bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2020‒12‒13
twenty-four papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh

  1. Cell Metab. 2020 Dec 01. pii: S1550-4131(20)30651-3. [Epub ahead of print]
      Neutrophils can function and survive in injured and infected tissues, where oxygen and metabolic substrates are limited. Using radioactive flux assays and LC-MS tracing with U-13C glucose, glutamine, and pyruvate, we observe that neutrophils require the generation of intracellular glycogen stores by gluconeogenesis and glycogenesis for effective survival and bacterial killing. These metabolic adaptations are dynamic, with net increases in glycogen stores observed following LPS challenge or altitude-induced hypoxia. Neutrophils from patients with chronic obstructive pulmonary disease have reduced glycogen cycling, resulting in impaired function. Metabolic specialization of neutrophils may therefore underpin disease pathology and allow selective therapeutic targeting.
    Keywords:  COPD; GYS1; gluconeogenesis; glycogen; glycogenesis; glycogenolysis; glycolysis; inflammation; neutrophil
  2. Proc Natl Acad Sci U S A. 2020 Dec 07. pii: 202006828. [Epub ahead of print]
      Ferroptosis is an iron-dependent regulated necrosis mediated by lipid peroxidation. Cancer cells survive under metabolic stress conditions by altering lipid metabolism, which may alter their sensitivity to ferroptosis. However, the association between lipid metabolism and ferroptosis is not completely understood. In this study, we found that the expression of elongation of very long-chain fatty acid protein 5 (ELOVL5) and fatty acid desaturase 1 (FADS1) is up-regulated in mesenchymal-type gastric cancer cells (GCs), leading to ferroptosis sensitization. In contrast, these enzymes are silenced by DNA methylation in intestinal-type GCs, rendering cells resistant to ferroptosis. Lipid profiling and isotope tracing analyses revealed that intestinal-type GCs are unable to generate arachidonic acid (AA) and adrenic acid (AdA) from linoleic acid. AA supplementation of intestinal-type GCs restores their sensitivity to ferroptosis. Based on these data, the polyunsaturated fatty acid (PUFA) biosynthesis pathway plays an essential role in ferroptosis; thus, this pathway potentially represents a marker for predicting the efficacy of ferroptosis-mediated cancer therapy.
    Keywords:  ELOVL5; FADS1; arachidonic acid; ferroptosis; lipid peroxidation
  3. Cell. 2020 Dec 07. pii: S0092-8674(20)31526-9. [Epub ahead of print]
      Obesity is a major cancer risk factor, but how differences in systemic metabolism change the tumor microenvironment (TME) and impact anti-tumor immunity is not understood. Here, we demonstrate that high-fat diet (HFD)-induced obesity impairs CD8+ T cell function in the murine TME, accelerating tumor growth. We generate a single-cell resolution atlas of cellular metabolism in the TME, detailing how it changes with diet-induced obesity. We find that tumor and CD8+ T cells display distinct metabolic adaptations to obesity. Tumor cells increase fat uptake with HFD, whereas tumor-infiltrating CD8+ T cells do not. These differential adaptations lead to altered fatty acid partitioning in HFD tumors, impairing CD8+ T cell infiltration and function. Blocking metabolic reprogramming by tumor cells in obese mice improves anti-tumor immunity. Analysis of human cancers reveals similar transcriptional changes in CD8+ T cell markers, suggesting interventions that exploit metabolism to improve cancer immunotherapy.
    Keywords:  CD8+ T cells; anti-tumor immunity; colorectal cancer; fat oxidation; metabolism; obesity; tumor microenvironment
  4. Clin Breast Cancer. 2020 Nov 11. pii: S1526-8209(20)30232-9. [Epub ahead of print]
      INTRODUCTION: Breast cancer (BC) is the most common cancer in women, with a high disease burden, especially in the advanced disease stages. Our study investigated the metabolomic profile of breast cancer patients' serum with the aim of identifying novel diagnostic biomarkers that could be used, especially for early disease detection.MATERIALS AND METHODS: Using targeted metabolomic serum profiling based on high-performance liquid chromatography mass spectrometry, women with BC (n = 39) and a control group (n = 21) were examined for 232 endogenous metabolites.
    RESULTS: The top performing biomarkers included acylcarnitines (ACs) and 9,12-linoleic acid. A combined panel of the top 4 biomarkers achieved 83% sensitivity and 81% specificity, with an area under the curve (AUC) of 0.839 (95% confidence interval, 0.811-0.867). Individual markers also provided significant predictive values: AC 12:0, sensitivity of 72%, specificity of 67%, and AUC of 0.71; AC 14:2, sensitivity of 74%, specificity of 71%, and AUC of 0.73; AC 14:0: sensitivity of 67%, specificity of 81%, and AUC of 0.73; and 9,12-linoleic acid, sensitivity of 69%, specificity of 67%, and AUC of 0.71. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination.
    CONCLUSION: Using mass spectrometry-targeted metabolomic profiling, ACs and 9,12-linoleic acid were identified as potential diagnostic biomarkers for breast cancer. Additionally, these identified metabolites could provide additional insight into cancer cell metabolism.
    Keywords:  9,12-linoleic acid; Acylcarnitines; Breast cancer; Mass spectrometry; Metabolomic profiling
  5. Talanta. 2021 Feb 01. pii: S0039-9140(20)31163-2. [Epub ahead of print]223(Pt 2): 121872
      Metabolic phenotyping using mass spectrometry (MS) is being applied to ever increasing sample numbers in clinical and epidemiology studies. High-throughput and robust methods are being developed for the accurate measurement of metabolites associated with disease. Traditionally, quantitative assays have utilized triple quadrupole (QQQ) MS based methods; however, the use of such focused methods removes the ability to perform discovery-based metabolic phenotyping. An integrated workflow for the hybrid simultaneous quantification of 34 biogenic amines in combination with full scan high-resolution accurate mass (HRAM) exploratory metabolic phenotyping is presented. Primary and secondary amines are derivatized with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate prior to revered-phase liquid chromatographic separation and mass spectrometric detection. Using the HRAM-MS data, retrospective phenotypic data mining could be performed, demonstrating the versatility of HRAM-MS instrumentation in a clinical and molecular epidemiological environment. Quantitative performance was assessed using two MS detector platforms: Waters TQ-XS (QQQ; n = 3) and Bruker Impact II QToF (HRAMS-MS; n = 2) and three human biofluids (plasma, serum and urine). Finally, each platform was assessed using a certified external reference sample (NIST SRM 1950 plasma). Intra- and inter-day accuracy and precision were comparable between the QQQ and QToF instruments (<15%), with excellent linearity (R2 > 0.99) over the quantification range of 1-400 μmol L-1. Quantitative values were comparable across all instruments for human plasma, serum and urine samples, and calculated concentrations were verified against certified reference values for NIST SRM 1950 plasma as an external reference. As a real-life biological exemplar, the method was applied to plasma samples obtained from SARS-CoV-2 positive patients versus healthy controls. Both the QQQ and QToF approaches were equivalent in being able to correctly classify SARS-CoV-2 positivity. Critically, the use of HRAM full scan data was also assessed for retrospective exploratory mining of data to extract additional biogenic amines of biomarker interest beyond the 34 quantified targets.
    Keywords:  Amino acids; Biogenic amines; COVID-19; Cross validation; High-resolution accurate mass; Metabolic profiling; Quantitative mass spectrometry; SARS-CoV-2; Triple quadrupole; UHPLC
  6. Proc Natl Acad Sci U S A. 2020 Dec 07. pii: 202020197. [Epub ahead of print]
      The yeast Saccharomyces cerevisiae is a powerful model system for systems-wide biology screens and large-scale proteomics methods. Nearly complete proteomics coverage has been achieved owing to advances in mass spectrometry. However, it remains challenging to scale this technology for rapid and high-throughput analysis of the yeast proteome to investigate biological pathways on a global scale. Here we describe a systems biology workflow employing plate-based sample preparation and rapid, single-run, data-independent mass spectrometry analysis (DIA). Our approach is straightforward, easy to implement, and enables quantitative profiling and comparisons of hundreds of nearly complete yeast proteomes in only a few days. We evaluate its capability by characterizing changes in the yeast proteome in response to environmental perturbations, identifying distinct responses to each of them and providing a comprehensive resource of these responses. Apart from rapidly recapitulating previously observed responses, we characterized carbon source-dependent regulation of the GID E3 ligase, an important regulator of cellular metabolism during the switch between gluconeogenic and glycolytic growth conditions. This unveiled regulatory targets of the GID ligase during a metabolic switch. Our comprehensive yeast system readout pinpointed effects of a single deletion or point mutation in the GID complex on the global proteome, allowing the identification and validation of targets of the GID E3 ligase. Moreover, this approach allowed the identification of targets from multiple cellular pathways that display distinct patterns of regulation. Although developed in yeast, rapid whole-proteome-based readouts can serve as comprehensive systems-level assays in all cellular systems.
    Keywords:  GID E3 ligase; mass spectrometry; proteomics; stress; yeast systems biology
  7. Methods Mol Biol. 2021 ;2130 157-168
      Lipidomics approaches provide quantitative characterization of hundreds of lipid species from biological samples. Recent studies highlight the value of these methods in studying circadian biology, and their potential goes far beyond studying lipid metabolism per se. For example, lipidomics analyses of subcellular compartments can be used to determine daily rhythmicity of different organelles and their intracellular dynamics. In this chapter we describe in detail the procedure for around the clock shotgun lipidomics, from sample preparation to bioinformatics analyses. Sample preparation includes biochemical fractionation of nuclei and mitochondria from mouse liver harvested throughout the day. Lipid content is determined and quantified, in unbiased manner and with wide coverage, using multidimensional mass spectrometry shotgun lipidomics (MDMS-SL). Circadian parameters are then determined with nonparametric statistical tests. Overall, the approach described herein is applicable for various animal models, tissues, and organelles, and is expected to yield new insight on various aspects of circadian biology and lipid metabolism.
    Keywords:  Circadian; Lipid metabolism; Liver; Mass spectrometry; Mitochondria; Mouse; Nuclei; Shotgun lipidomics
  8. Methods Mol Biol. 2021 ;2130 185-193
      Recent advances in mass spectrometry (MS)-based quantitative proteomics now allow the identification and quantification of deep proteomes and post-translational modifications (PTMs) in relatively short times. Therefore, in the last few years, this technology has proven successful in the circadian field to characterize temporal oscillations of the proteome and more recently PTMs in cellular systems and in tissues. In this chapter, we describe a robust and simple protocol, based on the EasyPhos workflow, to enable preparation of large number of proteomes and phosphoproteomes from mouse tissues for MS-based quantitative analysis. We additionally discuss computational methods to analyze proteome and phosphoproteome time series to determine circadian oscillations.
    Keywords:  Circadian; Mass spectrometry; Phosphoproteomics; Proteomics
  9. Cells. 2020 Dec 08. pii: E2635. [Epub ahead of print]9(12):
      The interest in fructose metabolism is based on the observation that an increased dietary fructose consumption leads to an increased risk of obesity and metabolic syndrome. In particular, obesity is a known risk factor to develop many types of cancer and there is clinical and experimental evidence that an increased fructose intake promotes cancer growth. The precise mechanism, however, in which fructose induces tumor growth is still not fully understood. In this article, we present an overview of the metabolic pathways that utilize fructose and how fructose metabolism can sustain cancer cell proliferation. Although the degradation of fructose shares many of the enzymes and metabolic intermediates with glucose metabolism through glycolysis, glucose and fructose are metabolized differently. We describe the different metabolic fates of fructose carbons and how they are connected to lipogenesis and nucleotide synthesis. In addition, we discuss how the endogenous production of fructose from glucose via the polyol pathway can be beneficial for cancer cells.
    Keywords:  AKR1B1; HFCS; KHK; SORD; cancer metabolism; fructose metabolism; lipogenesis; pentose phosphate pathway; polyol pathway
  10. Int J Cancer. 2020 Dec 07.
      Here we sought metabolic alterations specifically associated with MYCN amplification as nodes to indirectly target the MYCN oncogene. Liquid chromatography-mass spectrometry-based proteomics identified 7 proteins consistently correlated with MYCN in proteomes from 49 neuroblastoma biopsies and 13 cell lines. Among these was phosphoglycerate dehydrogenase (PHGDH), the rate-limiting enzyme in de novo serine synthesis. MYCN associated with two regions in the PHGDH promoter, supporting transcriptional PHGDH regulation by MYCN. Pulsed stable isotope-resolved metabolomics utilizing 13 C-glucose labeling demonstrated higher de novo serine synthesis in MYCN-amplified cells compared to cells with diploid MYCN. An independence of MYCN-amplified cells from exogenous serine and glycine was demonstrated by serine and glycine starvation, which attenuated nucleotide pools and proliferation only in cells with diploid MYCN but did not diminish these endpoints in MYCN-amplified cells. Proliferation was attenuated in MYCN-amplified cells by CRISPR/Cas9-mediated PHGDH knockout or treatment with PHGDH small molecule inhibitors without affecting cell viability. PHGDH inhibitors administered as single-agent therapy to NOG mice harboring patient-derived MYCN-amplified neuroblastoma xenografts slowed tumor growth. However, combining a PHGDH inhibitor with the standard-of-care chemotherapy drug, cisplatin, revealed antagonism of chemotherapy efficacy in vivo. Emergence of chemotherapy resistance was confirmed in the genetic PHGDH knockout model in vitro. Altogether, PHGDH knockout or inhibition by small molecules consistently slows proliferation, but stops short of killing the cells, which then establish resistance to classical chemotherapy. Although PHGDH inhibition with small molecules has produced encouraging results in other preclinical cancer models, this approach has limited attractiveness for patients with neuroblastoma.
    Keywords:  cancer cell metabolism; cell death; de novo serine synthesis pathway; one-carbon metabolism; therapy resistance
  11. Nat Methods. 2020 Dec 07.
      Single-cell proteomics by mass spectrometry (SCoPE-MS) is a recently introduced method to quantify multiplexed single-cell proteomes. While this technique has generated great excitement, the underlying technologies (isobaric labeling and mass spectrometry (MS)) have technical limitations with the potential to affect data quality and biological interpretation. These limitations are particularly relevant when a carrier proteome, a sample added at 25-500× the amount of a single-cell proteome, is used to enable peptide identifications. Here we perform controlled experiments with increasing carrier proteome amounts and evaluate quantitative accuracy, as it relates to mass analyzer dynamic range, multiplexing level and number of ions sampled. We demonstrate that an increase in carrier proteome level requires a concomitant increase in the number of ions sampled to maintain quantitative accuracy. Lastly, we introduce Single-Cell Proteomics Companion (SCPCompanion), a software tool that enables rapid evaluation of single-cell proteomic data and recommends instrument and data analysis parameters for improved data quality.
  12. J Pharm Biomed Anal. 2020 Nov 20. pii: S0731-7085(20)31649-6. [Epub ahead of print] 113763
      Arachidonic acid (AA) is closely associated with breast cancer. In addition to the two metabolic pathways regulated by cyclooxygenase and lipoxygenase, AA has a third metabolic pathway through which cytochrome P450 (CYP) enzymes produce hydroxyeicosatetraenoic acids (HETEs) and epoxyeicosatrienoic acids (EETs). The targeted CYP-mediated pathway of AA can not only kill cancer cells but also inhibit the interstitial microenvironment around a tumor. Therefore, it makes sense to identify potential biomarkers from the AA metabolome for the diagnosis and treatment of breast cancer. This study established a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the analysis of AA and its main metabolites, EETs and HETEs, in MMTV-PyMT mice, a spontaneous breast cancer mouse model. The results showed that there were significant differences in the concentrations of AA, 12-HETE, 19-HETE and 8,9-EET in plasma and tumor tissues between normal and MMTV-PyMT mice. Therefore, the eicosanoids mentioned above may be used as new biomarkers for breast cancer diagnosis. This study provides a new perspective for the recognition and diagnosis of breast cancer.
    Keywords:  Arachidonic acid (AA); Biomarker; Breast cancer; Cytochrome P450; LC–MS/MS
  13. Mol Omics. 2020 Dec 09.
      Triple-negative breast cancer (TNBC) is well-known for its metastatic aggressiveness and poor survival prognosis, accounting for nearly a quarter of cases in breast cancer. We performed intra- and extra-cellular profiling of 40 amino acids and derivatives on three cell lines and their culture media, including TNBC, non-TNBC and normal breast epithelial cells, using HILIC-MS/MS. Characteristic metabolic alteration of amino acids and derivatives was observed in TNBC cells, compared to non-TNBC cells, especially in correlated intra- and extra-cellular metabolic pathways. Intra-cellularly, quantified glutamic acid, β-alanine, aspartic acid, glutathione, N-acetyl-serine and N-acetyl-methionine were most significantly increased (>2-fold, p < 0.01 and VIP > 1) in TNBC cells. Extra-cellularly, significantly increased uptake of glutamine, serine, β-alanine, and lysine and elevated excretion of glutamic acid and l-cysteine-glutathione (p < 0.01 and VIP > 1) were observed by TNBC cells from or to their cell culture media. This study depicted a novel dynamic portrayal of metabolic dysregulation between TNBC and non-TNBC cells, correlated in both intra- and extra-cellular amino acid profiles. Quantification of these distinctive metabolites of TNBC cells might offer advanced understanding and new treatment targets for TNBC.
  14. Nano Lett. 2020 Dec 10.
      Reprogrammed glucose metabolism is vital for cancer cells, but aspartate, an intermediate metabolic product, is the limiting factor for cancer cell proliferation. However, due to the complexity of metabolic pathways, it remains unclear whether glucose is the primary source of endogenous aspartate. Here, we report the design of an innovative molecular deactivator, based on a multifunctional upconversion nanoprobe, to explore the link between glucose and aspartate. This molecular deactivator mainly works in the acidic, hypoxic tumor microenvironment and deactivates multiple types of glucose transporters on cancer cell membranes upon illumination at 980 nm. Cancer cell proliferation in vivo is strongly inhibited by blocking glucose transporters. Our experimental data confirm that the cellular synthesis of aspartate for tumor growth is glucose-dependent. This work also demonstrates the untapped potential of molecularly engineered upconversion nanoprobes for discovering hidden metabolic pathways and improving therapeutic efficacy of conventional antitumor drugs.
    Keywords:  Upconversion nanoprobe; cell metabolism; glucose transporter; glucose-derived aspartate; protein deactivation
  15. Cells. 2020 Dec 04. pii: E2598. [Epub ahead of print]9(12):
      Hypoxia is a condition commonly observed in the core of solid tumors. The hypoxia-inducible factors (HIF) act as hypoxia sensors that orchestrate a coordinated response increasing the pro-survival and pro-invasive phenotype of cancer cells, and determine a broad metabolic rewiring. These events favor tumor progression and chemoresistance. The increase in glucose and amino acid uptake, glycolytic flux, and lactate production; the alterations in glutamine metabolism, tricarboxylic acid cycle, and oxidative phosphorylation; the high levels of mitochondrial reactive oxygen species; the modulation of both fatty acid synthesis and oxidation are hallmarks of the metabolic rewiring induced by hypoxia. This review discusses how metabolic-dependent factors (e.g., increased acidification of tumor microenvironment coupled with intracellular alkalinization, and reduced mitochondrial metabolism), and metabolic-independent factors (e.g., increased expression of drug efflux transporters, stemness maintenance, and epithelial-mesenchymal transition) cooperate in determining chemoresistance in hypoxia. Specific metabolic modifiers, however, can reverse the metabolic phenotype of hypoxic tumor areas that are more chemoresistant into the phenotype typical of chemosensitive cells. We propose these metabolic modifiers, able to reverse the hypoxia-induced metabolic rewiring, as potential chemosensitizer agents against hypoxic and refractory tumor cells.
    Keywords:  cancer; chemoresistance; hypoxia; metabolic reprogramming
  16. Talanta. 2021 Feb 01. pii: S0039-9140(20)31031-6. [Epub ahead of print]223(Pt 2): 121740
      Formalin-fixed paraffin-embedded (FFPE) tissues play an irreplaceable role in cancer research. Although extensive research has been conducted for the detection of DNA, RNA and proteins in FFPE samples, literature dealing with the FFPE determination of small molecules is scarce. In this study, we aimed to explore the potential of targeted metabolomics in FFPE specimens. For that purpose, we developed a LC-MS/MS method for the quantification of acidic metabolites in FFPE samples. The method involves trimming tissue slices from FFPE blocks, deparaffinization, lysis of the tissue, o-benzyl hydroxylamine derivatization and LC-MS/MS detection. Deparaffinization and lysis steps were optimized to maximize the analytes extraction and to minimize the effect of the ubiquitous presence of some metabolites in the paraffin. Two validation approaches were applied: (i) using blank paraffin as matrix and (ii) using actual human FFPE tissue samples by standard additions. The method quantified 40 metabolites with appropriate accuracy (commonly 80-120%) and precision (CV 2-19%) in both validation approaches. LLOQs ranging 0.88-2001 pg mg-1 with low-moderate matrix effects (commonly 85-115%) were obtained. FFPE samples from 15 patients with colorectal cancer were analyzed and metabolites concentrations in tumor vs matched normal FFPE tissues were compared. Results show that tumor tissues have a well-established fingerprint including an increase in ketogenesis, a decrease in lipogenesis and an imbalance in the tricarboxylic acid cycle.
    Keywords:  Cancer; Carboxylic acids; Formalin-fixed paraffin-embedded; Liquid chromatography-tandem mass spectrometry; Mass spectrometry; Targeted metabolomics
  17. Elife. 2020 12 07. pii: e58783. [Epub ahead of print]9
      Improvements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to understanding the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without knowledge of the types of modifications involved. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased FLEXIQuant-LF approach to assess the modification extent in quantitative proteomics data provides a better understanding of the function and regulation of PTMs. The software is available at
    Keywords:  PTM identification; PTM quantification; biochemistry; chemical biology; computational biology; human; label-free quantification; mass spectrometry; modification stoichiometry; peptide modification; systems biology
  18. Cell Rep. 2020 Dec 08. pii: S2211-1247(20)31462-5. [Epub ahead of print]33(10): 108473
      A role for cancer cell epithelial-to-mesenchymal transition (EMT) in cancer is well established. Here, we show that, in addition to cancer cell EMT, ovarian cancer cell metastasis relies on an epigenomic mesenchymal-to-epithelial transition (MET) in host mesenchymal stem cells (MSCs). These reprogrammed MSCs, termed carcinoma-associated MSCs (CA-MSCs), acquire pro-tumorigenic functions and directly bind cancer cells to serve as a metastatic driver/chaperone. Cancer cells induce this epigenomic MET characterized by enhancer-enriched DNA hypermethylation, altered chromatin accessibility, and differential histone modifications. This phenomenon appears clinically relevant, as CA-MSC MET is highly correlated with patient survival. Mechanistically, mirroring MET observed in development, MET in CA-MSCs is mediated by WT1 and EZH2. Importantly, EZH2 inhibitors, which are clinically available, significantly inhibited CA-MSC-mediated metastasis in mouse models of ovarian cancer.
    Keywords:  EZH2; WT1; carcinoma-associated mesenchymal stem cells; epigenomic reprogramming; mesenchymal-to-epithelial transition; metastasis; ovarian cancer; tumor microenvironment
  19. Clin Gastroenterol Hepatol. 2020 Dec 03. pii: S1542-3565(20)31635-9. [Epub ahead of print]
      BACKGROUND & AIMS: Colorectal cancer risk can be lowered by adherence to the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) guidelines. We derived metabolic signatures of adherence to these guidelines and tested their associations with colorectal cancer risk in the European Prospective Investigation into Cancer (EPIC) cohort.METHODS: Scores reflecting adherence to the WCRF/AICR recommendations (scale 1-5) were calculated from participant data on weight maintenance, physical activity, diet, and alcohol among a discovery set of 5,738 cancer-free EPIC participants with metabolomics data. Partial least squares regression was used to derive fatty acid and endogenous metabolite signatures of WCRF/AICR score in this group. In an independent set of 1,608 colorectal cancer cases and matched controls, odds ratios (OR) and 95% confidence intervals (CI) were calculated for colorectal cancer risk per unit increase in WCRF/AICR score and per the corresponding change in metabolic signatures using multivariable conditional logistic regression.
    RESULTS: Higher WCRF/AICR scores were characterized by metabolic signatures of elevated odd-chain fatty acids, serine, glycine and specific phosphatidylcholines. Signatures were more strongly inversely associated with colorectal cancer risk (fatty acids: OR 0.51 per unit increase, 95% CI 0.29-0.90; endogenous metabolites: OR 0.62 per unit change, 95% CI 0.50-0.78) than the WCRF/AICR score (OR 0.93 per unit change, 95% CI 0.86-1.00) overall. Signature associations were stronger in male compared to female participants.
    CONCLUSIONS: Metabolite profiles reflecting adherence to WCRF/AICR guidelines and additional lifestyle or biological risk factors were associated with colorectal cancer. Measuring a specific panel of metabolites representative of healthy or unhealthy lifestyle may identify strata of the population at higher risk of colorectal cancer.
    Keywords:  Colorectal neoplasm; World Cancer Research Fund/American Institute for Cancer Research recommendations; risk factors; targeted metabolomics
  20. Clin Proteomics. 2020 Nov 02. 17(1): 39
      BACKGROUND: Remote ischaemic conditioning (RIC) is currently being explored as a non-invasive method to attenuate ischaemia/reperfusion injuries in organs. A randomised clinical study (CONTEXT) evaluated the effects of RIC compared to non-RIC controls in human kidney transplants.METHODS: RIC was induced prior to kidney reperfusion by episodes of obstruction to arterial flow in the leg opposite the transplant using a tourniquet (4 × 5 min). Although RIC did not lead to clinical improvement of transplant outcomes, we explored whether RIC induced molecular changes through precision analysis of CONTEXT recipient plasma and kidney tissue samples by high-resolution tandem mass spectrometry (MS/MS).
    RESULTS: We observed an accumulation of muscle derived proteins and altered amino acid metabolism in kidney tissue proteomes, likely provoked by RIC, which was not reflected in plasma. In addition, MS/MS analysis demonstrated transient upregulation of several acute phase response proteins (SAA1, SAA2, CRP) in plasma, 1 and 5 days post-transplant in RIC and non-RIC conditions with a variable effect on the magnitude of acute inflammation.
    CONCLUSIONS: Together, our results indicate sub-clinical systemic and organ-localised effects of RIC.
    Keywords:  Acute phase proteins; CONTEXT clinical trial; ELISA; Kidney transplantation; Mass spectrometry; Proteomics; Remote ischaemic conditioning
  21. Trends Cancer. 2020 Dec 03. pii: S2405-8033(20)30278-8. [Epub ahead of print]
      Lipid metabolic reprogramming is an established trait of cancer metabolism that guides response and resistance to antitumoral therapies. Enhanced lipogenesis, increased lipid content (either free or stored into lipid droplets), and lipid-dependent catabolism sustain therapy desensitization and the emergence of a resistant phenotype of tumor cells exposed to chemotherapy or targeted therapies. Aberrant lipid metabolism, therefore, has emerged as a potential metabolic vulnerability of therapy-resistant cancers that could be exploited for therapeutic interventions or for identifying tumors more likely to respond to further lines of therapies. This review gathers recent findings on the role of aberrant lipid metabolism in influencing antitumoral therapy response and in sustaining the emergence of resistance.
    Keywords:  lipid droplets; lipid metabolism; metabolic reprogramming; metabolic targeting; therapy resistance
  22. Methods Mol Biol. 2021 ;2130 169-183
      Lipidomics has been defined as the large-scale analysis of lipids in organelles, cells, tissues, or whole organisms. Including the temporal aspects of lipid metabolic changes into this analysis allows to access yet another important aspect of lipid regulation. The resulting methodology, circadian lipidomics, has thus emerged as a novel tool to address the enormous complexity, which is present among cellular lipids. Here, we describe how mass spectrometry-based circadian lipidomics can be applied to study the impact of peripheral clocks on lipid metabolism in human primary cells and tissues, exemplified by studies in human pancreatic islets and skeletal myotubes.
    Keywords:  Circadian clock; Human pancreatic islets; Human primary myotubes; Human skeletal muscle; Lipid metabolism; Lipidomics
  23. Cell Rep Med. 2020 Nov 17. 1(8): 100138
      Increasing evidence suggests Alzheimer's disease (AD) pathophysiology is influenced by primary and secondary bile acids, the end product of cholesterol metabolism. We analyze 2,114 post-mortem brain transcriptomes and identify genes in the alternative bile acid synthesis pathway to be expressed in the brain. A targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals supports these results. Our metabolic network analysis suggests that taurine transport, bile acid synthesis, and cholesterol metabolism differ in AD and cognitively normal individuals. We also identify putative transcription factors regulating metabolic genes and influencing altered metabolism in AD. Intriguingly, some bile acids measured in brain tissue cannot be explained by the presence of enzymes responsible for their synthesis, suggesting that they may originate from the gut microbiome and are transported to the brain. These findings motivate further research into bile acid metabolism in AD to elucidate their possible connection to cognitive decline.
    Keywords:  Alzheimer's disease; bile acids; cholesterol metabolism; genome-scale metabolic models; metabolomics; transcriptional regulatory networks; transcriptomics
  24. J Proteomics. 2020 Dec 08. pii: S1874-3919(20)30438-3. [Epub ahead of print] 104070
      Spectral similarity calculation is widely used in protein identification tools and mass spectra clustering algorithms while comparing theoretical or experimental spectra. The performance of the spectral similarity calculation plays an important role in these tools and algorithms especially in the analysis of large-scale datasets. Recently, deep learning methods have been proposed to improve the performance of clustering algorithms and protein identification by training the algorithms with existing data and the use of multiple spectra and identified peptide features. While the efficiency of these algorithms is still under study in comparison with traditional approaches, their application in proteomics data analysis is becoming more common. Here, we propose the use of deep learning to improve spectral similarity comparison. We assessed the performance of deep learning for spectral similarity, with GLEAMS and a newly trained embedder model (DLEAMSE), which uses high-quality spectra from PRIDE Cluster. Also, we developed a new bioinformatics tool (mslookup - that allows users to quickly search for spectra in previously identified mass spectra publish in public repositories and spectral libraries. Finally, we released a human database to enable bioinformaticians and biologists to search for identified spectra in their machines. SIGNIFICANCE STATEMENT: Spectral similarity calculation plays an important role in proteomics data analysis. With deep learning's ability to learn the implicit and effective features from large-scale training datasets, deep learning-based MS/MS spectra embedding models has emerged as a solution to improve mass spectral clustering similarity calculation algorithms. We compare multiple similarity scoring and deep learning methods in terms of accuracy (compute the similarity for a pair of the mass spectrum) and computing-time performance. The benchmark results showed no major differences in accuracy between DLEAMSE and normalized dot product for spectrum similarity calculations. The DLEAMSE GPU implementation is faster than NDP in preprocessing on the GPU server and the similarity calculation of DLEAMSE (Euclidean distance on 32-D vectors) takes about 1/3 of dot product calculations. The deep learning model (DLEAMSE) encoding and embedding steps needed to run once for each spectrum and the embedded 32-D points can be persisted in the repository for future comparison, which is faster for future comparisons and large-scale data. Based on these, we proposed a new tool mslookup that enables the researcher to find spectra previously identified in public data. The tool can be also used to generate in-house databases of previously identified spectra to share with other laboratories and consortiums.
    Keywords:  Deep learning; Mass spectra embedder; Scoring function; Spectral similarity