bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2020‒10‒11
twenty-one papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh


  1. Prostaglandins Leukot Essent Fatty Acids. 2020 Sep 15. pii: S0952-3278(20)30133-2. [Epub ahead of print]161 102175
    Fernandez RF, Ellis JM.
      Each individual cell-type is defined by its distinct morphology, phenotype, molecular and lipidomic profile. The importance of maintaining cell-specific lipidomic profiles is exemplified by the numerous diseases, disorders, and dysfunctional outcomes that occur as a direct result of altered lipidome. Therefore, the mechanisms regulating cellular lipidome diversity play a role in maintaining essential biological functions. The brain is an organ particularly rich in phospholipids, the main constituents of cellular membranes. The phospholipid acyl-chain profile of membranes in the brain is rather diverse due in part to the high degree of cellular heterogeneity. These membranes and the acyl-chain composition of their phospholipids are highly regulated, but the mechanisms that confer this tight regulation are incompletely understood. A family of enzymes called acyl-CoA synthetases (ACSs) stands at a pinnacle step allowing influence over cellular acyl-chain selection and subsequent metabolic flux. ACSs perform the initial reaction for cellular fatty acid metabolism by ligating a Coenzyme A to a fatty acid which both traps a fatty acid within a cell and activates it for metabolism. The ACS family of enzymes is large and diverse consisting of 25-26 family members that are nonredundant, each with unique distribution across and within cell types, and differential fatty acid substrate preferences. Thus, ACSs confer a critical intracellular fatty acid selecting step in a cell-type dependent manner providing acyl-CoA moieties that serve as essential precursors for phospholipid synthesis and remodeling, and therefore serve as a key regulator of cellular membrane acyl-chain compositional diversity. Here we will discuss how the contribution of individual ACSs towards brain lipid metabolism has only just begun to be elucidated and discuss the possibilities for how ACSs may differentially regulate brain lipidomic diversity.
    Keywords:  Acyl-CoA synthetase; Brain; Fatty acid metabolic control; Polyunsaturated fatty acids
    DOI:  https://doi.org/10.1016/j.plefa.2020.102175
  2. Biochim Biophys Acta Rev Cancer. 2020 Oct 02. pii: S0304-419X(20)30161-X. [Epub ahead of print] 188442
    Taddei ML, Pardella E, Pranzini E, Raugei G, Paoli P.
      In mammalian cells, tyrosine phosphorylation is one of the main mechanisms responsible for regulating signal transduction pathways and key cellular functions. Moreover, recent studies demonstrated that tyrosine phosphorylation influences the activity of some metabolic enzymes, even if it remains to be clarified whether tyrosine phosphorylation can be considered a general mechanism involving most of the metabolic enzymes or only a subset of these. To elucidate this aspect, we conducted a two-step analysis. First, we analyzed literature to identify all the metabolic enzymes whose activity is affected by tyrosine phosphorylation. Second, we crossed these data with those obtained from the PhosphoSitePlus database analysis. Collected information was used to depict an exhaustive map showing the real spread of tyrosine phosphorylation among metabolic enzymes. In summary, data reported in this review highlight that tyrosine phosphorylation is not a sporadic event but a widespread post-translational modification, which is essential to promote the metabolic reprogramming of cancer cells.
    Keywords:  Metabolic enzymes; PhosphoSitePlus; Tumor metabolism; Tyrosine phosphorylation
    DOI:  https://doi.org/10.1016/j.bbcan.2020.188442
  3. Pharmaceuticals (Basel). 2020 Oct 05. pii: E292. [Epub ahead of print]13(10):
    Guerra B, Issinger OG.
      Uncontrolled proliferation is a feature defining cancer and it is linked to the ability of cancer cells to effectively adapt their metabolic needs in response to a harsh tumor environment. Metabolic reprogramming is considered a hallmark of cancer and includes increased glucose uptake and processing, and increased glutamine utilization, but also the deregulation of lipid and cholesterol-associated signal transduction, as highlighted in recent years. In the first part of the review, we will i) provide an overview of the major types of lipids found in eukaryotic cells and their importance as mediators of intracellular signaling pathways ii) analyze the main metabolic changes occurring in cancer development and the role of oncogenic signaling in supporting aberrant lipid metabolism and iii) discuss combination strategies as powerful new approaches to cancer treatment. The second part of the review will address the emerging role of CK2, a conserved serine/threonine protein kinase, in lipid homeostasis with an emphasis regarding its function in lipogenesis and adipogenesis. Evidence will be provided that CK2 regulates these processes at multiple levels. This suggests that its pharmacological inhibition combined with dietary restrictions and/or inhibitors of metabolic targets could represent an effective way to undermine the dependency of cancer cells on lipids to interfere with tumor progression.
    Keywords:  adipogenesis; lipogenesis; metabolic changes in cancer; protein kinase CK2
    DOI:  https://doi.org/10.3390/ph13100292
  4. Anal Bioanal Chem. 2020 Oct 03.
    Franke AA, Li X, Lai JF.
      Aminomethylphosphonic acid (AMPA) is the main metabolite of glyphosate (GLYP) and phosphonic acids in detergents. GLYP is a synthetic herbicide frequently used worldwide alone or together with its analog glufosinate (GLUF). The general public can be exposed to these potentially harmful chemicals; thus, sensitive methods to monitor them in humans are urgently required to evaluate health risks. We attempted to simultaneously detect GLYP, AMPA, and GLUF in human urine by high-resolution accurate-mass liquid chromatography mass spectrometry (HRAM LC-MS) before and after derivatization with 9-fluorenylmethoxycarbonyl chloride (Fmoc-Cl) or 1-methylimidazole-sulfonyl chloride (ImS-Cl) with several urine pre-treatment and solid phase extraction (SPE) steps. Fmoc-Cl derivatization achieved the best combination of method sensitivity (limit of detection; LOD) and accuracy for all compounds compared to underivatized urine or ImS-Cl-derivatized urine. Before derivatization, the best steps for GLYP involved 0.4 mM ethylenediaminetetraacetic acid (EDTA) pre-treatment followed by SPE pre-cleanup (LOD 37 pg/mL), for AMPA involved no EDTA pre-treatment and no SPE pre-cleanup (LOD 20 pg/mL) or 0.2-0.4 mM EDTA pre-treatment with no SPE pre-cleanup (LOD 19-21 pg/mL), and for GLUF involved 0.4 mM EDTA pre-treatment and no SPE pre-cleanup (LOD 7 pg/mL). However, for these methods, accuracy was sufficient only for AMPA (101-105%), while being modest for GLYP (61%) and GLUF (63%). Different EDTA and SPE treatments prior to Fmoc-Cl derivatization resulted in high sensitivity for all analytes but satisfactory accuracy only for AMPA. Thus, we conclude that our HRAM LC-MS method is suited for urinary AMPA analysis in cross-sectional studies.
    Keywords:  Aminomethylphosphonic acid; Glufosinate; Glyphosate; Humans; LC-MS; Urine
    DOI:  https://doi.org/10.1007/s00216-020-02966-1
  5. Cell Cycle. 2020 Oct 04. 1-9
    Roci I, Watrous JD, Lagerborg KA, Jain M, Nilsson R.
      Proliferating cells must synthesize a wide variety of macromolecules while progressing through the cell cycle, but the coordination between cell cycle progression and cellular metabolism is still poorly understood. To identify metabolic processes that oscillate over the cell cycle, we performed comprehensive, non-targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics of HeLa cells isolated in the G1 and SG2M cell cycle phases, capturing thousands of diverse metabolite ions. When accounting for increased total metabolite abundance due to cell growth throughout the cell cycle, 18% of the observed LC-HRMS peaks were at least twofold different between the stages, consistent with broad metabolic remodeling throughout the cell cycle. While most amino acids, phospholipids, and total ribonucleotides were constant across cell cycle phases, consistent with the view that total macromolecule synthesis does not vary across the cell cycle, certain metabolites were oscillating. For example, ribonucleotides were highly phosphorylated in SG2M, indicating an increase in energy charge, and several phosphatidylinositols were more abundant in G1, possibly indicating altered membrane lipid signaling. Within carbohydrate metabolism, pentose phosphates and methylglyoxal metabolites were associated with the cycle. Interestingly, hundreds of yet uncharacterized metabolites similarly oscillated between cell cycle phases, suggesting previously unknown metabolic activities that may be synchronized with cell cycle progression, providing an important resource for future studies.
    Keywords:  Metabolomics; S-lactoylglutathione; cell sorting
    DOI:  https://doi.org/10.1080/15384101.2020.1825203
  6. FEBS J. 2020 Oct 08.
    Berndt N, Eckstein J, Heucke N, Wuensch T, Gajowski R, Stockmann M, Meierhofer D, Holzhütter HG.
      Metabolic reprogramming is a characteristic feature of cancer cells but there is no unique metabolic program for all tumors. Genetic and gene expression studies have revealed heterogeneous inter- and intra-tumor patterns of metabolic enzymes and membrane transporters. The functional implications of this heterogeneity remain often elusive. Here, we applied a systems biology approach to gain a comprehensive and quantitative picture of metabolic changes in individual hepatocellular carcinoma (HCC). We used protein intensity profiles determined by mass spectrometry in samples of ten human HCCs and the adjacent non-cancerous tissue to calibrate Hepatokin1, a complex mathematical model of liver metabolism. We computed the 24h profile of 18 metabolic functions related to carbohydrate, lipid and nitrogen metabolism. There was a general tendency among the tumors towards downregulated glucose uptake and glucose release albeit with large inter-tumor variability. This finding calls into question that the Warburg effect dictates the metabolic phenotype of HCC. All tumors comprised elevated β-oxidation rates. Urea synthesis was found to be consistently downregulated but without compromising the tumor's capacity for ammonia detoxification owing to increased glutamine synthesis. The largest inter-tumor heterogeneity was found for the uptake and release of lactate and the size of the cellular glycogen content. In line with the observed metabolic heterogeneity, the individual HCCs differed largely in their vulnerability against pharmacological treatment with metformin. Taken together, our approach provided a comprehensive and quantitative characterization of HCC metabolism that may pave the way for a computational a priori assessment of pharmacological therapies targeting metabolic processes of HCC.
    Keywords:  kinetic modeling; liver; mathematical model; metabolism; tumor metabolism
    DOI:  https://doi.org/10.1111/febs.15587
  7. Elife. 2020 10 05. pii: e56749. [Epub ahead of print]9
    Jin H, Wang S, Zaal EA, Wang C, Wu H, Bosma A, Jochems F, Isima N, Jin G, Lieftink C, Beijersbergen R, Berkers CR, Qin W, Bernards R.
      The dependency of cancer cells on glutamine may be exploited therapeutically as a new strategy for treating cancers that lack druggable driver genes. Here we found that human liver cancer was dependent on extracellular glutamine. However, targeting glutamine addiction using the glutaminase inhibitor CB-839 as monotherapy had a very limited anticancer effect, even against the most glutamine addicted human liver cancer cells. Using a chemical library, we identified V-9302, a novel inhibitor of glutamine transporter ASCT2, as sensitizing glutamine dependent (GD) cells to CB-839 treatment. Mechanically, a combination of CB-839 and V-9302 depleted glutathione and induced reactive oxygen species (ROS), resulting in apoptosis of GD cells. Moreover, this combination also showed tumor inhibition in HCC xenograft mouse models in vivo. Our findings indicate that dual inhibition of glutamine metabolism by targeting both glutaminase and glutamine transporter ASCT2 represents a potential novel treatment strategy for glutamine addicted liver cancers.
    Keywords:  CB-839; cancer biology; glutamine addiction; glutaminolysis; hepatocellular carcinoma; human
    DOI:  https://doi.org/10.7554/eLife.56749
  8. Crit Rev Anal Chem. 2020 Oct 07. 1-23
    Fraga-Corral M, Carpena M, Garcia-Oliveira P, Pereira AG, Prieto MA, Simal-Gandara J.
      Metabolomics is a young field of knowledge that arises linked to other omics such as genomics, transcriptomics, and proteomics. This discipline seeks to understand the performance of metabolites, identifying, quantifying them, and thus understanding its mechanism of action. This new branch of omics science shows high potential, due to its noninvasive character and its close relation with phenotype. Several techniques have been developed to study the metabolome of biological samples, fundamentally nuclear magnetic resonance (NMR), mass spectrometry (MS) and vibrational spectrometry (VS) or a combination of several techniques. These techniques are focused to separate, detect, characterize, and quantify metabolites, as well as elucidate their structures and their function on the metabolic pathways they are involved. However, due to the complexity of the metabolome, in most cases it is necessary to apply several of these techniques to understand completely the whole scenery. This review is aimed to offer a summary of the current knowledge of these analytical techniques for metabolomics and their application to different fields as environmental, food or health sciences. Each technique shows different advantages and drawbacks depending on their technical characteristics and limitations, some factors, such as the aim of the study or the nature of the biological sample will condition the choice. Regarding their applications, NMR has been employed specially to identify new compounds and elucidate structures. The use of MS has gained popularity because of its versatility, easiness to be coupled to separation techniques and its high sensitivity. Whereas VS is widely employed for in situ studies, due to its nondestructive character. Metabolomics applications in different science fields are growing each year, due to advances in analytical techniques and combination with other omics that allow to increase the comprehension of metabolic processes. Further development of analytical tools is necessary to continue exploiting all the possibilities of metabolomics. Highlights Metabolomics seeks to understand the performance of metabolites and its mechanism of action Different metabolomics techniques have been developed and improved in the last years Metabolomics applications cover clinical, pharmaceuticals and food and environmental sciences This review is aimed to offer a summary of the current knowledge of these analytical techniques.
    Keywords:  Metabolomics; applications; mass spectrometry; metabolite; nuclear magnetic resonance; vibrational spectroscopy
    DOI:  https://doi.org/10.1080/10408347.2020.1823811
  9. Mol Cell. 2020 Sep 29. pii: S1097-2765(20)30648-1. [Epub ahead of print]
    Cai Z, Li CF, Han F, Liu C, Zhang A, Hsu CC, Peng D, Zhang X, Jin G, Rezaeian AH, Wang G, Zhang W, Pan BS, Wang CY, Wang YH, Wu SY, Yang SC, Hsu FC, D'Agostino RB, Furdui CM, Kucera GL, Parks JS, Chilton FH, Huang CY, Tsai FJ, Pasche B, Watabe K, Lin HK.
      Cancer metastasis accounts for the major cause of cancer-related deaths. How disseminated cancer cells cope with hostile microenvironments in secondary site for full-blown metastasis is largely unknown. Here, we show that AMPK (AMP-activated protein kinase), activated in mouse metastasis models, drives pyruvate dehydrogenase complex (PDHc) activation to maintain TCA cycle (tricarboxylic acid cycle) and promotes cancer metastasis by adapting cancer cells to metabolic and oxidative stresses. This AMPK-PDHc axis is activated in advanced breast cancer and predicts poor metastasis-free survival. Mechanistically, AMPK localizes in the mitochondrial matrix and phosphorylates the catalytic alpha subunit of PDHc (PDHA) on two residues S295 and S314, which activates the enzymatic activity of PDHc and alleviates an inhibitory phosphorylation by PDHKs, respectively. Importantly, these phosphorylation events mediate PDHc function in cancer metastasis. Our study reveals that AMPK-mediated PDHA phosphorylation drives PDHc activation and TCA cycle to empower cancer cells adaptation to metastatic microenvironments for metastasis.
    Keywords:  AMPK; PDHA; TCA cycle; breast cancer; cancer metastasis; metabolic stress
    DOI:  https://doi.org/10.1016/j.molcel.2020.09.018
  10. Cancer Metab. 2020 ;8 12
    Mollick T, Laín S.
      By providing the necessary building blocks for nucleic acids and precursors for cell membrane synthesis, pyrimidine ribonucleotides are essential for cell growth and proliferation. Therefore, depleting pyrimidine ribonucleotide pools has long been considered as a strategy to reduce cancer cell growth. Here, we review the pharmacological approaches that have been employed to modulate pyrimidine ribonucleotide synthesis and degradation routes and discuss their potential use in cancer therapy. New developments in the treatment of myeloid malignancies with inhibitors of pyrimidine ribonucleotide synthesis justify revisiting the literature as well as discussing whether targeting this metabolic pathway can be effective and sufficiently selective for cancer cells to warrant an acceptable therapeutic index in patients.
    Keywords:  CAD; CDA; CTPS; Cancer therapy; DHODH; Nucleoside transporters; Pyrimidine ribonucleotide metabolism; Therapeutic index; UMPS
    DOI:  https://doi.org/10.1186/s40170-020-00218-5
  11. J Lipid Res. 2020 Oct 09. pii: jlr.S120001025. [Epub ahead of print]
    Liebisch G, Fahy E, Aoki J, Dennis EA, Durand T, Ejsing C, Fedorova M, Feussner I, Griffiths WJ, Koefeler H, Merrill AH, Murphy RC, O'Donnell VB, Oskolkova OV, Subramaniam S, Wakelam M, Spener F.
      A comprehensive and standardized system to report lipid structures analyzed by mass spectrometry is essential for the communication and storage of lipidomics data. Herein, an update on both the LIPID MAPS classification system and shorthand notation of lipid structures is presented for lipid categories Fatty Acyls (FA), Glycerolipids (GL), Glycerophospholipids (GP), Sphingolipids (SP), and Sterols (ST). With its major changes, i.e. annotation of ring double bond equivalents and number of oxygens, the updated shorthand notation facilitates reporting of newly delineated oxygenated lipid species as well. For standardized reporting in lipidomics, the hierarchical architecture of shorthand notation reflects the diverse structural resolution powers provided by mass spectrometric assays. Moreover, shorthand notation is expanded beyond mammalian phyla to lipids from plant and yeast phyla. Finally, annotation of atoms is included for the use of stable isotope-labelled compounds in metabolic labelling experiments or as internal standards.This update on lipid classification, nomenclature and shorthand annotation for lipid mass spectra is considered a standard for lipid data presentation.
    Keywords:  Glycerolipids; Glycerophospholipids; Lipidomics; Mass spectrometry; Sphingolipids; Sterols
    DOI:  https://doi.org/10.1194/jlr.S120001025
  12. Tumour Biol. 2020 Oct;42(10): 1010428320965284
    Farhadi P, Yarani R, Dokaneheifard S, Mansouri K.
      Glucose, as the main consuming nutrient of the body, faces different destinies in cancer cells. Glycolysis, oxidative phosphorylation, and pentose phosphate pathways produce different glucose-derived metabolites and thus affect cells' bioenergetics differently. Tumor cells' dependency to aerobic glycolysis and other cancer-specific metabolism changes are known as the cancer hallmarks, distinct cancer cells from normal cells. Therefore, these tumor-specific characteristics receive the limelight as targets for cancer therapy. Glutamine, serine, and fatty acid oxidation together with 5-lipoxygenase are main pathways that have attracted lots of attention for cancer therapy. In this review, we not only discuss different tumor metabolism aspects but also discuss the metabolism roles in the promotion of cancer cells at different stages and their difference with normal cells. Besides, we dissect the inhibitors potential in blocking the main metabolic pathways to introduce the effective and non-effective inhibitors in the field.
    Keywords:  Cancer metabolism; cancer therapy; glycolysis; oxidative phosphorylation; pentose phosphate pathway
    DOI:  https://doi.org/10.1177/1010428320965284
  13. Biochim Biophys Acta Mol Cell Biol Lipids. 2020 Sep 30. pii: S1388-1981(20)30215-8. [Epub ahead of print] 158823
    Sobczak AIS, Pitt SJ, Smith TK, Ajjan RA, Stewart AJ.
      Type-1 diabetes mellitus (T1DM) is associated with metabolic changes leading to alterations in glucose and lipid handling. While T1DM-associated effects on many major plasma lipids have been characterised, such effects on plasma free fatty acids (FFA) have not been fully examined. Using gas chromatography-mass spectrometry, we measured the plasma concentrations of FFA species in individuals with T1DM (n=44) and age/sex-matched healthy controls (n=44). Relationships between FFA species and various parameters were evaluated. Plasma concentrations of myristate (14:0), palmitoleate (16:1), palmitate (16:0), linoleate (18:2), oleate (18:1c9), cis-vaccenate (18:1c11), eicosapentaenoate (20:5), arachidonate (20:4) and docosahexanoate (22:6) were reduced in the T1DM group (p<0.0001 for all, except p=0.0020 for eicosapentaenoate and p=0.0068 for arachidonate); α-linolenate (18:3) and dihomo-γ-linolenate (20:3) concentrations were unchanged. Saturated/unsaturated FFA ratio, n-3/n-6 ratio, de novo lipogenesis index (palmitate (main lipogenesis product)/linoleate (only found in diet)) and elongase index (oleate/palmitoleate) were increased in the T1DM group (p=0.0166, p=0.0089, p<0.0001 and p=0.0008 respectively). The stearoyl-CoA desaturase 1 (SCD1) index 1 (palmitoleate/palmitate) and index 2 (oleate/stearate) were reduced in T1DM (p<0.0001 for all). The delta-(5)-desaturase (D5D) index (arachidonate/dihomo-γ-linolenate) was unchanged. Age and sex had no effect on plasma FFA concentrations in T1DM, while SCD1 index 1 was positively correlated (p=0.098) and elongase index negatively correlated with age (p=0.0363). HbA1c was negatively correlated with all plasma FFAs concentrations measured except α-linolenate and dihomo-γ-linolenate. Correlations were observed between plasma FFAs and cholesterol and HDL, but not LDL or diabetes duration. Collectively, these results aid our understanding of T1DM and its effects on lipid metabolism.
    Keywords:  Fatty acid metabolism; GC-MS; HbA1c; Lipid metabolism; Lipidomics; Non-esterified fatty acid
    DOI:  https://doi.org/10.1016/j.bbalip.2020.158823
  14. BMC Bioinformatics. 2020 Oct 07. 21(1): 439
    Wang S, Zhu H, Zhou H, Cheng J, Yang H.
      BACKGROUND: Mass spectrometry (MS) has become a promising analytical technique to acquire proteomics information for the characterization of biological samples. Nevertheless, most studies focus on the final proteins identified through a suite of algorithms by using partial MS spectra to compare with the sequence database, while the pattern recognition and classification of raw mass-spectrometric data remain unresolved.RESULTS: We developed an open-source and comprehensive platform, named MSpectraAI, for analyzing large-scale MS data through deep neural networks (DNNs); this system involves spectral-feature swath extraction, classification, and visualization. Moreover, this platform allows users to create their own DNN model by using Keras. To evaluate this tool, we collected the publicly available proteomics datasets of six tumor types (a total of 7,997,805 mass spectra) from the ProteomeXchange consortium and classified the samples based on the spectra profiling. The results suggest that MSpectraAI can distinguish different types of samples based on the fingerprint spectrum and achieve better prediction accuracy in MS1 level (average 0.967).
    CONCLUSION: This study deciphers proteome profiling of raw mass spectrometry data and broadens the promising application of the classification and prediction of proteomics data from multi-tumor samples using deep learning methods. MSpectraAI also shows a better performance compared to the other classical machine learning approaches.
    Keywords:  Deep neural networks; Feature swath extraction; Leave-one-out cross prediction strategy; Multi-tumor types; Proteome profiling; Raw mass spectrometry data
    DOI:  https://doi.org/10.1186/s12859-020-03783-0
  15. Biochim Biophys Acta Rev Cancer. 2020 Oct 05. pii: S0304-419X(20)30163-3. [Epub ahead of print] 188444
    Han X, Zhang WH, Wang WQ, Yu XJ, Liu L.
      Pancreatic cancer is highly lethal, and the most effective treatment is curative resection followed by chemotherapy. Unfortunately, chemoresistance is an extremely common occurrence, and novel treatment modalities, such as immunotherapy and molecular targeted therapy, have shown limited success in clinical practice. Pancreatic cancer is characterized by an abundant stromal compartment. Cancer-associated fibroblasts (CAFs) and the extracellular matrix they deposit account for a large portion of the pancreatic tumor stroma. CAFs interact directly and indirectly with pancreatic cancer cells and can compromise the effects of, and even promote tumorigenic responses to, various treatment approaches. To eliminate these adverse effects, CAFs depletion strategies were developed. Instead of the anticipated antitumor effects of CAFs depletion, more aggressive tumor phenotypes were occasionally observed. The failure of universal stromal depletion led to the investigation of CAFs heterogeneity that forms the foundation for stromal remodeling and normalization. This review analyzes the role of CAFs in therapeutic resistance of pancreatic cancer and discusses potential CAFs-targeting strategies basing on the diverse biological functions of CAFs, thus to improve the outcome of pancreatic cancer treatment.
    Keywords:  Cancer-associated fibroblasts; Depletion of CAFs; Normalization of CAFs; Pancreatic cancer; Therapeutic resistance
    DOI:  https://doi.org/10.1016/j.bbcan.2020.188444
  16. Biochem Biophys Res Commun. 2020 Oct 02. pii: S0006-291X(20)31861-1. [Epub ahead of print]
    Yamamoto J, Han Q, Inubushi S, Sugisawa N, Hamada K, Nishino H, Miyake K, Kumamoto T, Matsuyama R, Bouvet M, Endo I, Hoffman RM.
      Methionine addiction is a fundamental and general hallmark of cancer. Methionine addiction prevents cancer cells, but not normal cells from proliferation under methionine restriction (MR). Previous studies reported that MR altered the histone methylation levels in methionine-addicted cancer cells. However, no study has yet compared the status of histone methylation status, under MR, between cancer cells and normal cells. In the present study, we compared the histone methylation status between cancer cells and normal fibroblasts of H3K4me3 and H3K9me3, using recombinant methioninase (rMETase) to effect MR. Human lung and colon cancer cell lines and human normal foreskin fibroblasts were cultured in control medium or medium with rMETase. The viability of foreskin fibroblasts was approximately 10 times more resistant to rMETase than the cancer cells in vitro. Proliferation only of the cancer cells ceased under MR. The histone methylation status of H3K4me3 and H3K9me3 under MR was evaluated by immunoblotting. The levels of the H3K4me3 and H3K9me3 were strongly decreased by MR in the cancer cells. In contrast, the levels of H3K4me3 and H3K9me3 were not altered by MR in normal fibroblasts. The present results suggest that histone methylation status of H3K4me3 and H3K9me3 under MR was unstable in cancer cells but stable in normal cells and the instability of histone methylation status under MR may determine the high methionine dependency of cancer cells to survive and proliferate.
    Keywords:  Histone methylation; Methioninase; Methionine addiction; Methionine dependence; Methionine restriction
    DOI:  https://doi.org/10.1016/j.bbrc.2020.09.108
  17. Bioinformatics. 2020 Oct 07. pii: btaa856. [Epub ahead of print]
    Alvarez-Jarreta J, Rodrigues PRS, Fahy E, O'Connor A, Price A, Gaud C, Andrews S, Benton P, Siuzdak G, Hawksworth JI, Valdivia-Garcia M, Allen SM, O'Donnell VB.
      We present LipidFinder 2.0, incorporating four new modules that apply artefact filters, remove lipid and contaminant stacks, in-source fragments and salt clusters, and a new isotope deletion method which is significantly more sensitive than available open-access alternatives. We also incorporate a novel false discovery rate (FDR) method, utilizing a target-decoy strategy, which allows users to assess data quality. A renewed lipid profiling method is introduced which searches three different databases from LIPID MAPS and returns bulk lipid structures only, and a lipid category scatter plot with color blind friendly pallet. An API interface with XCMS Online is made available on LipidFinder's online version. We show using real data that LipidFinder 2.0 provides a significant improvement over non-lipid metabolite filtering and lipid profiling, compared to available tools.AVAILABILITY: LipidFinder 2.0 is freely available at https://github.com/ODonnell-Lipidomics/LipidFinder and http://lipidmaps.org/resources/tools/lipidfinder.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btaa856
  18. Mol Omics. 2020 Oct 09.
    Krasny L, Huang PH.
      Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.
    DOI:  https://doi.org/10.1039/d0mo00072h
  19. Metabolomics. 2020 Oct 07. 16(10): 107
    Wright Muelas M, Roberts I, Mughal F, O'Hagan S, Day PJ, Kell DB.
      INTRODUCTION: It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites.OBJECTIVES: Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to detect and identify metabolites present in serum, but to also establish a method capable of measure their uptake and secretion by different cell lines.
    METHODS: We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the 'exometabolome' or metabolic footprint).
    RESULTS: Our method measures some 4000-5000 metabolic features in both positive and negative electrospray ionisation modes. We show that the metabolic footprints of different cell lines differ greatly from each other.
    CONCLUSION: Our new, 15-min untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum. This will enable future research to study these differences in multiple cell lines that will relate this to transporter expression, thereby advancing our knowledge of transporter substrates, both natural and xenobiotic compounds.
    Keywords:  Cell culture; Human serum; LC-MS/MS; Orbitrap; Transporters; Untargeted metabolomics
    DOI:  https://doi.org/10.1007/s11306-020-01725-8
  20. Proteomics. 2020 Oct 04. e2000178
    Poschmann G, Brenig K, Lenz T, Stühler K.
      Secretome analysis is broadly applied to understand the interplay between cells and their microenvironment. In particular, the unbiased analysis by mass spectrometry-based proteomics of conditioned medium has been successfully applied. In this context, several approaches have been developed allowing to distinguish proteins actively secreted by cells from proteins derived from culture medium or proteins released from dying cells. Here, we evaluated three different methods comparing conditioned medium and lysate by quantitative mass spectrometry-based proteomics to identify bona fide secreted proteins. Evaluation in three different human cell lines revealed that all three methods gave access to a similar set of bona fide secreted proteins covering a broad abundance range. In the analyzed primary cells, i.e., Mesenchymal Stromal Cells and Normal Human Dermal Fibroblasts, more than 70% of the identified proteins were linked to classical secretion pathways. Furthermore, 4-12% were predicted to be released by unconventional secretion pathways. Interestingly, we found evidence of release by ectodomain shedding in a large number of the remaining candidate proteins. In summary, we are convinced that comparative secretomics is currently the method of choice to obtain high-confident secretome data and to identify novel candidates also for unconventional protein secretion which have been neglected so far. This article is protected by copyright. All rights reserved.
    Keywords:  ectodomain shedding; method comparison; quantitative mass spectrometry; secretome; unconventional protein secretion
    DOI:  https://doi.org/10.1002/pmic.202000178
  21. Sci Adv. 2020 Oct;pii: eabc7120. [Epub ahead of print]6(41):
    Guarecuco R, Williams RT, Baudrier L, La K, Passarelli MC, Ekizoglu N, Mestanoglu M, Alwaseem H, Rostandy B, Fidelin J, Garcia-Bermudez J, Molina H, Birsoy K.
      Tumor environment influences anticancer therapy response but which extracellular nutrients affect drug sensitivity is largely unknown. Using functional genomics, we determine modifiers of l-asparaginase (ASNase) response and identify thiamine pyrophosphate kinase 1 as a metabolic dependency under ASNase treatment. While thiamine is generally not limiting for cell proliferation, a DNA-barcode competition assay identifies leukemia cell lines that grow suboptimally under low thiamine and are characterized by low expression of solute carrier family 19 member 2 (SLC19A2), a thiamine transporter. SLC19A2 is necessary for optimal growth and ASNase resistance, when standard medium thiamine is lowered ~100-fold to human plasma concentrations. In addition, humanizing blood thiamine content of mice through diet sensitizes SLC19A2-low leukemia cells to ASNase in vivo. Together, our work reveals that thiamine utilization is a determinant of ASNase response for some cancer cells and that oversupplying vitamins may affect therapeutic response in leukemia.
    DOI:  https://doi.org/10.1126/sciadv.abc7120