  20. Metabolites. 2023 Jul 27. pii: 890. [Epub ahead of print]13(8):
    Evaluation of Metabolomics as Diagnostic Targets in Oral Squamous Cell Carcinoma: A Systematic Review.
    Susanth Alapati, Giulio Fortuna, Gordon Ramage, Christopher Delaney.
      In recent years, high-throughput technologies have facilitated the widespread use of metabolomics to identify biomarkers and targets for oral squamous cell carcinoma (OSCC). As a result, the primary goal of this systematic review is to identify and evaluate metabolite biomarkers and their pathways for OSCC that featured consistently across studies despite methodological variations. Six electronic databases (Medline, Cochrane, Web of Science, CINAHL, ProQuest, and Embase) were reviewed for the longitudinal studies involving OSCC patients and metabolic marker analysis (in accordance with PRISMA 2020). The studies included ranged from the inception of metabolomics in OSCC (i.e., 1 January 2007) to 30 April 2023. The included studies were then assessed for their quality using the modified version of NIH quality assessment tool and QUADOMICS. Thirteen studies were included after screening 2285 studies. The majority of the studies were from South Asian regions, and metabolites were most frequently derived from saliva. Amino acids accounted for more than quarter of the detected metabolites, with glutamate and methionine being the most prominent. The top dysregulated metabolites indicated dysregulation of six significantly enriched pathways including aminoacyl-tRNA biosynthesis, glutathione metabolism and arginine biosynthesis with the false discovery rate (FDR) <0.05. Finally, this review highlights the potential of metabolomics for early diagnosis and therapeutic targeting of OSCC. However, larger studies and standardized protocols are needed to validate these findings and make them a clinical reality.
    Keywords:  OSCC; biomarkers; cancer diagnosis; metabolites; metabolomics; oral cancer
    DOI:  https://doi.org/10.3390/metabo13080890
  21. Front Mol Biosci. 2023 ;10 1230282
    Right in two: capabilities of ion mobility spectrometry for untargeted metabolomics.
    Tessa Moses, Karl Burgess.
      This mini review focuses on the opportunities provided by current and emerging separation techniques for mass spectrometry metabolomics. The purpose of separation technologies in metabolomics is primarily to reduce complexity of the heterogeneous systems studied, and to provide concentration enrichment by increasing sensitivity towards the quantification of low abundance metabolites. For this reason, a wide variety of separation systems, from column chemistries to solvent compositions and multidimensional separations, have been applied in the field. Multidimensional separations are a common method in both proteomics applications and gas chromatography mass spectrometry, allowing orthogonal separations to further reduce analytical complexity and expand peak capacity. These applications contribute to exponential increases in run times concomitant with first dimension fractionation followed by second dimension separations. Multidimensional liquid chromatography to increase peak capacity in metabolomics, when compared to the potential of running additional samples or replicates and increasing statistical confidence, mean that uptake of these methods has been minimal. In contrast, in the last 15 years there have been significant advances in the resolution and sensitivity of ion mobility spectrometry, to the point where high-resolution separation of analytes based on their collision cross section approaches chromatographic separation, with minimal loss in sensitivity. Additionally, ion mobility separations can be performed on a chromatographic timescale with little reduction in instrument duty cycle. In this review, we compare ion mobility separation to liquid chromatographic separation, highlight the history of the use of ion mobility separations in metabolomics, outline the current state-of-the-art in the field, and discuss the future outlook of the technology. "Where there is one, you're bound to divide it. Right in two", James Maynard Keenan.
    Keywords:  chromatography; drift time; drift tube; high-field asymmetric; separation; trapped; travelling wave
    DOI:  https://doi.org/10.3389/fmolb.2023.1230282
  22. Cancer Lett. 2023 Aug 18. pii: S0304-3835(23)00303-8. [Epub ahead of print]572 216352
    Fatty acids in cancer chemoresistance.
    Jiale Qin, Lvlan Ye, Xiangqiong Wen, Xiang Zhang, Yuqin Di, Zhihui Chen, Ziyang Wang.
      Despite the remarkable clinical success of immunotherapy and molecular targeted therapy in patients with advanced tumors, chemotherapy remains the most commonly used treatment for most tumor patients. Chemotherapy drugs effectively inhibit tumor cell proliferation and survival through their remarkable mechanisms. However, tumor cells often develop severe intrinsic and acquired chemoresistance under chemotherapy stress, limiting the effectiveness of chemotherapy and leading to treatment failure. Growing evidence suggests that alterations in lipid metabolism may be implicated in the development of chemoresistance in tumors. Therefore, in this review, we provide a comprehensive overview of fatty acid metabolism and its impact on chemoresistance mechanisms. Additionally, we discuss the potential of targeting fatty acid metabolism as a therapeutic strategy to overcome drug resistance.
    Keywords:  Chemoresistance; ER stress; Fatty acid metabolism; Ferroptosis; Unsaturated fatty acids
    DOI:  https://doi.org/10.1016/j.canlet.2023.216352
  23. Metabolites. 2023 Aug 15. pii: 948. [Epub ahead of print]13(8):
    Metabolomics Approaches for the Diagnosis, Treatment, and Better Disease Management of Viral Infections.
    Haya Al-Sulaiti, Jehad Almaliti, C Benjamin Naman, Asmaa A Al Thani, Hadi M Yassine.
      Metabolomics is an analytical approach that involves profiling and comparing the metabolites present in biological samples. This scoping review article offers an overview of current metabolomics approaches and their utilization in evaluating metabolic changes in biological fluids that occur in response to viral infections. Here, we provide an overview of metabolomics methods including high-throughput analytical chemistry and multivariate data analysis to identify the specific metabolites associated with viral infections. This review also focuses on data interpretation and applications designed to improve our understanding of the pathogenesis of these viral diseases.
    Keywords:  COVID-19; HBV; HCMV; HCV; HIV; LC-MS; NMR; influenza; metabolomics; viral infections
    DOI:  https://doi.org/10.3390/metabo13080948
  24. Anal Bioanal Chem. 2023 Aug 25.
    Isoniazid derivatization strategy of carboxyl-containing metabolites for LC-MS/MS-based targeted metabolomics.
    Aimin Wang, Yuting Fan, Jun Fu, Fengrui Song, Zhiqiang Liu, Shu Liu.
      Metabolomics is a biochemical analysis tool for identifying metabolic phenotypes and used to reveal the pathogenic mechanisms of disease and to inform drug-targeted therapies. Carboxyl-containing metabolites (CCMs) account for an important proportion of the metabolome, but because of the diversity of physical and chemical properties of CCMs in biological samples, traditional liquid chromatography-mass spectrometry (LC-MS) targeted metabolome analysis methods cannot achieve simultaneous quantification of multiple types of CCMs. Therefore, we proposed for the first time a targeted metabolomics strategy using isoniazid derivatization combined with LC-MS/MS to simultaneously quantify 39 CCMs of 5 different types (short-chain fatty acids, amino acids, bile acids, phenylalanine and tryptophan metabolic pathway acids) with large polarity differences associated with Alzheimer's disease (AD) and significantly improve the detection coverage and sensitivity. The yields of isoniazid derivative CCMs were high and could guarantee the accuracy of CCM quantification. The LODs of CCMs increased significantly (1.25-2000-fold) after derivatization. The method showed good selectivity, intra-day and inter-day accuracies and precisions, and repeatability. There was no significant effect on the determination of CCMs in terms of matrix effect and recovery. CCMs showed good stability. And CCMs showed good stability under short-term storage and freeze-thaw cycles. At the same time, the regulatory effects of Schisandrae chinensis Fructus and Ginseng Radix et Rhizoma (SG) herb pair on CCM metabolic disorders in feces, urine, serum, and the brain of AD rats were elucidated from the perspective of targeted metabolomics. In combination with pharmacodynamic evaluation and gut microbiota analysis, the mechanism of SG herb pair on AD rats was comprehensively understood. In summary, this innovative isoniazid derivatization combined with a targeted metabolomics method has great potential for trace biological lineage analysis.
    Keywords:  Alzheimer’s disease; Carboxyl-containing metabolites; Isoniazid derivatization; Targeted metabolomics
    DOI:  https://doi.org/10.1007/s00216-023-04910-5
  25. J Mass Spectrom. 2023 Aug 24. e4973
    Collision cross section measurement and prediction methods in omics.
    Kimberly Y Kartowikromo, Orobola E Olajide, Ahmed M Hamid.
      Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.
    Keywords:  CCS; computational methods; ion mobility; machine learning; omics
    DOI:  https://doi.org/10.1002/jms.4973
  26. Int J Mol Sci. 2023 Aug 12. pii: 12717. [Epub ahead of print]24(16):
    Plasma Lipids Profile in the Prediction of Non-Alcoholic Steatohepatitis in Adults: A Case-Control Study.
    Georgios Kalopitas, Thomai Mouskeftara, Theodoros Liapikos, Konstantinos Arvanitakis, Aristeidis Ioannidis, Konstantinos Malandris, Eleni Theocharidou, Michail Chourdakis, Emmanouil Sinakos, Helen Gika, Georgios Germanidis.
      Patients with non-alcoholic steatohepatitis (NASH) show significantly faster progress in the stages of fibrosis compared to those with non-alcoholic fatty liver (NAFL) disease. The non-invasive diagnosis of NASH remains an unmet clinical need. Preliminary data have shown that sphingolipids, especially ceramides, fatty acids, and other lipid classes may be related to the presence of NASH and the histological activity of the disease. The aim of our study was to assess the association of certain plasma lipid classes, such as fatty acids, acylcarnitines, and ceramides, with the histopathological findings in patients with NASH. The study included three groups: patients with NASH (N = 12), NAFL (N = 10), and healthy [non non-alcoholic fatty liver disease (NAFLD)] controls (N = 15). Plasma samples were collected after 12 h of fasting, and targeted analyses for fatty acids, acylcarnitines, and ceramides were performed. Baseline clinical and demographic characteristics were collected. There was no significant difference in baseline characteristics across the three groups or between NAFL and NASH patients. Patients with NASH had increased levels of several fatty acids, including, among others, fatty acid (FA) 14:0, FA 15:0, FA 18:0, FA 18:3n3, as well as Cer(d18:1/16:0), compared to NAFL patients and healthy controls. No significant difference was found between NAFL patients and healthy controls. In conclusion, patients with NASH exhibited a distinctive plasma lipid profile that can differentiate them from NAFL patients and non-NAFLD populations. More data from larger cohorts are needed to validate these findings and examine possible implications for diagnostic and management strategies of the disease.
    Keywords:  acylcarnitines; ceramides; diagnostic markers; fatty acids; non-alcoholic fatty liver disease; non-alcoholic steatohepatitis; non-invasive diagnosis; plasma lipids
    DOI:  https://doi.org/10.3390/ijms241612717
  27. J Vis Exp. 2023 Feb 03.
    Tips And Tricks for Proteome Sample Preparation.
    Alan A Doucette.
       ARTICLES DISCUSSED: Kovalchuk, S. I., Ziganshin, R., Shelukhina, I. Simple in-house ultra-high performance capillary column manufacturing with the FlashPack approach. Journal of Visualized Experiments. (178), e62522 (2021). Sirois, I., Isabelle, M., Duquette, J. D., Saab, F., Caron, E. Immunopeptidomics: Isolation of mouse and human MHC Class I- and II-associated peptides for mass spectrometry analysis. Journal of Visualized Experiments. (176), e63052 (2021). Han, Y., Thomas, C. T., Wennersten, S. A., Lau, E., Lam, M. P. Y. Shotgun proteomics sample processing automated by an open-source lab robot. Journal of Visualized Experiments. (176), e63092 (2021). Nickerson, J. L. et al. Organic solvent-based protein precipitation for robust proteome purification ahead of mass spectrometry. Journal of Visualized Experiments. (180), e63503 (2022). Li, D., Liang, J., Zhang, Y., Zhang, G. An integrated workflow of identification and quantification on FDR control-based untargeted metabolome. Journal of Visualized Experiments. doi: 10.3791/63625-v (2022). Petelski, A. A., Nikolai Slavov, N., Specht, N. Single-cell proteomics preparation for mass spectrometry analysis using freeze-heat lysis and an isobaric carrier. Journal of Visualized Experiments. doi: 10.3791/63802 (2022).
    DOI:  https://doi.org/10.3791/64830
This page is being maintained by Thomas Krichel. It was last updated on 2024‒11‒22 at 4:19.