bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2020‒12‒06
nineteen papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh


  1. Metabolomics. 2020 Nov 29. 16(12): 125
    Roci I, Watrous JD, Lagerborg KA, Jain M, Nilsson R.
      INTRODUCTION: Choline is an essential human nutrient that is particular important for proliferating cells, and altered choline metabolism has been associated with cancer transformation. Yet, the various metabolic fates of choline in proliferating cells have not been investigated systematically.OBJECTIVES: This study aims to map the metabolic products of choline in normal and cancerous proliferating cells.
    METHODS: We performed 13C-choline tracing followed by liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis of metabolic products in normal and in vitro-transformed (tumor-forming) epithelial cells, and also in tumor-derived cancer cell lines. Selected metabolites were quantified by internal standards.
    RESULTS: Untargeted analysis revealed 121 LCMS peaks that were 13C-labeled from choline, including various phospholipid species, but also previously unknown products such as monomethyl- and dimethyl-ethanolamines. Interestingly, we observed formation of betaine from choline specifically in tumor-derived cells. Expression of choline dehydrogenase (CHDH), which catalyzes the first step of betaine synthesis, correlated with betaine synthesis across the cell lines studied. RNAi silencing of CHDH did not affect cell proliferation, although we observed an increased fraction of G2M phase cells with some RNAi sequences, suggesting that CHDH and its product betaine may play a role in cell cycle progression. Betaine cell concentration was around 10 µM, arguing against an osmotic function, and was not used as a methyl donor. The function of betaine in these tumor-derived cells is presently unknown.
    CONCLUSION: This study identifies novel metabolites of choline in cancer and normal cell lines, and reveals altered choline metabolism in cancer cells.
    Keywords:  13C3 choline; Betaine; CHDH; Isotope tracing; Methylation
    DOI:  https://doi.org/10.1007/s11306-020-01749-0
  2. Metabolites. 2020 Dec 02. pii: E495. [Epub ahead of print]10(12):
    Medina J, van der Velpen V, Teav T, Guitton Y, Gallart-Ayala H, Ivanisevic J.
      Expanding metabolome coverage to include complex lipids and polar metabolites is essential in the generation of well-founded hypotheses in biological assays. Traditionally, lipid extraction is performed by liquid-liquid extraction using either methyl-tert-butyl ether (MTBE) or chloroform, and polar metabolite extraction using methanol. Here, we evaluated the performance of single-step sample preparation methods for simultaneous extraction of the complex lipidome and polar metabolome from human plasma. The method performance was evaluated using high-coverage Hydrophilic Interaction Liquid Chromatography-ESI coupled to tandem mass spectrometry (HILIC-ESI-MS/MS) methodology targeting a panel of 1159 lipids and 374 polar metabolites. The criteria used for method evaluation comprised protein precipitation efficiency, and relative MS signal abundance and repeatability of detectable lipid and polar metabolites in human plasma. Among the tested methods, the isopropanol (IPA) and 1-butanol:methanol (BUME) mixtures were selected as the best compromises for the simultaneous extraction of complex lipids and polar metabolites, allowing for the detection of 584 lipid species and 116 polar metabolites. The extraction with IPA showed the greatest reproducibility with the highest number of lipid species detected with the coefficient of variation (CV) < 30%. Besides this difference, both IPA and BUME allowed for the high-throughput extraction and reproducible measurement of a large panel of complex lipids and polar metabolites, thus warranting their application in large-scale human population studies.
    Keywords:  LC-MS/MS; extraction; human plasma; lipidomics; metabolomics; sample preparation
    DOI:  https://doi.org/10.3390/metabo10120495
  3. Med Sci Monit. 2020 Dec 01. 26 e926766
    Guo Y, Wan S, Han M, Zhao Y, Li C, Cai G, Zhang S, Sun Z, Hu X, Cao H, Li Z.
      BACKGROUND Abdominal aortic aneurysm (AAA) is a complicated aortic dilatation disease. Metabolomics is an emerging system biology method. This aim of this study was to identify abnormal metabolites and metabolic pathways associated with AAA and to discover potential biomarkers that could affect the size of AAAs. MATERIAL AND METHODS An untargeted metabolomic method was used to analyze the plasma metabolic profiles of 39 patients with AAAs and 30 controls. Multivariate analysis methods were used to perform differential metabolite screening and metabolic pathway analysis. Cluster analysis and univariate analysis were performed to identify potential metabolites that could affect the size of an AAA. RESULTS Forty-five different metabolites were identified with an orthogonal projection to latent squares-discriminant analysis model and the differences between them in the patients with AAAs and the control group were compared. A variable importance in the projection score >1 and P<0.05 were considered statistically significant. In patients with AAAs, the pathways involving metabolism of alanine, aspartate, glutamate, D-glutamine, D-glutamic acid, arginine, and proline; tricarboxylic acid cycling; and biosynthesis of arginine are abnormal. The progression of an AAA may be related to 13 metabolites: citric acid, 2-oxoglutarate, succinic acid, coenzyme Q1, pyruvic acid, sphingosine-1-phosphate, platelet-activating factor, LysoPC (16: 00), lysophosphatidylcholine (18: 2(9Z,12Z)/0: 0), arginine, D-aspartic acid, and L- and D-glutamine. CONCLUSIONS An untargeted metabolomic analysis using ultraperformance liquid chromatography-tandem mass spectrometry identified metabolites that indicate disordered metabolism of energy, lipids, and amino acids in AAAs.
    DOI:  https://doi.org/10.12659/MSM.926766
  4. Nat Methods. 2020 Dec;17(12): 1229-1236
    Meier F, Brunner AD, Frank M, Ha A, Bludau I, Voytik E, Kaspar-Schoenefeld S, Lubeck M, Raether O, Bache N, Aebersold R, Collins BC, Röst HL, Mann M.
      Data-independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall, only a few per cent of all incoming ions are isolated for mass analysis. Here, we make use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro) to devise a scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extraction workflow by inclusion of the ion mobility dimension for both signal extraction and scoring and thereby increase the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts.
    DOI:  https://doi.org/10.1038/s41592-020-00998-0
  5. Signal Transduct Target Ther. 2020 Dec 04. 5(1): 280
    Li Z, Wang F, Liang B, Su Y, Sun S, Xia S, Shao J, Zhang Z, Hong M, Zhang F, Zheng S.
      As one of the bicyclic metabolic pathways of one-carbon metabolism, methionine metabolism is the pivot linking the folate cycle to the transsulfuration pathway. In addition to being a precursor for glutathione synthesis, and the principal methyl donor for nucleic acid, phospholipid, histone, biogenic amine, and protein methylation, methionine metabolites can participate in polyamine synthesis. Methionine metabolism disorder can aggravate the damage in the pathological state of a disease. In the occurrence and development of chronic liver diseases (CLDs), changes in various components involved in methionine metabolism can affect the pathological state through various mechanisms. A methionine-deficient diet is commonly used for building CLD models. The conversion of key enzymes of methionine metabolism methionine adenosyltransferase (MAT) 1 A and MAT2A/MAT2B is closely related to fibrosis and hepatocellular carcinoma. In vivo and in vitro experiments have shown that by intervening related enzymes or downstream metabolites to interfere with methionine metabolism, the liver injuries could be reduced. Recently, methionine supplementation has gradually attracted the attention of many clinical researchers. Most researchers agree that adequate methionine supplementation can help reduce liver damage. Retrospective analysis of recently conducted relevant studies is of profound significance. This paper reviews the latest achievements related to methionine metabolism and CLD, from molecular mechanisms to clinical research, and provides some insights into the future direction of basic and clinical research.
    DOI:  https://doi.org/10.1038/s41392-020-00349-7
  6. Nat Metab. 2020 Nov 30.
    Kim J, Lee HM, Cai F, Ko B, Yang C, Lieu EL, Muhammad N, Rhyne S, Li K, Haloul M, Gu W, Faubert B, Kaushik AK, Cai L, Kasiri S, Marriam U, Nham K, Girard L, Wang H, Sun X, Kim J, Minna JD, Unsal-Kacmaz K, DeBerardinis RJ.
      In non-small-cell lung cancer (NSCLC), concurrent mutations in the oncogene KRAS and the tumour suppressor STK11 (also known as LKB1) encoding the kinase LKB1 result in aggressive tumours prone to metastasis but with liabilities arising from reprogrammed metabolism. We previously demonstrated perturbed nitrogen metabolism and addiction to an unconventional pathway of pyrimidine synthesis in KRAS/LKB1 co-mutant cancer cells. To gain broader insight into metabolic reprogramming in NSCLC, we analysed tumour metabolomes in a series of genetically engineered mouse models with oncogenic KRAS combined with mutations in LKB1 or p53. Metabolomics and gene expression profiling pointed towards activation of the hexosamine biosynthesis pathway (HBP), another nitrogen-related metabolic pathway, in both mouse and human KRAS/LKB1 co-mutant tumours. KRAS/LKB1 co-mutant cells contain high levels of HBP metabolites, higher flux through the HBP pathway and elevated dependence on the HBP enzyme glutamine-fructose-6-phosphate transaminase [isomerizing] 2 (GFPT2). GFPT2 inhibition selectively reduced KRAS/LKB1 co-mutant tumour cell growth in culture, xenografts and genetically modified mice. Our results define a new metabolic vulnerability in KRAS/LKB1 co-mutant tumours and provide a rationale for targeting GFPT2 in this aggressive NSCLC subtype.
    DOI:  https://doi.org/10.1038/s42255-020-00316-0
  7. Mol Cancer Res. 2020 Dec 01. pii: molcanres.0316.2020. [Epub ahead of print]
    Gevariya N, Lachance G, Robitaille K, Joly Beauparlant C, Beaudoin L, Fournier E, Fradet Y, Droit A, Julien P, Marette A, Bergeron A, Fradet V.
      The impact of omega (ω)-3 fatty acids on prostate cancer is controversial in epidemiological studies but experimental studies suggest a protective effect. However, little is known about the mechanism of action. Here, we studied the effects of purified fatty acid molecules on prostate tumor progression using the TRAMP-C2 syngeneic immunocompetent mouse model. Compared to ω-6 or ω-9 supplemented animals, we observed that late-stage prostate tumor growth was reduced with a monoacylglyceride (MAG)-conjugated form of eicosapentaenoic acid (EPA) supplementation, while docosahexanenoic acid (DHA) caused an early reduction. MAG-EPA significantly decreased tumor blood vessel diameter (p<0.001). RNA sequencing analysis revealed that MAG-EPA down-regulated angiogenesis- and vascular-related pathways in tumors. We also observed this tissue vascular phenotype in a clinical trial testing MAG-EPA versus a high oleic sunflower oil placebo. Using anti-CD31 immunohistochemistry, we observed that MAG-EPA reduced blood vessel diameter in prostate tumor tissue (p=0.03) but not in normal adjacent tissue. Finally, testing autocrine and paracrine effects in an avascular tumor spheroid growth assay, both exogenous MAG-EPA and endogenous ω3 reduced VEGF secretion and in vitro endothelial cell tube formation and blocked tumor spheroid growth, suggesting that ω3 molecules can directly hinder prostate cancer cell growth. Altogether, our results suggest that fatty acids regulate prostate cancer growth and that a tumor-specific microenvironment is required for the anti-vascular effect of MAG-EPA in prostate cancer patients. Implications: Increasing the amount of ingested EPA omega-3 subtype for prostate cancer patients might help to reduce prostate tumor progression by reducing tumor vascularisation.
    DOI:  https://doi.org/10.1158/1541-7786.MCR-20-0316
  8. Cell Metab. 2020 Dec 01. pii: S1550-4131(20)30598-2. [Epub ahead of print]32(6): 981-995.e7
    Corrado M, Edwards-Hicks J, Villa M, Flachsmann LJ, Sanin DE, Jacobs M, Baixauli F, Stanczak M, Anderson E, Azuma M, Quintana A, Curtis JD, Clapes T, Grzes KM, Kabat AM, Kyle R, Patterson AE, Geltink RK, Amulic B, Steward CG, Strathdee D, Trompouki E, O'Sullivan D, Pearce EJ, Pearce EL.
      Mitochondria constantly adapt to the metabolic needs of a cell. This mitochondrial plasticity is critical to T cells, which modulate metabolism depending on antigen-driven signals and environment. We show here that de novo synthesis of the mitochondrial membrane-specific lipid cardiolipin maintains CD8+ T cell function. T cells deficient for the cardiolipin-synthesizing enzyme PTPMT1 had reduced cardiolipin and responded poorly to antigen because basal cardiolipin levels were required for activation. However, neither de novo cardiolipin synthesis, nor its Tafazzin-dependent remodeling, was needed for T cell activation. In contrast, PTPMT1-dependent cardiolipin synthesis was vital when mitochondrial fitness was required, most notably during memory T cell differentiation or nutrient stress. We also found CD8+ T cell defects in a small cohort of patients with Barth syndrome, where TAFAZZIN is mutated, and in a Tafazzin-deficient mouse model. Thus, the dynamic regulation of a single mitochondrial lipid is crucial for CD8+ T cell immunity.
    Keywords:  Barth Syndrome; CD8 T cells; PTPMT1; Tafazzin; cardiolipin; immune memory; immunometabolism; mitochodria
    DOI:  https://doi.org/10.1016/j.cmet.2020.11.003
  9. Metabolites. 2020 Dec 01. pii: E494. [Epub ahead of print]10(12):
    Neef SK, Janssen N, Winter S, Wallisch SK, Hofmann U, Dahlke MH, Schwab M, Mürdter TE, Haag M.
      As metabolic rewiring is crucial for cancer cell proliferation, metabolic phenotyping of patient-derived organoids is desirable to identify drug-induced changes and trace metabolic vulnerabilities of tumor subtypes. We established a novel protocol for metabolomic and lipidomic profiling of colorectal cancer organoids by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) facing the challenge of capturing metabolic information from a minimal sample amount (<500 cells/injection) in the presence of an extracellular matrix (ECM). The best procedure of the tested protocols included ultrasonic metabolite extraction with acetonitrile/methanol/water (2:2:1, v/v/v) without ECM removal. To eliminate ECM-derived background signals, we implemented a data filtering procedure based on the p-value and fold change cut-offs, which retained features with signal intensities >120% compared to matrix-derived signals present in blank samples. As a proof-of-concept, the method was applied to examine the early metabolic response of colorectal cancer organoids to 5-fluorouracil treatment. Statistical analysis revealed dose-dependent changes in the metabolic profiles of treated organoids including elevated levels of 2'-deoxyuridine, 2'-O-methylcytidine, inosine and 1-methyladenosine and depletion of 2'-deoxyadenosine and specific phospholipids. In accordance with the mechanism of action of 5-fluorouracil, changed metabolites are mainly involved in purine and pyrimidine metabolism. The novel protocol provides a first basis for the assessment of metabolic drug response phenotypes in 3D organoid models.
    Keywords:  LC-MS; QTOF; colorectal cancer; lipidomics; metabolic profiling; metabolomics; organoids
    DOI:  https://doi.org/10.3390/metabo10120494
  10. Sci Rep. 2020 Dec 04. 10(1): 21244
    Acevedo-Acevedo S, Millar DC, Simmons AD, Favreau P, Cobra PF, Skala M, Palecek SP.
      Breast cancer metastasis occurs via blood and lymphatic vessels. Breast cancer cells 'educate' lymphatic endothelial cells (LECs) to support tumor vascularization and growth. However, despite known metabolic alterations in breast cancer, it remains unclear how lymphatic endothelial cell metabolism is altered in the tumor microenvironment and its effect in lymphangiogenic signaling in LECs. We analyzed metabolites inside LECs in co-culture with MCF-7, MDA-MB-231, and SK-BR-3 breast cancer cell lines using [Formula: see text] nuclear magnetic resonance (NMR) metabolomics, Seahorse, and the spatial distribution of metabolic co-enzymes using optical redox ratio imaging to describe breast cancer-LEC metabolic crosstalk. LECs co-cultured with breast cancer cells exhibited cell-line dependent altered metabolic profiles, including significant changes in lactate concentration in breast cancer co-culture. Cell metabolic phenotype analysis using Seahorse showed LECs in co-culture exhibited reduced mitochondrial respiration, increased reliance on glycolysis and reduced metabolic flexibility. Optical redox ratio measurements revealed reduced NAD(P)H levels in LECs potentially due to increased NAD(P)H utilization to maintain redox homeostasis. [Formula: see text]-labeled glucose experiments did not reveal lactate shuttling into LECs from breast cancer cells, yet showed other [Formula: see text] signals in LECs suggesting internalized metabolites and metabolic exchange between the two cell types. We also determined that breast cancer co-culture stimulated lymphangiogenic signaling in LECs, yet activation was not stimulated by lactate alone. Increased lymphangiogenic signaling suggests paracrine signaling between LECs and breast cancer cells which could have a pro-metastatic role.
    DOI:  https://doi.org/10.1038/s41598-020-76394-7
  11. J Bone Miner Res. 2020 Nov 30.
    Stegen S, Devignes CS, Torrekens S, Van Looveren R, Carmeliet P, Carmeliet G.
      Skeletal homeostasis critically depends on the proper anabolic functioning of osteolineage cells. Proliferation and matrix synthesis are highly demanding in terms of biosynthesis and bioenergetics, but the nutritional requirements that support these processes in bone-forming cells are not fully understood. Here, we show that glutamine metabolism is a major determinant of osteoprogenitor function during bone mass accrual. Genetic inactivation of the rate-limiting enzyme glutaminase 1 (GLS1) results in decreased postnatal bone mass, caused by impaired biosynthesis and cell survival. Mechanistically, we uncovered that GLS1-mediated glutamine catabolism supports nucleotide and amino acid synthesis, required for proliferation and matrix production. In addition, glutamine-derived glutathione prevents accumulation of reactive oxygen species and thereby safeguards cell viability. The pro-anabolic role of glutamine metabolism was further underscored in a model of parathyroid hormone (PTH)-induced bone formation. PTH administration increases glutamine uptake and catabolism, and GLS1 deletion fully blunts the PTH-induced osteoanabolic response. Taken together, our findings indicate that glutamine metabolism in osteoprogenitors is indispensable for bone formation. This article is protected by copyright. All rights reserved.
    Keywords:  PTH; biosynthesis; glutamine metabolism; osteoprogenitor; redox homeostasis
    DOI:  https://doi.org/10.1002/jbmr.4219
  12. Mol Cell Proteomics. 2020 Nov 30. pii: mcp.RA120.002384. [Epub ahead of print]
    Zhou J, Liu B, Li Z, Li Y, Chen X, Ma Y, Yan S, Yang X, Zhong L, Wu N.
      The histopathological subtype of lung adenocarcinoma (LUAD) is closely associated with prognosis. Micropapillary or solid predominant LUAD tends to relapse after surgery at an early stage, whereas lepidic pattern shows a favorable outcome. However, the molecular mechanism underlying this phenomenon remains unknown. Here, we recruited 31 lepidic predominant LUADs (LR: low-risk subtype group) and 28 micropapillary or solid predominant LUADs (HR: high-risk subtype group). Tissues of these cases were obtained and label-free quantitative proteomic and bioinformatic analyses were performed. Additionally, prognostic impact of targeted proteins was validated using The Cancer Genome Atlas databases (n=492) and tissue microarrays composed of early-stage LUADs (n=228). A total of 192 differentially expressed proteins were identified between tumor tissues of LR and HR and three clusters were identified via hierarchical clustering excluding eight proteins. Cluster 1 (65 proteins) showed a sequential decrease in expression from normal tissues to tumor tissues of LR and then to HR and was predominantly enriched in pathways such as tyrosine metabolism and ECM-receptor interaction, and increased matched mRNA expression of 18 proteins from this cluster predicted favorable prognosis. Cluster 2 (70 proteins) demonstrated a sequential increase in expression from normal tissues to tumor tissues of LR and then to HR and was mainly enriched in pathways such as extracellular organization, DNA replication and cell cycle, and high matched mRNA expression of 25 proteins indicated poor prognosis. Cluster 3 (49 proteins) showed high expression only in LR, with high matched mRNA expression of 20 proteins in this cluster indicating favorable prognosis. Furthermore, high expression of ERO1A and FEN1 at protein level predicted poor prognosis in early-stage LUAD, supporting the mRNA results. In conclusion, we discovered key differentially expressed proteins and pathways between low-risk and high-risk subtypes of early-stage LUAD. Some of these proteins could serve as potential biomarkers in prognostic evaluation.
    Keywords:  Biomarker: Diagnostic; Biomarker: Prognostic; Label-free quantification; Lung cancer; Mass Spectrometry; high-risk subtype; low-risk subtype; lung adenocarcinoma; prognosis; proteomics
    DOI:  https://doi.org/10.1074/mcp.RA120.002384
  13. Anal Chem. 2020 Dec 03.
    Kutuzova S, Colaianni P, Röst H, Sachsenberg T, Alka O, Kohlbacher O, Burla B, Torta F, Schrübbers L, Kristensen M, Nielsen L, Herrgård MJ, McCloskey D.
      Technological advances in high-resolution mass spectrometry (MS) vastly increased the number of samples that can be processed in a life science experiment, as well as volume and complexity of the generated data. To address the bottleneck of high-throughput data processing, we present SmartPeak (https://github.com/AutoFlowResearch/SmartPeak), an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of capillary electrophoresis-, gas chromatography-, and liquid chromatography (LC)-MS(/MS) data and high-pressure LC data for targeted and semitargeted metabolomics, lipidomics, and fluxomics experiments. The application allows for an approximate 100-fold reduction in the data processing time compared to manual processing while enhancing quality and reproducibility of the results.
    DOI:  https://doi.org/10.1021/acs.analchem.0c03421
  14. Med Princ Pract. 2020 Dec 03.
    Ashrafian H, Sounderajah V, Glen R, Ebbels T, Blaise BJ, Kalra D, Kultima K, Spjuth O, Tenori L, Salek R, Kale N, Haug K, Schober D, Rocca-Serra P, O'Donovan C, Steinbeck C, Cano I, de Atauri P, Cascante M.
      Metabolomics offers systematic identification and quantification of all metabolic products from the human body. This field could provide clinicians with new sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualised level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice and discuss the translational challenges that the field faces. We searched PubMed, Medline and Embase for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and NMR based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use centre around scalability of data interpretation, standardisation of sample handling practice and e-infrastructure. Routine utilisation of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.
    DOI:  https://doi.org/10.1159/000513545
  15. Metabolites. 2020 Nov 27. pii: E487. [Epub ahead of print]10(12):
    Lee YR, An KY, Jeon J, Kim NK, Lee JW, Hong J, Chung BC.
      Colorectal cancer is one of the most prevalent cancers in Korea and globally. In this study, we aimed to characterize the differential serum metabolomic profiles between pre-operative and post-operative patients with colorectal cancer. To investigate the significant metabolites and metabolic pathways associated with colorectal cancer, we analyzed serum samples from 68 patients (aged 20-71, mean 57.57 years). Untargeted and targeted metabolomics profiling in patients with colorectal cancer were performed using liquid chromatography-mass spectrometry. Untargeted analysis identified differences in sphingolipid metabolism, steroid biosynthesis, and arginine and proline metabolism in pre- and post-operative patients with colorectal cancer. We then performed quantitative target profiling of polyamines, synthesized from arginine and proline metabolism, to identify potential polyamines that may serve as effective biomarkers for colorectal cancer. Results indicate a significantly reduced serum concentration of putrescine in post-operative patients compared to pre-operative patients. Our metabolomics approach provided insights into the physiological alterations in patients with colorectal cancer after surgery.
    Keywords:  biomarker; colorectal cancer; liquid chromatography-mass spectrometry; metabolomics; polyamine; tumorectomy
    DOI:  https://doi.org/10.3390/metabo10120487
  16. Anal Chim Acta. 2021 Jan 02. pii: S0003-2670(20)31058-8. [Epub ahead of print]1141 144-162
    Wörheide MA, Krumsiek J, Kastenmüller G, Arnold M.
      Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
    Keywords:  Data integration; Lipidomics; Metabolomics; Multi-omics; Systems biology
    DOI:  https://doi.org/10.1016/j.aca.2020.10.038
  17. Anal Chem. 2020 Nov 30.
    Tian X, de Vries MP, Permentier HP, Bischoff R.
      Quantifying proteins based on peptide-coupled reporter ions is a multiplexed quantitative strategy in proteomics that alleviates the problem of ratio distortion caused by peptide cofragmentation, as commonly observed in other reporter-ion-based approaches, such as TMT and iTRAQ. Data-independent acquisition (DIA) is an attractive alternative to data-dependent acquisition (DDA) due to its better reproducibility. While multiplexed labeling is widely used in DDA, it is rarely used in DIA, presumably because current approaches lead to more complex MS2 spectra, severe ratio distortion, or to a reduction in quantification accuracy and precision. Herein, we present a versatile acetyl-alanine-glycine (Ac-AG) tag that conceals quantitative information in isobarically labeled peptides and reveals it upon tandem MS in the form of peptide-coupled reporter ions. Since the peptide-coupled reporter ion is precursor-specific while fragment ions of the peptide backbone originating from different labeling channels are identical, the Ac-AG tag is compatible with both DDA and DIA. By isolating the monoisotopic peak of the precursor ion in DDA, intensities of the peptide-coupled reporter ions represent the relative ratios between constituent samples, whereas in DIA, the ratio can be inferred after deconvoluting the peptide-coupled reporter ion isotopes. The proteome quantification capability of the Ac-AG tag was demonstrated by triplex labeling of a yeast proteome spiked with bovine serum albumin (BSA) over a 10-fold dynamic range. Within this complex proteomics background, BSA spiked at 1:5:10 ratios was detected at ratios of 1.00:4.87:10.13 in DDA and 1.16:5.20:9.64 in DIA.
    DOI:  https://doi.org/10.1021/acs.analchem.0c03858
  18. Metabolites. 2020 Nov 26. pii: E486. [Epub ahead of print]10(12):
    Faquih T, van Smeden M, Luo J, le Cessie S, Kastenmüller G, Krumsiek J, Noordam R, van Heemst D, Rosendaal FR, van Hylckama Vlieg A, Willems van Dijk K, Mook-Kanamori DO.
      Metabolomics studies have seen a steady growth due to the development and implementation of affordable and high-quality metabolomics platforms. In large metabolite panels, measurement values are frequently missing and, if neglected or sub-optimally imputed, can cause biased study results. We provided a publicly available, user-friendly R script to streamline the imputation of missing endogenous, unannotated, and xenobiotic metabolites. We evaluated the multivariate imputation by chained equations (MICE) and k-nearest neighbors (kNN) analyses implemented in our script by simulations using measured metabolites data from the Netherlands Epidemiology of Obesity (NEO) study (n = 599). We simulated missing values in four unique metabolites from different pathways with different correlation structures in three sample sizes (599, 150, 50) with three missing percentages (15%, 30%, 60%), and using two missing mechanisms (completely at random and not at random). Based on the simulations, we found that for MICE, larger sample size was the primary factor decreasing bias and error. For kNN, the primary factor reducing bias and error was the metabolite correlation with its predictor metabolites. MICE provided consistently higher performance measures particularly for larger datasets (n > 50). In conclusion, we presented an imputation workflow in a publicly available R script to impute untargeted metabolomics data. Our simulations provided insight into the effects of sample size, percentage missing, and correlation structure on the accuracy of the two imputation methods.
    Keywords:  imputation; k-nearest neighbors; metabolon; multiple imputation using chained equations; simulation; untargeted metabolomics; workflow
    DOI:  https://doi.org/10.3390/metabo10120486
  19. Cancers (Basel). 2020 Nov 24. pii: E3494. [Epub ahead of print]12(12):
    Curtin N, Bai P.
      The role of poly(ADP-ribose) polymerase-1 (PARP1) in DNA repair and as a potential target for anticancer therapy has been under investigation for more than 50 years [...].
    DOI:  https://doi.org/10.3390/cancers12123494