bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2021‒02‒14
twenty-six papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh

  1. Anal Chim Acta. 2021 Mar 08. pii: S0003-2670(21)00030-1. [Epub ahead of print]1149 338210
      When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is common to detect thousands of features from a biological extract. Although it is impractical to collect non-chimeric MS/MS data for each in a single chromatographic run, this is generally unnecessary because most features do not correspond to unique metabolites of biological relevance. Here we show that relatively simple data-processing strategies that can be applied on the fly during acquisition of data with an Orbitrap ID-X, such as blank subtraction and well-established adduct or isotope calculations, decrease the number of features to target for MS/MS analysis by up to an order of magnitude for various types of biological matrices. We demonstrate that annotating these non-biological contaminants and redundancies in real time during data acquisition enables comprehensive MS/MS data to be acquired on each remaining feature at a single collision energy. To ensure that an appropriate collision energy is applied, we introduce a method using a series of hidden ion-trap scans in an Orbitrap ID-X to find an optimal value for each feature that can then be applied in a subsequent high-resolution Orbitrap scan. Data from 100 metabolite standards indicate that this real-time optimization of collision energies leads to more informative MS/MS patterns compared to using a single fixed collision energy alone. As a benchmark to evaluate the overall workflow, we manually annotated unique biological features by independently subjecting E. coli samples to a credentialing analysis. While credentialing led to a more rigorous reduction in feature number, on-the-fly annotation with blank subtraction on an Orbitrap ID-X did not inappropriately discard unique biological metabolites. Taken together, our results reveal that optimal fragmentation data can be obtained in a single LC/MS/MS run for >90% of the unique biological metabolites in a sample when features are annotated during acquisition and collision energies are selected by using parallel mass spectrometry detection.
    Keywords:  Credentialing; Liquid chromatography; Mass spectrometry; Metabolite identification; Untargeted metabolomics
  2. J Lipid Res. 2020 Jun;pii: S0022-2275(20)43601-6. [Epub ahead of print]61(6): 911-932
      Atherogenic LDL particles are physicochemically and metabolically heterogeneous. Can bioactive lipid cargo differentiate LDL subclasses, and thus potential atherogenicity? What is the effect of statin treatment? Obese hypertriglyceridemic hypercholesterolemic males [n = 12; lipoprotein (a) <10 mg/dl] received pitavastatin calcium (4 mg/day) for 180 days in a single-phase unblinded study. The lipidomic profiles (23 lipid classes) of five LDL subclasses fractionated from baseline and post-statin plasmas were determined by LC-MS. At baseline and on statin treatment, very small dense LDL (LDL5) was preferentially enriched (up to 3-fold) in specific lysophospholipids {LPC, lysophosphatidylinositol (LPI), lysoalkylphosphatidylcholine [LPC(O)]; 9, 0.2, and 0.14 mol per mole of apoB, respectively; all P < 0.001 vs. LDL1-4}, suggesting elevated inflammatory potential per particle. In contrast, lysophosphatidylethanolamine was uniformly distributed among LDL subclasses. Statin treatment markedly reduced absolute plasma concentrations of all LDL subclasses (up to 33.5%), including LPC, LPI, and LPC(O) contents (up to -52%), consistent with reduction in cardiovascular risk. Despite such reductions, lipotoxic ceramide load per particle in LDL1-5 (1.5-3 mol per mole of apoB; 3-7 mmol per mole of PC) was either conserved or elevated. Bioactive lipids may constitute biomarkers for the cardiometabolic risk associated with specific LDL subclasses in atherogenic dyslipidemia at baseline, and with residual risk on statin therapy.
    Keywords:  ceramides; isopycnic density gradient ultracentrifugation; lipoprotein-associated phospholipase A2; liquid chromatography electrospray ionization-tandem mass spectrometry; low density lipoprotein; low density lipoprotein subclass heterogeneity; lysophosphatidylcholine; metabolic syndrome; pitavastatin calcium
  3. F1000Res. 2021 ;10 4
      Lipidomics increasingly describes the quantitation using mass spectrometry of all lipids present in a biological sample.  As the power of lipidomics protocols increase, thousands of lipid molecular species from multiple categories can now be profiled in a single experiment.  Observed changes due to biological differences often encompass large numbers of structurally-related lipids, with these being regulated by enzymes from well-known metabolic pathways.  As lipidomics datasets increase in complexity, the interpretation of their results becomes more challenging.  BioPAN addresses this by enabling the researcher to visualise quantitative lipidomics data in the context of known biosynthetic pathways.  BioPAN provides a list of genes, which could be involved in the activation or suppression of enzymes catalysing lipid metabolism in mammalian tissues.
    Keywords:  Biosynthetic pathway analysis; LIPID MAPS; Lipidomics; lipid profiling.; lipids
  4. Int J Mol Sci. 2021 Feb 08. pii: 1701. [Epub ahead of print]22(4):
      The significance of glutamine in cancer metabolism has been extensively studied. Cancer cells consume an excessive amount of glutamine to facilitate rapid proliferation. Thus, glutamine depletion occurs in various cancer types, especially in poorly vascularized cancers. This makes glutamine synthetase (GS), the only enzyme responsible for de novo synthesizing glutamine, essential in cancer metabolism. In cancer, GS exhibits pro-tumoral features by synthesizing glutamine, supporting nucleotide synthesis. Furthermore, GS is highly expressed in the tumor microenvironment (TME) and provides glutamine to cancer cells, allowing cancer cells to maintain sufficient glutamine level for glutamine catabolism. Glutamine catabolism, the opposite reaction of glutamine synthesis by GS, is well known for supporting cancer cell proliferation via contributing biosynthesis of various essential molecules and energy production. Either glutamine anabolism or catabolism has a critical function in cancer metabolism depending on the complex nature and microenvironment of cancers. In this review, we focus on the role of GS in a variety of cancer types and microenvironments and highlight the mechanism of GS at the transcriptional and post-translational levels. Lastly, we discuss the therapeutic implications of targeting GS in cancer.
    Keywords:  anticancer effect; cancer metabolism; glutamine metabolism; glutamine synthetase
  5. Steroids. 2021 Feb 05. pii: S0039-128X(21)00012-X. [Epub ahead of print]167 108800
      BACKGROUND: Steroid hormones are essential signalling molecules in prostate cancer (PC). However, many studies focusing on liquid biomarkers fail to take the hormonal status of these patients into account. Steroid measurements are sensitive to bias caused by matrix effects, thus assessing potential matrix effects is an important step in combining circulating tumour DNA (ctDNA) analysis with hormone status.METHODS: We investigated the accuracy of multi-steroid hormone profiling in mechanically-separated plasma (MSP) samples and in plasma from CellSave Preservative (CS) tubes, that are typically used to obtain ctDNA, compared to measurements in serum. We performed multiplex steroid profiling by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in samples obtained from ten healthy controls and ten castration-resistant prostate cancer (CRPC) patients.
    RESULTS: Steroid measurements were comparable between MSP and serum. A small but consistent decrease of 8-21% compared to serum was observed when using CS plasma, which was considered to be within the acceptable margin. The minimal residual testosterone levels of CRPC patients could be sensitively quantified in both MSP and CS samples.
    CONCLUSIONS: We validated the use of MSP and CS samples for multi-steroid profiling by LC-MS/MS. The optimised use of these samples in clinical trials will allow us to gain further insight into the steroid metabolism in PC patients.
    Keywords:  Androgens; CellSave; LC–MS/MS; Steroids; Testosterone
  6. Chem Phys Lipids. 2021 Feb 06. pii: S0009-3084(21)00001-3. [Epub ahead of print] 105048
      Small molecules, including metabolites and lipids, provide information on metabolic pathways and active biological processes in living organisms. They are often diagnostic of disease. Current exploratory methods for metabolomics and lipidomics mostly rely on separation using liquid or gas chromatography (LC or GC) coupled with mass spectrometers capable of acquiring high resolution data to generate an enormous data, but at the cost of lengthy processing and data acquisition. Even though many molecules can be identified and quantified by these methods, the laborious protocols for purification, identification, and validation limit the accessible sample chemical information. To improve the speed and efficiency of exploratory metabolomics and lipidomics, multiple reaction monitoring profiling (MRM profiling) has been developed. This strategy involves a three-stage workflow which starts by considering the metabolome as a collection of functional groups. The Discovery Stage interrogates a representative sample mixture for functional groups using the functional group specific precursor ion (Prec) scans and neutral loss (NL) scans. This experiment usually uses a triple quadrupole mass spectrometer without chromatography, i.e. by direct sample infusion. In the second Screening Stage, the main features seen in the Prec and NL scans are organized into lists of precursor ion/product ion transitions (MRMs) which are then used for the fast, specific, and sensitive interrogation of each individual sample. Data analysis by univariate and multivariate statistical methods is used to identify the most informative MRMs and so classify the individual samples. The compounds (biomarkers) which are responsible for the most informative MRMs in particular sample classes can be investigated in an optional third Identification Stage i.e. in a structural identification study. MRM profiling benefits from the much smaller number of functional groups compared to the number of individual metabolites existing in biological samples (where most metabolites are still unknown), resulting in acquisition of a much smaller data set and a shorter analysis time. The application of MRM Profiling to several biological and clinical problems is used to illustrate its features.
    Keywords:  Lipid profiling; Parkinson’s disease; exploratory lipidomics, oocytes; functional group profiling; microorganisms; tandem mass spectrometry
  7. Cell Rep. 2021 Feb 09. pii: S2211-1247(21)00051-6. [Epub ahead of print]34(6): 108738
      Canonical fatty acid metabolism describes specific enzyme-substrate interactions that result in products with well-defined chain lengths, degree(s), and positions of unsaturation. Deep profiling of lipids across a range of prostate cancer cell lines reveals a variety of fatty acids with unusual site(s) of unsaturation that are not described by canonical pathways. The structure and abundance of these unusual lipids correlate with changes in desaturase expression and are strong indicators of cellular phenotype. Gene silencing and stable isotope tracing demonstrate that direct Δ6 and Δ8 desaturation of 14:0 (myristic), 16:0 (palmitic), and 18:0 (stearic) acids by FADS2 generate new families of unsaturated fatty acids (including n-8, n-10, and n-12) that have rarely-if ever-been reported in human-derived cells. Isomer-resolved lipidomics reveals the selective incorporation of these unusual fatty acids into complex structural lipids and identifies their presence in cancer tissues, indicating functional roles in membrane structure and signaling.
    Keywords:  desaturase; elongase; fatty acids; imaging; isomer; lipids; mass spectrometry; metabolism; prostate cancer; unsaturation
  8. J Lipid Res. 2020 Jun;pii: S0022-2275(20)43599-0. [Epub ahead of print]61(6): 884-895
      Ceramides are the predominant lipids in the stratum corneum (SC) and are crucial components for normal skin barrier function. Although the composition of various ceramide classes in the human SC has been reported, that in mice is still unknown, despite mice being widely used as animal models of skin barrier function. Here, we performed LC/MS/MS analyses using recently available ceramide class standards to measure 25 classes of free ceramides and 5 classes of protein-bound ceramides from human and mouse SC. Phytosphingosine- and 6-hydroxy sphingosine-type ceramides, which both contain an additional hydroxyl group, were abundant in the human SC (35% and 45% of total ceramides, respectively). In contrast, in mice, phytosph-ingosine- and 6-hydroxy sphingosine-type ceramides were present at ∼1% and undetectable levels, respectively, and sphingosine-type ceramides accounted for ∼90%. In humans, ceramides containing α-hydroxy FA were abundant, whereas ceramides containing β-hydroxy or ω-hydroxy FA were abundant in mice. The hydroxylated β-carbon in β-hydroxy ceramides was in the (R) configuration. Genetic knockout of β-hydroxy acyl-CoA dehydratases in HAP1 cells increased β-hydroxy ceramide levels, suggesting that β-hydroxy acyl-CoA, an FA-elongation cycle intermediate in the ER, is a substrate for β-hydroxy ceramide synthesis. We anticipate that our methods and findings will help to elucidate the role of each ceramide class in skin barrier formation and in the pathogenesis of skin disorders.
    Keywords:  epidermis; fatty acid; lipidomics; mass spectrometry; skin barrier; sphingolipids
  9. Mol Cancer Res. 2021 Feb 12. pii: molcanres.MCR-20-0827-E.2020. [Epub ahead of print]
      Accumulating scientific evidences strongly support the importance of cancer-derived extracellular vesicles (EVs) in organization of tumor microenvironment and metastatic niches, which are also considered as ideal tools for cancer liquid biopsy. To uncover the full scope of proteomic information packaged within EVs secreted directly from human colorectal cancer (CRC), we cultured surgically-resected viable tissues and obtained tissue-exudative extracellular vesicles (Te-EVs). Our quantitative profiling of 6,307 Te-EV proteins and 8,565 tissue proteins from primary CRC and adjacent normal mucosa (n = 17) allowed identification of a specific cargo in CRC-derived Te-EVs, high affinity cationic amino acid transporter 1 (CAT1, p = 5.0 × 10-3, fold change = 6.2), in addition to discovery of a new class of EV markers, VPS family proteins. The EV sandwich ELISA confirmed escalation of the EV-CAT1 level in plasma from CRC patients compared to healthy donors (n = 119, p = 3.8 × 10-7). Further metabolomic analysis revealed that CAT1-overexpressed EVs drastically enhanced vascular endothelial cell growth and tubule formation via upregulation of arginine transport and downstream nitric oxide metabolic pathway. These findings demonstrate the potency of CAT1 as an EV-based biomarker for CRC and its functional significance on tumor angiogenesis. Implications: This study provides a proteome-wide compositional dataset for viable CRC tissue-derived EVs and especially emphasizes importance of EV-CAT1 as a key regulator of angiogenesis.
  10. Mol Cell Proteomics. 2021 Feb 05. pii: S1535-9476(21)00033-5. [Epub ahead of print] 100060
      Intact glycopeptide identification has long been known as a key and challenging barrier to the comprehensive and accurate understanding the role of glycosylation in an organism. Intact glycopeptide analysis is a blossoming field that has received increasing attention in recent years. Mass spectrometry (MS)-based strategies and relative software tools are major drivers that have greatly facilitated the analysis of intact glycopeptides, particularly intact N-glycopeptides. This manuscript provides a systematic review of the intact glycopeptide identification process using mass spectrometry data generated in shotgun proteomic experiments, which typically focus on N-glycopeptide analysis. Particular attention is paid to the software tools that have been recently developed in the last decade for the interpretation and quality control of glycopeptide spectra acquired using different MS strategies. The review also provides information about the characteristics and applications of these software tools, discusses their advantages and disadvantages, and concludes with a discussion of outstanding tools.
  11. Metabolites. 2021 Feb 10. pii: 101. [Epub ahead of print]11(2):
      γ-Hydroxybutyric acid (GHB) is an endogenous short chain fatty acid that acts as a neurotransmitter and neuromodulator in the mammalian brain. It has often been illegally abused or misused due to its strong anesthetic effect, particularly in drug-facilitated crimes worldwide. However, proving its ingestion is not straightforward because of the difficulty in distinguishing between endogenous and exogenous GHB, as well as its rapid metabolism. Metabolomics and metabolism studies have recently been used to identify potential biomarkers of GHB exposure. This mini-review provides an overview of GHB-associated metabolic alterations and explores the potential of metabolites for application as biomarkers of GHB exposure. For this, we discuss the biosynthesis and metabolism of GHB, analytical issues of GHB in biological samples, alterations in metabolic pathways, and changes in the levels of GHB conjugates in biological samples from animal and human studies. Metabolic alterations in organic acids, amino acids, and polyamines in urine enable discrimination between GHB-ingested animals or humans and controls. The potential of GHB conjugates has been investigated in a variety of clinical settings. Despite the recent growth in the application of metabolomics and metabolism studies associated with GHB exposure, it remains challenging to distinguish between endogenous and exogenous GHB. This review highlights the significance of further metabolomics and metabolism studies for the discovery of practical peripheral biomarkers of GHB exposure.
    Keywords:  drug-facilitated crimes; drugs of abuse; metabolomics; γ-hydroxybutyrate
  12. Mol Cell Proteomics. 2020 Dec 08. pii: S1535-9476(20)35125-2. [Epub ahead of print]20 100011
      Glycopeptides in peptide or digested protein samples pose a number of analytical and bioinformatics challenges beyond those posed by unmodified peptides or peptides with smaller posttranslational modifications. Exact structural elucidation of glycans is generally beyond the capability of a single mass spectrometry experiment, so a reasonable level of identification for tandem mass spectrometry, taken by several glycopeptide software tools, is that of peptide sequence and glycan composition, meaning the number of monosaccharides of each distinct mass, e.g., HexNAc(2)Hex(5) rather than man5. Even at this level, however, glycopeptide analysis poses challenges: finding glycopeptide spectra when they are a tiny fraction of the total spectra; assigning spectra with unanticipated glycans, not in the initial glycan database; and finding, scoring, and labeling diagnostic peaks in tandem mass spectra. Here, we discuss recent improvements to Byonic, a glycoproteomics search program, that address these three issues. Byonic now supports filtering spectra by m/z peaks, so that the user can limit attention to spectra with diagnostic peaks, e.g., at least two out of three of 204.087 for HexNAc, 274.092 for NeuAc (with water loss), and 366.139 for HexNAc-Hex, all within a set mass tolerance, e.g., ± 0.01 Da. Also, new is glycan "wildcard" search, which allows an unspecified mass within a user-set mass range to be applied to N- or O-linked glycans and enables assignment of spectra with unanticipated glycans. Finally, the next release of Byonic supports user-specified peak annotations from user-defined posttranslational modifications. We demonstrate the utility of these new software features by finding previously unrecognized glycopeptides in publicly available data, including glycosylated neuropeptides from rat brain.
    Keywords:  Byonic; CD16a; FcγRIIIa; IsoTaG; glycosylation; neuropeptide; prenylation; proSAAS
  13. Oncogene. 2021 Feb 09.
      Fatty acid metabolism is essential for the biogenesis of cellular components and ATP production to sustain proliferation of cancer cells. Long-chain fatty acyl-CoA synthetases (ACSLs), a group of rate-limiting enzymes in fatty acid metabolism, catalyze the bioconversion of exogenous or de novo synthesized fatty acids to their corresponding fatty acyl-CoAs. In this study, systematical analysis of ACSLs levels and the amount of fatty acyl-CoAs illustrated that ACSL1 were significantly associated with the levels of a broad spectrum of fatty acyl-CoAs, and were elevated in human prostate tumors. ACSL1 increased the biosynthesis of fatty acyl-CoAs including C16:0-, C18:0-, C18:1-, and C18:2-CoA, triglycerides and lipid accumulation in cancer cells. Mechanistically, ACSL1 modulated mitochondrial respiration, β-oxidation, and ATP production through regulation of CPT1 activity. Knockdown of ACSL1 inhibited the cell cycle, and suppressed the proliferation and migration of prostate cancer cells in vitro, and growth of prostate xenograft tumors in vivo. Our study implicates ACSL1 as playing an important role in prostate tumor progression, and provides a therapeutic strategy of targeting fatty acid metabolism for the treatment of prostate cancer.
  14. Int J Mol Sci. 2021 Jan 25. pii: 1171. [Epub ahead of print]22(3):
      Pyruvate kinase is a key regulator in glycolysis through the conversion of phosphoenolpyruvate (PEP) into pyruvate. Pyruvate kinase exists in various isoforms that can exhibit diverse biological functions and outcomes. The pyruvate kinase isoenzyme type M2 (PKM2) controls cell progression and survival through the regulation of key signaling pathways. In cancer cells, the dimer form of PKM2 predominates and plays an integral role in cancer metabolism. This predominance of the inactive dimeric form promotes the accumulation of phosphometabolites, allowing cancer cells to engage in high levels of synthetic processing to enhance their proliferative capacity. PKM2 has been recognized for its role in regulating gene expression and transcription factors critical for health and disease. This role enables PKM2 to exert profound regulatory effects that promote cancer cell metabolism, proliferation, and migration. In addition to its role in cancer, PKM2 regulates aspects essential to cellular homeostasis in non-cancer tissues and, in some cases, promotes tissue-specific pathways in health and diseases. In pursuit of understanding the diverse tissue-specific roles of PKM2, investigations targeting tissues such as the kidney, liver, adipose, and pancreas have been conducted. Findings from these studies enhance our understanding of PKM2 functions in various diseases beyond cancer. Therefore, there is substantial interest in PKM2 modulation as a potential therapeutic target for the treatment of multiple conditions. Indeed, a vast plethora of research has focused on identifying therapeutic strategies for targeting PKM2. Recently, targeting PKM2 through its regulatory microRNAs, long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) has gathered increasing interest. Thus, the goal of this review is to highlight recent advancements in PKM2 research, with a focus on PKM2 regulatory microRNAs and lncRNAs and their subsequent physiological significance.
    Keywords:  cancer metabolism; long non-coding RNAs; metabolic reprogramming; pyruvate kinases
  15. J Lipid Res. 2020 Jun;pii: S0022-2275(20)43604-1. [Epub ahead of print]61(6): 953-967
      MS-assisted lipidomic tissue analysis is a valuable tool to assess sphingolipid metabolism dysfunction in disease. These analyses can reveal potential pharmacological targets or direct mechanistic studies to better understand the molecular underpinnings and influence of sphingolipid metabolism alterations on disease etiology. But procuring sufficient human tissues for adequately powered studies can be challenging. Therefore, biorepositories, which hold large collections of cryopreserved human tissues, are an ideal retrospective source of specimens. However, this resource has been vastly underutilized by lipid biologists, as the components of OCT compound used in cryopreservation are incompatible with MS analyses. Here, we report results indicating that OCT compound also interferes with protein quantification assays, and that the presence of OCT compound impacts the quantification of extracted sphingolipids by LC-ESI-MS/MS. We developed and validated a simple and inexpensive method that removes OCT compound from OCT compound-embedded tissues. Our results indicate that removal of OCT compound from cryopreserved tissues does not significantly affect the accuracy of sphingolipid measurements with LC-ESI-MS/MS. We used the validated method to analyze sphingolipid alterations in tumors compared with normal adjacent uninvolved lung tissues from individuals with lung cancer and to determine the long-term stability of sphingolipids in OCT compound-cryopreserved normal lung tissues. We show that lung cancer tumors have significantly altered sphingolipid profiles and that sphingolipids are stable for up to 16 years in OCT compound-cryopreserved normal lung tissues. This validated sphingolipidomic OCT compound-removal protocol should be a valuable addition to the lipid biologist's toolbox.
    Keywords:  biorepository; cancer, ceramide; lipidomics; lung adenocarcinoma; lung squamous cell carcinoma; mass spectrometry; non-small cell lung cancer
  16. Metabolites. 2021 Jan 31. pii: 83. [Epub ahead of print]11(2):
      Availability of the amino acid methionine shows remarkable effects on the physiology of individual cells and whole organisms. For example, most cancer cells, but not normal cells, are hyper dependent on high flux through metabolic pathways connected to methionine, and diets restricted for methionine increase healthy lifespan in model organisms. Methionine's impact on physiology goes beyond its role in initiation of translation and incorporation in proteins. Many of its metabolites have a major influence on cellular functions including epigenetic regulation, maintenance of redox balance, polyamine synthesis, and phospholipid homeostasis. As a central component of such essential pathways, cells require mechanisms to sense methionine availability. When methionine levels are low, cellular response programs induce transcriptional and signaling states to remodel metabolic programs and maintain methionine metabolism. In addition, an evolutionary conserved cell cycle arrest is induced to ensure cellular and genomic integrity during methionine starvation conditions. Methionine and its metabolites are critical for cell growth, proliferation, and development in all organisms. However, mechanisms of methionine perception are diverse. Here we review current knowledge about mechanisms of methionine sensing in yeast and mammalian cells, and will discuss the impact of methionine imbalance on cancer and aging.
    Keywords:  S-adenosylmethionine; aging; cancer; cell cycle; methionine; methionine/SAM sensing
  17. Cell Death Discov. 2020 Apr 16. 6(1): 20
      Cancer cells upregulate anabolic processes to maintain high rates of cellular turnover. Limiting the supply of macromolecular precursors by targeting enzymes involved in biosynthesis is a promising strategy in cancer therapy. Several tumors excessively metabolize glutamine to generate precursors for nonessential amino acids, nucleotides, and lipids, in a process called glutaminolysis. Here we show that pharmacological inhibition of glutaminase (GLS) eradicates glioblastoma stem-like cells (GSCs), a small cell subpopulation in glioblastoma (GBM) responsible for therapy resistance and tumor recurrence. Treatment with small molecule inhibitors compound 968 and CB839 effectively diminished cell growth and in vitro clonogenicity of GSC neurosphere cultures. However, our pharmaco-metabolic studies revealed that only CB839 inhibited GLS enzymatic activity thereby limiting the influx of glutamine derivates into the TCA cycle. Nevertheless, the effects of both inhibitors were highly GLS specific, since treatment sensitivity markedly correlated with GLS protein expression. Strikingly, we found GLS overexpressed in in vitro GSC models as compared with neural stem cells (NSC). Moreover, our study demonstrates the usefulness of in vitro pharmaco-metabolomics to score target specificity of compounds thereby refining drug development and risk assessment.
  18. Metabolomics. 2021 02 07. 17(2): 23
      OBJECTIVE: Gestational disorders including preeclampsia, growth restriction and diabetes are characterized, in part, by altered metabolic interactions between mother and fetus. Understanding their functional relevance requires metabolic characterization under normotypic conditions.METHODS: We performed untargeted metabolomics on livers of pregnant, late-term C57Bl/6J mice (N = 9 dams) and their fetuses (pooling 4 fetuses/litter), using UPLC-MS/MS.
    RESULTS: Multivariate analysis of 730 hepatic metabolites revealed that maternal and fetal metabolite profiles were highly compartmentalized, and were significantly more similar within fetuses (ρaverage = 0.81), or within dams (ρaverage = 0.79), than within each maternal-fetal dyad (ρaverage = - 0.76), suggesting that fetal hepatic metabolism is under distinct and equally tight metabolic control compared with its respective dam. The metabolite profiles were consistent with known differences in maternal-fetal metabolism. The reduced fetal glucose reflected its limited capacity for gluconeogenesis and dependence upon maternal plasma glucose pools. The fetal decreases in essential amino acids and elevations in their alpha-keto acid carnitine conjugates reflects their importance as secondary fuel sources to meet fetal energy demands. Whereas, contrasting elevations in fetal serine, glycine, aspartate, and glutamate reflects their contributions to endogenous nucleotide synthesis and fetal growth. Finally, the elevated maternal hepatic lipids and glycerol were consistent with a catabolic state that spares glucose to meet competing maternal-fetal energy demands.
    CONCLUSIONS: The metabolite profile of the late-term mouse dam and fetus is consistent with prior, non-rodent analyses utilizing plasma and urine. These data position mouse as a suitable model for mechanistic investigation into how maternal-fetal metabolism adapts (or not) to gestational stressors.
    Keywords:  Amino acids; Gestational stress; Hepatic; Nucleotides; Pregnancy; Untargeted metabolomics
  19. Cell Death Dis. 2021 Feb 10. 12(2): 169
      Cisplatin is one of the most effective chemotherapy drugs and is widely used in the treatment of cancer, including hepatocellular carcinoma (HCC) and cervical cancer, but its therapeutic benefit is limited by the development of resistance. Our previous studies demonstrated that BCAT1 promoted cell proliferation and decreased cisplatin sensitivity in HCC cells. However, the exact role and mechanism of how BCAT1 is involved in cisplatin cytotoxicity remain undefined. In this study, we revealed that cisplatin triggered autophagy in cancer cells, with an increase in BCAT1 expression. The cisplatin-induced up-regulation of BCAT1 decreased the cisplatin sensitivity by regulating autophagy through the mTOR signaling pathway. In addition, branched-chain amino acids or leucine treatment inhibited cisplatin- or BCAT1-mediated autophagy and increased cisplatin sensitivity by activating mTOR signaling in cancer cells. Moreover, inhibition of autophagy by chloroquine increased cisplatin sensitivity in vivo. Also, the knockdown of BCAT1 or the administration of leucine activated mTOR signaling, inhibited autophagy, and increased cisplatin sensitivity in cancer cells in vivo. These findings demonstrate a new mechanism, revealing that BCAT1 decreases cisplatin sensitivity in cancer cells by inducing mTOR-mediated autophagy via branched-chain amino acid leucine metabolism, providing an attractive pharmacological target to improve the effectiveness of chemotherapy.
  20. Anal Bioanal Chem. 2021 Feb 10.
      Insulin-like growth factors 1 and 2 (IGF-1 and IGF-2) are important biomarkers in research and diagnosis of growth disorders. Quantitative analysis is performed using various ligand-binding assays or enzymatic digestion LC-MS/MS methods, whose widespread adoption is hampered by time-consuming sample preparation procedures. We present a simple and fast antibody-free LC-MS/MS method for the quantification of intact IGF-1 and IGF-2 in human plasma. The method requires 50 μL of plasma and uses fully 15N-labelled IGF-1 as internal standard. It features trifluoroethanol (TFE)-based IGF/IGF-binding protein complex dissociation and a two-step selective protein precipitation workflow, using 5% acetic acid in 80/20 acetone/acetonitrile (precipitation 1) and ice-cold ethanol (precipitation 2). Detection of intact IGF-1 and IGF-2 is performed by means of a Waters XEVO TQ-S triple quadrupole mass spectrometer in positive electrospray ionisation (ESI+) mode. Lower limits of quantification were 5.9 ng/mL for IGF-1 and 8.4 ng/mL for IGF-2. Intra-assay imprecision was below 4.5% and inter-assay imprecision was below 5.8% for both analytes. An excellent correlation was found between nominal and measured concentrations of the WHO reference standard for IGF-1. Comparison with the IDS-iSYS IGF-1 immunoassay showed good correlation (R2 > 0.97), although a significant bias was observed with the immunoassay giving substantially higher concentrations. The LC-MS/MS method described here allows for reliable and simultaneous quantification of IGF-1 and IGF-2 in plasma, without the need for enzymatic digestion. The method can be readily implemented in clinical mass spectrometry laboratories and has the potential to be adapted for the analysis of different similarly sized peptide hormones.
    Keywords:  Biomarker; Insulin-like growth factor 1 (IGF-1); Insulin-like growth factor 2 (IGF-2); Liquid chromatography-tandem mass spectrometry (LC-MS/MS); Peptide hormone analysis
  21. Anal Chem. 2021 Feb 08.
      Quantitative metabolomics requires the analysis of the same or a very similar amount of samples in order to accurately determine the concentration differences of individual metabolites in comparative samples. Ideally, the total amount or concentration of metabolites in each sample is measured to normalize all the analyzed samples. In this work, we describe a very sensitive method to measure a subclass of metabolites as a surrogate quantifier for normalization of samples with limited amounts. This method starts with low-volume dansyl labeling of all metabolites containing a primary/secondary amine or phenol group in a sample to produce a final solution of 21 μL. The dansyl-labeled metabolites generate fluorescence signals at 520 nm with photoexcitation at 250 nm. To remove the interference of dansyl hydroxyl products (Dns-OH) formed from the labeling reagents used, a fast-gradient liquid chromatography separation is used to elute Dns-OH using aqueous solution, followed by organic solvent elution to produce a chromatographic peak of labeled metabolites, giving a measurement throughput of 6 min per sample. The integrated fluorescence signals of the peak are found to be related to the injection amount of the dansyl-labeled metabolites. A calibration curve using mixtures of dansyl-labeled amino acids is used to determine the total concentration of labeled metabolites in a sample. This concentration is used for normalization of samples in the range from 2 to 120 μM in 21 μL with only 1 μL consumed for fluorescence quantification (i.e., 2-120 pmol). We demonstrate the application of this sensitive sample normalization method in comparative metabolome analysis of human cancer cells, MCF-7 cells, treated with and without resveratrol, using a starting material of as low as 500 cells.
  22. Mol Cell Proteomics. 2021 Feb 06. pii: S1535-9476(21)00026-8. [Epub ahead of print] 100053
      Esophageal squamous cell cancer (ESCC) is an aggressive malignancy with poor therapeutic outcomes. However, the alterations in proteins and post-translational modifications (PTMs) leading to the pathogenesis of ESCC remains unclear. Here, we provide the comprehensive characterization of the proteome, phosphorylome, lysine acetylome and succinylome for ESCC and matched control cells using quantitative proteomic approach. We identify abnormal protein and post-translational modification (PTM) pathways, including significantly downregulated lysine succinylation sites in cancer cells. Focusing on hyposuccinylation, we reveal that this altered PTM was enriched on enzymes of metabolic pathways inextricably linked with cancer metabolism. Importantly, ESCC malignant behaviors such as cell migration are inhibited once the level of succinylation was restored in vitro or in vivo. This effect was further verified by mutations to disrupt succinylation sites in candidate proteins. Meanwhile, we found that succinylation has a negative regulatory effect on histone methylation to promote cancer migration. Finally, hyposuccinylation is confirmed in primary ESCC specimens. Our findings together demonstrate that lysine succinylation may alter ESCC metabolism and migration, providing new insights into the functional significance of PTM in cancer biology.
    Keywords:  Esophageal squamous; Migration; Post-translational modification; Succinylation
  23. ACS Omega. 2021 Feb 02. 6(4): 2494-2504
      Previous benchmarking studies have demonstrated the importance of instrument acquisition methodology and statistical analysis on quantitative performance in label-free proteomics. However, the effects of these parameters in combination with replicate number and false discovery rate (FDR) corrections are not known. Using a benchmarking standard, we systematically evaluated the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections. These analyses reveal a complex interaction between these parameters that greatly impacts the quantitative fidelity of protein- and peptide-level quantification. At a high replicate number (n = 8), both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methodologies yield accurate protein quantification across statistical approaches. However, at a low replicate number (n = 4), only DIA in combination with linear models for microarrays (LIMMA) and reproducibility-optimized test statistic (ROTS) produced a high level of quantitative fidelity. Quantitative accuracy at low replicates is also greatly impacted by FDR corrections, with Benjamini-Hochberg and Storey corrections yielding variable true positive rates for DDA workflows. For peptide quantification, replicate number and acquisition methodology are even more critical. A higher number of replicates in combination with DIA and LIMMA produce high quantitative fidelity, while DDA performs poorly regardless of replicate number or statistical approach. These results underscore the importance of pairing instrument acquisition methodology with the appropriate replicate number and statistical approach for optimal quantification performance.
  24. J Proteome Res. 2021 Feb 10.
      The deployment of proteomic analysis in clinical studies represents a significant opportunity to detect and validate biomarkers in translational medicine, improve disease understanding, and provide baseline information on population health. However, comprehensive proteome studies usually employ nanoscale chromatography and often require several hours of analysis/sample. Here, we describe a high-throughput liquid chromatography tandem mass spectrometry (LC/MS/MS) methodology using 1 mm scale chromatography requiring only 15 min/sample, coupled to ion mobility-enabled mass spectrometry. The short run time effected a 6-fold increase in productivity compared with nanoscale LC/MS. The method demonstrated excellent reproducibility with retention time coefficient of variations of less than 0.05% and peak area reproducibility ranging from 5 to 15%. The 1 mm system produced similar chromatographic peak capacity values to the nanoscale miniaturized system, detecting 90% of the Escherichia coli proteins identified by the 75 μm LC/MS system (albeit based on only 75% of the peptides found by the latter). Application to the analysis of serum samples from a human prostate cancer study group resulted in the identification of a total of 533 proteins revealing the differential expression of proteins linked to patients receiving hormone-radiotherapy or undergoing surgery.
    Keywords:  high throughput; human serum; large cohorts; prostate cancer; proteomics
  25. J Proteome Res. 2021 Feb 11.
      Simple light isotope metabolic labeling (SLIM labeling) is an innovative method to quantify variations in the proteome based on an original in vivo labeling strategy. Heterotrophic cells grown in U-[12C] as the sole source of carbon synthesize U-[12C]-amino acids, which are incorporated into proteins, giving rise to U-[12C]-proteins. This results in a large increase in the intensity of the monoisotope ion of peptides and proteins, thus allowing higher identification scores and protein sequence coverage in mass spectrometry experiments. This method, initially developed for signal processing and quantification of the incorporation rate of 12C into peptides, was based on a multistep process that was difficult to implement for many laboratories. To overcome these limitations, we developed a new theoretical background to analyze bottom-up proteomics data using SLIM-labeling (bSLIM) and established simple procedures based on open-source software, using dedicated OpenMS modules, and embedded R scripts to process the bSLIM experimental data. These new tools allow computation of both the 12C abundance in peptides to follow the kinetics of protein labeling and the molar fraction of unlabeled and 12C-labeled peptides in multiplexing experiments to determine the relative abundance of proteins extracted under different biological conditions. They also make it possible to consider incomplete 12C labeling, such as that observed in cells with nutritional requirements for nonlabeled amino acids. These tools were validated on an experimental dataset produced using various yeast strains of Saccharomyces cerevisiae and growth conditions. The workflows are built on the implementation of appropriate calculation modules in a KNIME working environment. These new integrated tools provide a convenient framework for the wider use of the SLIM-labeling strategy.
    Keywords:  12C; In vivo metabolic labeling; KNIME; OpenMS; data processing workflow; light carbon isotope; quantitative proteomics; yeast