bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2019‒08‒25
twenty-five papers selected by
Giovanny Rodriguez Blanco
The Beatson Institute for Cancer Research


  1. Methods Mol Biol. 2019 ;2044 353-361
    Millán L, Fernández-Irigoyen J, Santamaría E, Mayo R.
      Lipidomics aims at characterizing lipid profiles and their biological role with respect to protein expression involved in lipid metabolism. Specifically, cerebrospinal fluid (CSF) lipidomics is offering a new perspective in the search for surrogate biomarkers to facilitate early diagnosis of psychiatric and neurodegenerative diseases. In this chapter, we describe a nontargeted approach to profile lipid molecular species present in human CSF using ultrahigh-performance liquid chromatography-electrospray ionization-time-of-flight mass spectrometry (UPLC-ESI-ToF-MS). This workflow complements the toolbox useful for the exploration and monitoring neurodegenerative mechanisms associated with a dysregulation in lipid metabolism.
    Keywords:  CSF; Lipidomics; Mass spectrometry
    DOI:  https://doi.org/10.1007/978-1-4939-9706-0_23
  2. Methods Mol Biol. 2019 ;2044 155-168
    Lachén-Montes M, González-Morales A, Fernández-Irigoyen J, Santamaría E.
      Cerebrospinal fluid (CSF) is in direct contact with the brain and represents a valuable source of mediators that reflect metabolic processes occurring in the central nervous system (CNS). In this sense, mass spectrometry (MS) methods have proven to be sensitive in quantifying the proteomic profiles of CSF, therefore being able to detect biomarker candidates for neurological disorders. In particular, a key development has been the use of multiplexing technologies to easily identify and quantify complex protein mixtures. This chapter describes a workflow suitable for the analysis of CSF proteome using isobaric labeling coupled to strong cation-exchange chromatography fractionation for its potential use as a biomarker discovery platform. In this case, the isobaric tags for relative and absolute quantitation (iTRAQ) label all proteins in a sample via free amines at the N-terminus and on the side chain of lysine residues. Then, the labeled samples are pooled and chromatographically fractionated. These fractions with the pooled samples are afterward analyzed by tandem mass spectrometry (MS/MS), and proteins are quantified by the relative intensities of the reporter ions in the MS/MS spectra, simultaneously obtaining the amino acid sequence. This method complements the neuroproteomic toolbox to identify new protein biomarkers not only for the early clinical diagnosis and disease staging of CNS-related disorders but also to elucidate the molecular mechanisms related to the pathophysiology of these symptoms.
    Keywords:  Cerebrospinal fluid; Labeling; Mass spectrometry; Proteomics
    DOI:  https://doi.org/10.1007/978-1-4939-9706-0_10
  3. Anal Chem. 2019 Aug 19.
    Simón-Manso Y, Marupaka R, Yan X, Liang Y, Telu KH, Mirokhin YA, Stein SE.
      A large fraction of ions observed in electrospray liquid chromatography-mass spectrometry (LC-ESI-MS) experiments of biological samples remain unidentified. One of the main reasons for this is that spectral libraries of pure compounds fail to account for the complexity of the metabolite profiling of complex materials. Recently, the NIST Mass Spectrometry Data Center has been developing a novel type of searchable mass spectral library that includes all recurrent unidentified spectra found in the sample profile. These libraries, in conjunction with the NIST tandem mass spectral library, allow analysts to explore most of the chemical space accessible to LC-MS analysis. In this work, we demonstrate how these libraries can provide a reliable fingerprint of the material by applying them to a variety of urine samples, including an extremely altered urine from cancer patients undergoing total body irradiation. The same workflow is applicable to any other biological fluid. The selected class of acylcarnitines is examined in detail and derived libraries and related software are freely available. They are intended to serve as online resources for continuing community review and improvement.
    DOI:  https://doi.org/10.1021/acs.analchem.9b02977
  4. Methods Mol Biol. 2019 ;2044 129-154
    Macron C, Núñez Galindo A, Cominetti O, Dayon L.
      Human cerebrospinal fluid (CSF) is a sample of choice in the study of brain disorders. This biological fluid circulates in the brain and the spinal cord and contains tissue-specific proteins, indicative of health and disease conditions. Despite its potential as a valid source of biological markers, CSF remains largely understudied as compared to blood, in particular due to its more invasive way of sampling.Challenges remain when performing proteomic analysis in clinical research studies. State-of-the-art mass spectrometry (MS) enables deep characterization of the human proteome. But some technical limitations are cardinal to be addressed, such as the capacity to routinely analyze large cohorts of samples. Importantly, a trade-off still needs to be made between the proteome coverage depth and the number of measured samples. In this context, we developed a scalable automated proteomic pipeline for the analysis of CSF. Because of its versatility, this workflow can be adapted to accommodate proteome coverage and/or sample throughput. It allows us to prepare and quantitatively analyze hundreds to thousands of CSF samples; it can also allow identification of more than 3000 proteins in a CSF sample when coupled with isoelectric focusing fractionation.In this chapter, we describe an end-to-end pipeline for the proteomic analysis of CSF. The main steps of the sample preparation comprise spiking of a standard, protein digestion, isobaric labeling, and purification; these are performed in a 96-well plate format enabling automation. Depending on the targeted depth of the CSF proteome, optional analytical steps can be included, such as the removal of abundant proteins and sample pre-fractionation. Liquid chromatography tandem MS as well as data processing and analysis complete the pipeline.
    Keywords:  Automation; Biomarker; CSF; Deep proteome; Immuno-affinity depletion; Large scale; Mass spectrometry; Missing proteins; Proteomics; Tandem mass tags
    DOI:  https://doi.org/10.1007/978-1-4939-9706-0_9
  5. J Proteome Res. 2019 Aug 20.
    Peng W, Goli M, Mirzaei P, Mechref Y.
      Breast cancer is a leading cancer in women and is considered to be the second-most common metastatic cancer following lung cancer. An estimated 10-16% of breast cancer patients are suffering from brain metastasis, and the diagnostic cases of breast cancer brain metastasis are increasing. Nevertheless, the mechanisms behind this process are still unclear. Aberrant glycosylation has been proved to be related to many diseases and cancer metastasis. However, studies of N-glycan isomer function in breast cancer brain metastasis are limited. In this study, the expressions of N-glycan isomers derived from five breast cancer cell lines and one brain cancer cell line were investigated and compared to a brain-seeking cell line, 231BR, to acquire a better understanding of the role glycan isomers play in breast cancer brain metastasis. The high temperature nanoPGC-LC-MS/MS achieved an efficient isomeric separation and permitted the identification and quantitation of 144 isomers from 50 N-glycan compositions. There were significant expression alterations of these glycan isomers among the different breast cancer cell lines. The increase of total glycan abundance and sialylation level were observed to be associated with breast cancer invasion. With regard to individual isomers, the greatest number of sialylated isomers was observed along with significant expression alterations in 231BR, suggesting a relationship between glycan sialylation and breast cancer brain metastasis. Furthermore, the increase of the α2,6-sialylation level in 231BR likely contributes to the passage of breast cancer cells through the blood-brain barrier, thus facilitating breast cancer brain metastasis. Meanwhile, the upregulation of highly sialylated glycan isomers with α2,6-linked sialic acids were found to be associated with breast cancer metastasis. This investigation of glycan isomer expressions, especially the unique isomeric expression in brain-seeking cell line 231BR, provides new information toward understanding the potential roles glycan isomers play during breast cancer metastasis and more clues for a deeper insight of this bioprocess.
    DOI:  https://doi.org/10.1021/acs.jproteome.9b00429
  6. iScience. 2019 Aug 06. pii: S2589-0042(19)30276-7. [Epub ahead of print]19 474-491
    Wheeler LJ, Watson ZL, Qamar L, Yamamoto TM, Sawyer BT, Sullivan KD, Khanal S, Joshi M, Ferchaud-Roucher V, Smith H, Vanderlinden LA, Brubaker SW, Caino CM, Kim H, Espinosa JM, Richer JK, Bitler BG.
      High-grade serous ovarian cancers (HGSOCs) arise from exfoliation of transformed cells from the fallopian tube, indicating that survival in suspension, and potentially escape from anoikis, is required for dissemination. We report here the results of a multi-omic study to identify drivers of anoikis escape, including transcriptomic analysis, global non-targeted metabolomics, and a genome-wide CRISPR/Cas9 knockout (GeCKO) screen of HGSOC cells cultured in adherent and suspension settings. Our combined approach identified known pathways, including NOTCH signaling, as well as novel regulators of anoikis escape. Newly identified genes include effectors of fatty acid metabolism, ACADVL and ECHDC2, and an autophagy regulator, ULK1. Knockdown of these genes significantly inhibited suspension growth of HGSOC cells, and the metabolic profile confirmed the role of fatty acid metabolism in survival in suspension. Integration of our datasets identified an anoikis-escape gene signature that predicts overall survival in many carcinomas.
    Keywords:  Biological Sciences; Cancer; Cancer Systems Biology; Cell Biology
    DOI:  https://doi.org/10.1016/j.isci.2019.07.049
  7. Mol Ther Nucleic Acids. 2019 Jul 22. pii: S2162-2531(19)30196-9. [Epub ahead of print]17 714-725
    Kim J, Basiri B, Hassan C, Punt C, van der Hage E, den Besten C, Bartlett MG.
      Eluforsen (previously known as QR-010) is a 33-mer 2'-O-methyl modified phosphorothioate antisense oligonucleotide targeting the F508del mutation in the gene encoding CFTR protein of cystic fibrosis patients. In this study, eluforsen was incubated with endo- and exonucleases and mouse liver homogenates to elucidate its in vitro metabolism. Mice and monkeys were used to determine in vivo liver and lung metabolism of eluforsen following inhalation. We developed a liquid chromatography-mass spectrometry method for the identification and semi-quantitation of the metabolites of eluforsen and then applied the method for in vitro and in vivo metabolism studies. Solid-phase extraction was used following proteinase K digestion for sample preparation. Chain-shortened metabolites of eluforsen by 3' exonuclease were observed in mouse liver in an in vitro incubation system and by either 3' exonuclease or 5' exonuclease in liver and lung samples from an in vivo mouse and monkey study. This study provides approaches for further metabolite characterization of 2'-ribose-modified phosphorothioate oligonucleotides in in vitro and in vivo studies to support the development of oligonucleotide therapeutics.
    Keywords:  antisense oligonucleotide; cystic fibrosis; eluforsen; ion-pair; liquid chromatography; mass spectrometry; metabolism
    DOI:  https://doi.org/10.1016/j.omtn.2019.07.006
  8. Metabolomics. 2019 Aug 21. 15(9): 115
    Castaño-Cerezo S, Kulyk-Barbier H, Millard P, Portais JC, Heux S, Truan G, Bellvert F.
      INTRODUCTION: Isoprenoids are amongst the most abundant and diverse biological molecules and are involved in a broad range of biological functions. Functional understanding of their biosynthesis is thus key in many fundamental and applicative fields, including systems biology, medicine and biotechnology. However, available methods do not yet allow accurate quantification and tracing of stable isotopes incorporation for all the isoprenoids precursors.OBJECTIVES: We developed and validated a complete methodology for quantitative metabolomics and isotopologue profiling of isoprenoid precursors in the yeast Saccharomyces cerevisiae.
    METHODS: This workflow covers all the experimental and computational steps from sample collection and preparation to data acquisition and processing. It also includes a novel quantification method based on liquid chromatography coupled to high-resolution mass spectrometry. Method validation followed the Metabolomics Standards Initiative guidelines.
    RESULTS: This workflow ensures accurate absolute quantification (RSD < 20%) of all mevalonate and prenyl pyrophosphates intermediates with a high sensitivity over a large linear range (from 0.1 to 50 pmol). In addition, we demonstrate that this workflow brings crucial information to design more efficient phytoene producers. Results indicate stable turnover rates of prenyl pyrophosphate intermediates in the constructed strains and provide quantitative information on the change of the biosynthetic flux of phytoene precursors.
    CONCLUSION: This methodology fills one of the last technical gaps for functional studies of isoprenoids biosynthesis and should be applicable to other eukaryotic and prokaryotic (micro)organisms after adaptation of some organism-dependent steps. This methodology also opens the way to 13C-metabolic flux analysis of isoprenoid biosynthesis.
    Keywords:  Isoprenoids; Isotope labeling experiments; Mass spectrometry; Systems biology
    DOI:  https://doi.org/10.1007/s11306-019-1580-8
  9. Mol Cell. 2019 Aug 06. pii: S1097-2765(19)30504-0. [Epub ahead of print]
    Li Y, Dammer EB, Gao Y, Lan Q, Villamil MA, Duong DM, Zhang C, Ping L, Lauinger L, Flick K, Xu Z, Wei W, Xing X, Chang L, Jin J, Hong X, Zhu Y, Wu J, Deng Z, He F, Kaiser P, Xu P.
      A surprising complexity of ubiquitin signaling has emerged with identification of different ubiquitin chain topologies. However, mechanisms of how the diverse ubiquitin codes control biological processes remain poorly understood. Here, we use quantitative whole-proteome mass spectrometry to identify yeast proteins that are regulated by lysine 11 (K11)-linked ubiquitin chains. The entire Met4 pathway, which links cell proliferation with sulfur amino acid metabolism, was significantly affected by K11 chains and selected for mechanistic studies. Previously, we demonstrated that a K48-linked ubiquitin chain represses the transcription factor Met4. Here, we show that efficient Met4 activation requires a K11-linked topology. Mechanistically, our results propose that the K48 chain binds to a topology-selective tandem ubiquitin binding region in Met4 and competes with binding of the basal transcription machinery to the same region. The change to K11-enriched chain architecture releases this competition and permits binding of the basal transcription complex to activate transcription.
    DOI:  https://doi.org/10.1016/j.molcel.2019.07.001
  10. Methods Mol Biol. 2019 ;2044 119-128
    Birke R, Krause E, Schümann M, Blasig IE, Haseloff RF.
      Molecular analysis of cerebrospinal fluid (CSF) provides comprehensive information on physiological and pathological processes related to the brain. In particular, proteomic studies give insights into the pathogenesis of many brain diseases which still pose diagnostic and therapeutic challenges. The identification of reliable biomarkers is an important step to meet these challenges. Mass spectrometry is an essential proteomic tool, not only for highly sensitive identification of proteins and posttranslational modifications, but also for their reliable quantification. Here, 18O labeling of tryptic peptides was employed to qualitative and quantitative analyses of protein fractions obtained by depletion of highly abundant proteins from cerebrospinal fluid. It was found that the execution of the investigated depletion protocols may cause the loss of potential protein biomarkers of neurological diseases.
    Keywords:  18O labeling; Biomarker; Cerebrospinal fluid; Depletion; Proteomics; Quantitation
    DOI:  https://doi.org/10.1007/978-1-4939-9706-0_8
  11. Anal Chem. 2019 Aug 22.
    Yin Y, Wang R, Cai Y, Wang Z, Zhu ZJ.
      SWATH-MS based data independent acquisition mass spectrometry (DIA-MS) technology has been recently developed for untargeted metabolomics due to its capability to acquire all MS2 spectra and high quantitative accuracy. However, software tools for deconvolving multiplexed MS/MS spectra from SWATH-MS with high efficiency and high quality are still lacking in untargeted metabolomics. Here, we developed a new software tool, namely, DecoMetDIA, to deconvolve multiplexed MS/MS spectra for metabolite identification and support the SWATH based untargeted metabolomics. In DecoMetDIA, it selected multiple model peaks to model the co-eluted and unresolved chromatographic peaks of fragment ions in multiplexed spectra, and decomposed them into a linear combination of the model peaks. DecoMetDIA enabled to reconstruct the MS2 spectra of metabolites from a variety of different biological samples with high coverages. We also demonstrated that the deconvolved MS2 spectra from DecoMetDIA were of high accuracy through the comparison to the experimental MS2 spectra from data dependent acquisition (DDA). Finally, about 90% of deconvolved MS2 spectra in various biological samples were successfully annotated using software tools such as MetDNA and Sirius. The results demonstrated that the deconvolved MS2 spectra obtained from DecoMetDIA were accurate and valid for metabolite identification and structural elucidation. The comparison of DecoMetDIA to other deconvolution software such as MS-DIAL demonstrated that it performs very well for small polar metabolites. The DecoMetDIA software is freely available on the Internet (https://github.com/ZhuMSLab/DecoMetDIA).
    DOI:  https://doi.org/10.1021/acs.analchem.9b02655
  12. Am J Clin Nutr. 2019 Aug 20. pii: nqz169. [Epub ahead of print]
    Navarro SL, Tarkhan A, Shojaie A, Randolph TW, Gu H, Djukovic D, Osterbauer KJ, Hullar MA, Kratz M, Neuhouser ML, Lampe PD, Raftery D, Lampe JW.
      BACKGROUND: Low-glycemic load dietary patterns, characterized by consumption of whole grains, legumes, fruits, and vegetables, are associated with reduced risk of several chronic diseases.METHODS: Using samples from a randomized, controlled, crossover feeding trial, we evaluated the effects on metabolic profiles of a low-glycemic whole-grain dietary pattern (WG) compared with a dietary pattern high in refined grains and added sugars (RG) for 28 d. LC-MS-based targeted metabolomics analysis was performed on fasting plasma samples from 80 healthy participants (n = 40 men, n = 40 women) aged 18-45 y. Linear mixed models were used to evaluate differences in response between diets for individual metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG)-defined pathways and 2 novel data-driven analyses were conducted to consider differences at the pathway level.
    RESULTS: There were 121 metabolites with detectable signal in >98% of all plasma samples. Eighteen metabolites were significantly different between diets at day 28 [false discovery rate (FDR) < 0.05]. Inositol, hydroxyphenylpyruvate, citrulline, ornithine, 13-hydroxyoctadecadienoic acid, glutamine, and oxaloacetate were higher after the WG diet than after the RG diet, whereas melatonin, betaine, creatine, acetylcholine, aspartate, hydroxyproline, methylhistidine, tryptophan, cystamine, carnitine, and trimethylamine were lower. Analyses using KEGG-defined pathways revealed statistically significant differences in tryptophan metabolism between diets, with kynurenine and melatonin positively associated with serum C-reactive protein concentrations. Novel data-driven methods at the metabolite and network levels found correlations among metabolites involved in branched-chain amino acid (BCAA) degradation, trimethylamine-N-oxide production, and β oxidation of fatty acids (FDR < 0.1) that differed between diets, with more favorable metabolic profiles detected after the WG diet. Higher BCAAs and trimethylamine were positively associated with homeostasis model assessment-insulin resistance.
    CONCLUSIONS: These exploratory metabolomics results support beneficial effects of a low-glycemic load dietary pattern characterized by whole grains, legumes, fruits, and vegetables, compared with a diet high in refined grains and added sugars on inflammation and energy metabolism pathways. This trial was registered at clinicaltrials.gov as NCT00622661.
    Keywords:  crossover; dietary intervention; dietary patterns; glycemic load; inflammation; insulin resistance; metabolomics; whole grains
    DOI:  https://doi.org/10.1093/ajcn/nqz169
  13. J Proteome Res. 2019 Aug 20.
    De Clerck L, Willems S, Noberini R, Restellini C, Van Puyvelde B, Daled S, Bonaldi T, Deforce D, Dhaenens M.
      Mass spectrometry (MS) has become the technique of choice for large-scale analysis of histone post-translational modifications (hPTMs) and their combinatorial patterns, especially in untargeted settings where novel discovery-driven hypotheses are being generated. However, MS-based histone analysis requires a distinct sample preparation, acquisition and data analysis workflow when compared to traditional MS-based approaches. To this end, sequential window acquisition of all theoretical fragment ion spectra (SWATH) has great potential, as it allows for untargeted accurate identification and quantification of hPTMs. Here, we present a complete SWATH workflow specifically adapted for the untargeted study of histones (hSWATH). We assess its validity on a technical dataset of a time-lapse deacetylation of a commercial histone extract using HDAC1, which contains a ground truth, i.e. acetylated substrate peptides reduce in intensity. We successfully apply this workflow in a biological setting and subsequently investigate the differential response to HDAC inhibition in different breast cancer cell lines.
    DOI:  https://doi.org/10.1021/acs.jproteome.9b00214
  14. Methods Mol Biol. 2019 ;2044 291-302
    Sheikh AM, Nagai A.
      Cystatin C (CST3) is expressed ubiquitously and implicated in several neurological diseases. It can be posttranscriptionally modified. CST3 is usually quantified in a biological sample using antibody-based methods. Posttranscriptional modification can hamper antibody-based detection systems by altering antibody-binding epitope(s). To circumvent this problem, enzymatic digestion and liquid chromatography tandem mass spectrometry (LC-MS/MS) technique can be employed to identify and measure peptides of a target protein in a complex biological mixture. This chapter describes an LC-MS/MS-based method for accurate measurement of CST3 in cerebrospinal fluid (CSF). Here, CSF was directly subjected to trypsin digestion and digested peptides were extracted using a solid-phase extraction column. Extracted peptide samples were directly used for LC-MS/MS-based identification and quantification of CST3 peptides. Comparing the concentration in a set of samples measured by LC-MS/MS with that of immunoassay shows that it was significantly higher when measured by LC-MS/MS method, suggesting it a better quantification method. This approach is particularly well suited when posttranscriptional modification of CST3 is suspected and sample volume of CSF is small.
    Keywords:  CSF; Cystatin C; Endopeptidase digestion; Liquid chromatography tandem mass spectrometry; Posttranscriptional modification; Quantitation
    DOI:  https://doi.org/10.1007/978-1-4939-9706-0_18
  15. Anal Chem. 2019 Aug 23.
    Schuhmann K, Moon H, Thomas H, Ackerman JM, Groessl M, Wagner N, Kellmann M, Henry I, Nadler A, Shevchenko A.
      Quantitative bottom-up shotgun lipidomics relies on molecular species-specific "signature" fragments consistently detectable in MS/MS spectra of analytes and standards. Molecular species of glycerophospholipids are typically quantified using carboxylate fragments of their fatty acid moieties produced by higher-energy collisional dissociation of their molecular anions. However, employing standards whose fatty acids moieties are similar, yet not identical to the target lipids could severely compromise their quantification. We developed a generic and portable fragmentation model implemented in the open-source LipidXte software that harmonizes the abundances of carboxylate anion fragments originating from fatty acid moieties having different sn-1/2 position at the glycerol backbone, length of the hydrocarbon chain, number and location of double bonds. The post-acquisition adjustment enables unbiased absolute (molar) quantification of glycerophospholipid species independent of instrument settings, collision energy, and employed internal standards.
    DOI:  https://doi.org/10.1021/acs.analchem.9b03270
  16. Metabolites. 2019 Aug 21. pii: E171. [Epub ahead of print]9(9):
    Borgsmüller N, Gloaguen Y, Opialla T, Blanc E, Sicard E, Royer AL, Le Bizec B, Durand S, Migné C, Pétéra M, Pujos-Guillot E, Giacomoni F, Guitton Y, Beule D, Kirwan J.
      Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.
    Keywords:  gas chromatography-mass spectrometry (GC-MS); machine learning; metabolomics; parameter optimisation; peak classification; peak detection; pre-processing workflow; support vector machine
    DOI:  https://doi.org/10.3390/metabo9090171
  17. Oncol Lett. 2019 Aug;18(2): 1579-1584
    Wen SS, Zhang TT, Xue DX, Wu WL, Wang YL, Wang Y, Ji QH, Zhu YX, Qu N, Shi RL.
      Warburg found that tumor cells exhibit high-level glycolysis, even under aerobic condition, which is known as the 'Warburg effect'. As systemic changes in the entire metabolic network are gradually revealed, it is recognized that metabolic reprogramming has gone far beyond the imagination of Warburg. Metabolic reprogramming involves an active change in cancer cells to adapt to their biological characteristics. Thyroid cancer is a common endocrine malignant tumor whose metabolic characteristics have been studied in recent years. Some drugs targeting tumor metabolism are under clinical trial. This article reviews the metabolic changes and mechanisms in thyroid cancer, aiming to find metabolic-related molecules that could be potential markers to predict prognosis and metabolic pathways, or could serve as therapeutic targets. Our review indicates that knowledge in metabolic alteration has potential contributions in the diagnosis, treatment and prognostic evaluation of thyroid cancer, but further studies are needed for verification as well.
    Keywords:  metabolism; predictive factor; prognosis; targeted therapy; thyroid cancer
    DOI:  https://doi.org/10.3892/ol.2019.10485
  18. Methods Mol Biol. 2019 ;2044 193-219
    Gay M, Sánchez-Jiménez E, Villarreal L, Vilanova M, Huguet R, Arauz-Garofalo G, Díaz-Lobo M, López-Ferrer D, Vilaseca M.
      Cerebrospinal fluid (CSF) is the fluid of choice to study pathologies and disorders of the central nervous system (CNS). Its composition, especially its proteins and peptides, holds the promise that it may reflect the pathological state of an individual. Traditionally, proteins and peptides in CSF have been analyzed using bottom-up proteomics technologies in the search of high proteome coverage. However, the limited protein sequence coverage of this technology means that information regarding post-translational modifications (PTMs) and alternative splice variants is lost. As an alternative technology, top-down proteomics offers low to medium proteome coverage, but high protein coverage enabling almost a full characterization of the proteins' primary structure. This allows us to precisely identify distinct molecular forms of proteins (proteoforms) as well as naturally occurring bioactive peptide fragments, which could be of critical biological relevance and would otherwise remain undetected with a classical proteomics approach.Here, we describe various strategies including sample preparation protocols, off-line intact protein prefractionation, and LC-MS/MS methods together with data analysis pipelines to analyze cerebrospinal fluid (CSF) by top-down proteomics. However, there is not a unique or standardized method and the selection of the top-down strategy will depend on the exact goal of the study. Here, we describe various top-down proteomics methods that enable rapid protein characterization and may be an excellent companion analytical workflow in the search for new protein biomarkers in neurodegenerative diseases.
    Keywords:  Depletion; Human CSF; Peptide fragments; Prefractionation; Proteoforms; Top-down
    DOI:  https://doi.org/10.1007/978-1-4939-9706-0_12
  19. Methods Mol Biol. 2019 ;2044 273-289
    Lachén-Montes M, González-Morales A, Fernández-Irigoyen J, Santamaría E.
      Nowadays, diagnosis of neurodegenerative disorders is mainly based on neuroimaging and clinical symptoms, although postmortem neuropathological confirmation remains the gold standard diagnostic technique. Therefore, cerebrospinal fluid (CSF) proteome is considered a valuable molecular repository for diagnosing and targeting the neurodegenerative process. It is well known that olfactory dysfunction is among the earliest features of synucleinopathies such as Parkinson's disease (PD). Consequently, we consider that the application of tissue proteomics in primary olfactory structures is an ideal approach to explore early pathophysiological changes, detecting olfactory proteins that might be tested in CSF as potential biomarkers. Data mining of mass spectrometry-generated datasets has revealed that 30% of the olfactory bulb (OB) proteome is also localized in CSF. In this chapter, we describe a method that utilizes label-free quantitative proteomics and computational analysis to characterize human OB proteomes and potential cerebrospinal fluid (CSF) biomarkers associated with neurodegenerative syndromes. For that, we applied peptide fractionation methods, followed by tandem mass spectrometry (nanoLC-MS/MS), in silico analysis, and semi-quantitative orthogonal techniques in OB derived from PD subjects. After obtaining the differential OB proteome across Lewy-type alpha-synucleinopathy (LTS) stages and further validating the method, this workflow was applied to probe changes in NEGR1 (neuronal growth regulator 1) and GNPDA2 (glucosamine-6-phosphate deaminase 2) protein levels in CSF derived from parkinsonian subjects with respect to controls, observing an inverse correlation between both proteins and α-synuclein, the principal component analysis of Lewy pathology.
    Keywords:  Cerebrospinal fluid; GNPDA2; Mass spectrometry; NEGR1; Olfactory bulb; Parkinson’s disease; Proteomics
    DOI:  https://doi.org/10.1007/978-1-4939-9706-0_17
  20. Methods Mol Biol. 2019 ;2044 337-342
    Hörmann P, Barkovits K, Marcus K, Hiller K.
      In the field of neurodegeneration, it is important to identify biomarkers that enable early disease prediction, since these disorders start decades before clinical symptoms manifest. Cerebrospinal fluid (CSF) is considered an excellent source for biomarker discovery since it is in direct contact with the extracellular space of the brain and directly reflects disease-specific changes.While the liquor drainage is no major risk factor for patients, it is still not as easy and popular as simple blood sampling and less liquid can be collected. Especially when a variety of experiments for one cohort is planned, the volume of CSF can be a limiting factor. Therefore, it is essential that extraction and analytical methods are adapted to low amounts of liquor. If in follow-up studies, additional replicates to increase statistical significance or different extraction approaches are planned, the required amounts have to be minimized.With this extraction method, a combined proteomics and metabolomics approach is possible. This opportunity implies a variety of advantages. First, a classification matrix based on the comprehensive data set has a potentially higher accuracy even without a deeper understanding of the biological meaning of the different omics changes. If the proteome and metabolome differences can be linked to each other, this approach can conceivably open so far unknown doors regarding the cause or progression of different diseases like Alzheimer's or Parkinson's disease.
    Keywords:  Derivatization; Gas chromatography; In-solution digest; Liquid chromatography; Metabolomics; Methanol precipitation
    DOI:  https://doi.org/10.1007/978-1-4939-9706-0_21
  21. Methods Mol Biol. 2019 ;2044 321-336
    Mendes VM, Coelho M, Manadas B.
      Cerebrospinal fluid (CSF) has been considered the key source for the search of biomarkers, in particular for neurological diseases, such as Alzheimer's and Parkinson's disease, since it reflects the state of the central nervous system (CNS). Finding biomarkers in the earliest stages of neurodegenerative diseases has become imperative, since, at the moment, there are no drugs that can reverse these pathological processes. Untargeted metabolomics analysis by liquid chromatography combined with SWATH-MS relative quantification is an emerging approach to search for potential biomarkers. In this chapter, we describe a method for untargeted metabolomics analysis of CSF samples that can also be used in parallel to a proteomics approach. The analysis is focused on the SWATH acquisition mode, where beyond precursor's relative quantification, the information of the MS/MS relative quantification is also used to help in the search of new potential biomarkers.
    Keywords:  Cerebrospinal fluid; Potential biomarkers; SWATH-MS relative quantification; Untargeted metabolomics
    DOI:  https://doi.org/10.1007/978-1-4939-9706-0_20
  22. J Am Soc Mass Spectrom. 2019 Aug 22.
    Baker ES, Patti GJ.
      In November 2018, the American Society for Mass Spectrometry hosted the Annual Fall Workshop on informatic methods in metabolomics. The Workshop included sixteen lectures presented by twelve invited speakers. The focus of the talks was untargeted metabolomics performed with liquid chromatography/mass spectrometry. In this review, we highlight five recurring topics that were covered by multiple presenters: (i) data sharing, (ii) artifacts and contaminants, (iii) feature degeneracy, (iv) database organization, and (v) requirements for metabolite identification. Our objective here is to present viewpoints that were widely shared among participants, as well as those in which varying opinions were articulated. We note that most of the presenting speakers employed different data processing software, which underscores the diversity of informatic programs currently being used in metabolomics. We conclude with our thoughts on the potential role of reference datasets as a step towards standardizing data processing methods in metabolomics.
    Keywords:  ASMS Fall Workshop; Informatics; Metabolism; Metabolomics
    DOI:  https://doi.org/10.1007/s13361-019-02295-3
  23. Nat Commun. 2019 Aug 21. 10(1): 3763
    Xiao Z, Dai Z, Locasale JW.
      The tumor milieu consists of numerous cell types each existing in a different environment. However, a characterization of metabolic heterogeneity at single-cell resolution is not established. Here, we develop a computational pipeline to study metabolic programs in single cells. In two representative human cancers, melanoma and head and neck, we apply this algorithm to define the intratumor metabolic landscape. We report an overall discordance between analyses of single cells and those of bulk tumors with higher metabolic activity in malignant cells than previously appreciated. Variation in mitochondrial programs is found to be the major contributor to metabolic heterogeneity. Surprisingly, the expression of both glycolytic and mitochondrial programs strongly correlates with hypoxia in all cell types. Immune and stromal cells could also be distinguished by their metabolic features. Taken together this analysis establishes a computational framework for characterizing metabolism using single cell expression data and defines principles of the tumor microenvironment.
    DOI:  https://doi.org/10.1038/s41467-019-11738-0
  24. J Mol Biol. 2019 Aug 15. pii: S0022-2836(19)30509-1. [Epub ahead of print]
    Bestard-Escalas J, Maimó-Barceló A, Pérez-Romero K, Lopez DH, Barceló-Coblijn G.
      Membrane lipids are essential for life; however, research on how cells regulate cell lipid composition has been falling behind for quite some time. One reason was the difficulty in establishing analytical methods able to cope with the cell lipid repertoire. Development of a diversity of mass spectrometry-based (MS) technologies, including imaging MS, has helped to demonstrate beyond doubt that the cell lipidome is not only greatly cell-type dependent but also highly sensitive to any pathophysiological alteration such as differentiation or tumorigenesis. Interestingly, the current popularization of metabolomic studies among numerous disciplines has led many researchers to rediscover lipids. Hence, it is important to underscore the peculiarities of these metabolites and their metabolism, which are both radically different from protein and nucleic acid metabolism. Once differences in lipid composition have been established, researchers face a rather complex scenario, to investigate the signaling pathways and molecular mechanisms accounting for their results. Thus, a detail often overlooked, but of crucial relevance, is the complex networks of enzymes involved in controlling the level of each one of the lipid species present in the cell. In most cases, these enzymes are redundant and promiscuous, complicating any study on lipid metabolism, since the modification of one particular lipid enzyme impacts simultaneously on many species. Altogether, this review aims to describe the difficulties in delving into the regulatory mechanisms tailoring the lipidome at the activity, genetic, and epigenetic level, while conveying the numerous, stimulating, and sometimes unexpected research opportunities afforded by this type of studies.
    DOI:  https://doi.org/10.1016/j.jmb.2019.08.006
  25. Methods Mol Biol. 2019 ;2044 61-67
    Barkovits K, Tönges L, Marcus K.
      To study changes in neurological diseases and to identify disease-related mechanisms or biomarkers for diagnosis, cerebrospinal fluid (CSF) is frequently used for proteomic-based discovery. In the last years, development and application of mass spectrometry (MS) techniques have made essential contributions to proteomic studies including protein identification as well as quantification. Until recently, biomarker discovery studies were performed through bottom-up proteomics utilizing data-dependent acquisition. However, drawbacks like stochastic selection of precursor ions cause the exclusion of low-abundant ions from fragmentation as well as from data analysis leading to technical variances among different samples and result in inconsistent data sets. In contrast, data-independent acquisition (DIA) enables almost complete and reproducible quantitative analysis gaining more and more interest as a method for reliable MS-based protein quantification. Besides the utilization of a proper analysis platform, a prerequisite for biomarker studies is the selection of suitable samples and sample processing strategies. Especially for CSF, blood contamination has tremendous impact on the quantitative analysis. In addition, complex processing methods such as protein or peptide fractionation prior to MS analysis can lead to variabilities that affect the reliability of the quantitative results. Here we present methods to evaluate in a first step the CSF quality in regard to blood contamination for the subsequent MS-based sample preparation and finally a DIA method for the analysis of CSF.
    Keywords:  Blood contamination; Data-dependent acquisition; Data-independent acquisition; In-solution digest; Mass spectrometry
    DOI:  https://doi.org/10.1007/978-1-4939-9706-0_4