bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2021‒01‒17
37 papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh


  1. Mol Cell Proteomics. 2019 Jun;pii: S1535-9476(20)31823-5. [Epub ahead of print]18(6): 1242-1254
    Bruderer R, Muntel J, Müller S, Bernhardt OM, Gandhi T, Cominetti O, Macron C, Carayol J, Rinner O, Astrup A, Saris WHM, Hager J, Valsesia A, Dayon L, Reiter L.
      Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%. The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA. In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma.
    Keywords:  Absolute quantification; Clinical proteomics; Label-free quantification; Plasma or serum analysis; SWATH-MS; data-independent acquisition; high throughput; single shot; stable isotope standards
    DOI:  https://doi.org/10.1074/mcp.RA118.001288
  2. Mol Cell Proteomics. 2019 May;pii: S1535-9476(20)31607-8. [Epub ahead of print]18(5): 982-994
    Wichmann C, Meier F, Virreira Winter S, Brunner AD, Cox J, Mann M.
      Mass spectrometry (MS)-based proteomics is often performed in a shotgun format, in which as many peptide precursors as possible are selected from full or MS1 scans so that their fragment spectra can be recorded in MS2 scans. Although achieving great proteome depths, shotgun proteomics cannot guarantee that each precursor will be fragmented in each run. In contrast, targeted proteomics aims to reproducibly and sensitively record a restricted number of precursor/fragment combinations in each run, based on prescheduled mass-to-charge and retention time windows. Here we set out to unify these two concepts by a global targeting approach in which an arbitrary number of precursors of interest are detected in real-time, followed by standard fragmentation or advanced peptide-specific analyses. We made use of a fast application programming interface to a quadrupole Orbitrap instrument and real-time recalibration in mass, retention time and intensity dimensions to predict precursor identity. MaxQuant.Live is freely available (www.maxquant.live) and has a graphical user interface to specify many predefined data acquisition strategies. Acquisition speed is as fast as with the vendor software and the power of our approach is demonstrated with the acquisition of breakdown curves for hundreds of precursors of interest. We also uncover precursors that are not even visible in MS1 scans, using elution time prediction based on the auto-adjusted retention time alone. Finally, we successfully recognized and targeted more than 25,000 peptides in single LC-MS runs. Global targeting combines the advantages of two classical approaches in MS-based proteomics, whereas greatly expanding the analytical toolbox. MaxQuant.Live builds on the fast application programming interface of quadrupole Orbitrap mass analyzers to control data acquisition in real-time (freely available at www.maxquant.live). Its graphical user interface enables advanced data acquisition strategies, such as in-depth characterization of peptides of interest. Online recalibration in mass, retention time, and intensity dimensions extends this concept to more than 25,000 peptides per run. Our "global targeting" strategy combines the best of targeted and shotgun approaches.
    Keywords:  Bioinformatics software; Computational Biology; Global targeting; Label-free quantification; Parallel reaction monitoring; Protein Identification*; Quality control and metrics; Quantification; Real time control; Targeted mass spectrometry
    DOI:  https://doi.org/10.1074/mcp.TIR118.001131
  3. Nat Commun. 2021 01 11. 12(1): 254
    Hansen FM, Tanzer MC, Brüning F, Bludau I, Stafford C, Schulman BA, Robles MS, Karayel O, Mann M.
      Protein ubiquitination is involved in virtually all cellular processes. Enrichment strategies employing antibodies targeting ubiquitin-derived diGly remnants combined with mass spectrometry (MS) have enabled investigations of ubiquitin signaling at a large scale. However, so far the power of data independent acquisition (DIA) with regards to sensitivity in single run analysis and data completeness have not yet been explored. Here, we develop a sensitive workflow combining diGly antibody-based enrichment and optimized Orbitrap-based DIA with comprehensive spectral libraries together containing more than 90,000 diGly peptides. This approach identifies 35,000 diGly peptides in single measurements of proteasome inhibitor-treated cells - double the number and quantitative accuracy of data dependent acquisition. Applied to TNF signaling, the workflow comprehensively captures known sites while adding many novel ones. An in-depth, systems-wide investigation of ubiquitination across the circadian cycle uncovers hundreds of cycling ubiquitination sites and dozens of cycling ubiquitin clusters within individual membrane protein receptors and transporters, highlighting new connections between metabolism and circadian regulation.
    DOI:  https://doi.org/10.1038/s41467-020-20509-1
  4. Front Cell Dev Biol. 2020 ;8 602476
    Haferkamp S, Drexler K, Federlin M, Schlitt HJ, Berneburg M, Adamski J, Gaumann A, Geissler EK, Ganapathy V, Parkinson EK, Mycielska ME.
      Cancer cells need excess energy and essential nutrients/metabolites not only to divide and proliferate but also to migrate and invade distant organs for metastasis. Fatty acid and cholesterol synthesis, considered a hallmark of cancer for anabolism and membrane biogenesis, requires citrate. We review here potential pathways in which citrate is synthesized and/or supplied to cancer cells and the impact of extracellular citrate on cancer cell metabolism and growth. Cancer cells employ different mechanisms to support mitochondrial activity and citrate synthesis when some of the necessary substrates are missing in the extracellular space. We also discuss the different transport mechanisms available for the entry of extracellular citrate into cancer cells and how citrate as a master metabolite enhances ATP production and fuels anabolic pathways. The available literature suggests that cancer cells show an increased metabolic flexibility with which they tackle changing environmental conditions, a phenomenon crucial for cancer cell proliferation and metastasis.
    Keywords:  cancer; cancer associated fibroblast (CAF); metabolism; senescent fibroblasts; transporter
    DOI:  https://doi.org/10.3389/fcell.2020.602476
  5. Mol Cell Proteomics. 2020 Feb;pii: S1535-9476(20)35088-X. [Epub ahead of print]19(2): 421-430
    Huang T, Bruderer R, Muntel J, Xuan Y, Vitek O, Reiter L.
      In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. When comparing biological conditions, the interferences can compromise the detection of differential peptide or protein abundance and lead to false positive or false negative conclusions. We hypothesized that the combined use of MS1 and MS2 quantitative signals could improve our ability to detect differentially abundant proteins. Therefore, we developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2. This was particularly true for comparisons with low fold changes, few replicates, and situations where MS1 and MS2 were of similar quality. When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways. Since recent technological developments continue to increase the quality of MS1 signals (e.g. using the BoxCar scan mode for Orbitrap instruments), the combination of the MS1 and MS2 information has a high potential for future statistical analysis of DIA data.
    Keywords:  Cancer Biomarker(s); Label-Free Quantification; Lung Cancer; Mass Spectrometry; Quantification; SWATH-MS
    DOI:  https://doi.org/10.1074/mcp.RA119.001705
  6. Biochim Biophys Acta Mol Cell Biol Lipids. 2021 Jan 11. pii: S1388-1981(21)00010-X. [Epub ahead of print] 158884
    Ruiz M, Palmgren H, Henricsson M, Devkota R, Jaiswal H, Maresca M, Bohlooly-Y M, Peng XR, Borén J, Pilon M.
      How cells maintain vital membrane lipid homeostasis while obtaining most of their constituent fatty acids from a varied diet remains largely unknown. Here, we used transcriptomics, lipidomics, growth and respiration assays, and membrane property analyses in human HEK293 cells or human umbilical vein endothelial cells (HUVEC) to show that the function of AdipoR2 is to respond to membrane rigidification by regulating many lipid metabolism genes. We also show that AdipoR2-dependent membrane homeostasis is critical for growth and respiration in cells challenged with saturated fatty acids. Additionally, we found that AdipoR2 deficiency causes transcriptome and cell physiological defects similar to those observed in SREBP-deficient cells upon SFA challenge. Finally, we compared several genes considered important for lipid homeostasis, namely AdipoR2, SCD, FADS2, PEMT and ACSL4, and found that AdipoR2 and SCD are the most important among these to prevent membrane rigidification and excess saturation when human cells are challenged with exogenous SFAs. We conclude that AdipoR2-dependent membrane homeostasis is one of the primary mechanisms that protects against exogenous SFAs.
    Keywords:  AdipoR2; cell biology; diet and dietary lipids; fatty acid/desaturase; lipidomics; lipotoxicity; membrane fluidity; membrane lipids; molecular biology/genetics
    DOI:  https://doi.org/10.1016/j.bbalip.2021.158884
  7. Mol Cell Proteomics. 2020 Sep;pii: S1535-9476(20)35102-1. [Epub ahead of print]19(9): 1546-1560
    Karayel Ö, Tonelli F, Virreira Winter S, Geyer PE, Fan Y, Sammler EM, Alessi DR, Steger M, Mann M.
      Pathogenic mutations in the Leucine-rich repeat kinase 2 (LRRK2) are the predominant genetic cause of Parkinson's disease (PD). They increase its activity, resulting in augmented Rab10-Thr73 phosphorylation and conversely, LRRK2 inhibition decreases pRab10 levels. Currently, there is no assay to quantify pRab10 levels for drug target engagement or patient stratification. To meet this challenge, we developed an high accuracy and sensitivity targeted mass spectrometry (MS)-based assay for determining Rab10-Thr73 phosphorylation stoichiometry in human samples. It uses synthetic stable isotope-labeled (SIL) analogues for both phosphorylated and nonphosphorylated tryptic peptides surrounding Rab10-Thr73 to directly derive the percentage of Rab10 phosphorylation from attomole amounts of the endogenous phosphopeptide. The SIL and the endogenous phosphopeptides are separately admitted into an Orbitrap analyzer with the appropriate injection times. We test the reproducibility of our assay by determining Rab10-Thr73 phosphorylation stoichiometry in neutrophils of LRRK2 mutation carriers before and after LRRK2 inhibition. Compared with healthy controls, the PD predisposing mutation carriers LRRK2 G2019S and VPS35 D620N display 1.9-fold and 3.7-fold increased pRab10 levels, respectively. Our generic MS-based assay further establishes the relevance of pRab10 as a prognostic PD marker and is a powerful tool for determining LRRK2 inhibitor efficacy and for stratifying PD patients for LRRK2 inhibitor treatment.
    Keywords:  Biomarker: diagnostic; Parkinson disease; clinical proteomics; neurodegenerative diseases; parallel reaction monitoring; phosphorylation; protein kinases; selected ion monitoring; targeted mass spectrometry; targeted therapies
    DOI:  https://doi.org/10.1074/mcp.RA120.002055
  8. Sci Rep. 2021 Jan 15. 11(1): 1521
    Tripp BA, Dillon ST, Yuan M, Asara JM, Vasunilashorn SM, Fong TG, Metzger ED, Inouye SK, Xie Z, Ngo LH, Marcantonio ER, Libermann TA, Otu HH.
      Postoperative delirium is the most common complication among older adults undergoing major surgery. The pathophysiology of delirium is poorly understood, and no blood-based, predictive markers are available. We characterized the plasma metabolome of 52 delirium cases and 52 matched controls from the Successful Aging after Elective Surgery (SAGES) cohort (N = 560) of patients ≥ 70 years old without dementia undergoing scheduled major non-cardiac surgery. We applied targeted mass spectrometry with internal standards and pooled controls using a nested matched case-control study preoperatively (PREOP) and on postoperative day 2 (POD2) to identify potential delirium risk and disease markers. Univariate analyses identified 37 PREOP and 53 POD2 metabolites associated with delirium and multivariate analyses achieved significant separation between the two groups with an 11-metabolite prediction model at PREOP (AUC = 83.80%). Systems biology analysis using the metabolites with differential concentrations rendered "valine, leucine, and isoleucine biosynthesis" at PREOP and "citrate cycle" at POD2 as the most significantly enriched pathways (false discovery rate < 0.05). Perturbations in energy metabolism and amino acid synthesis pathways may be associated with postoperative delirium and suggest potential mechanisms for delirium pathogenesis. Our results could lead to the development of a metabolomic delirium predictor.
    DOI:  https://doi.org/10.1038/s41598-020-80412-z
  9. Mol Cell Proteomics. 2019 Aug;pii: S1535-9476(20)34062-7. [Epub ahead of print]18(8): 1619-1629
    Qiao Z, Zhang Y, Ge M, Liu S, Jiang X, Shang Z, Liu H, Cao C, Xiao H.
      Cancer progression is frequently caused by metastasis and leads to significantly increased mortality. Cell derived extracellular vesicles, including exosomes, in the microenvironment play key roles in cellular signal transduction, whereas their biological function in cancer metastasis and progression needs in-depth investigation. Here, we initially demonstrate that the small extracellular vesicles (sEVs) derived from highly metastatic lung cancer cells exhibited great capacity to promote the progression of recipient cells. Quantitative proteomics was employed to comprehensively decipher the proteome of cell derived sEVs and more than 1400 sEVs proteins were identified. Comparison analysis indicates that sEVs-HGF is a potential metastasis related protein and our verification data from clinical lung cancer plasma samples and in vivo experiments further confirmed the association. We found that sEVs-HGF could induce epithelial-mesenchymal transition and the coordination between HGF and c-Met was confirmed through corresponding target knockdown and kinase inhibition. Our data collectively demonstrate that cancer cell derived sEVs contribute to recipient cell metastasis through promoting HGF/c-Met pathway, which are potential targets for the prevention and treatment of cancer metastasis.
    Keywords:  Exosomes; HGF/c-Met pathway; Lung cancer; Mass Spectrometry; Metastasis; Proteomics; Subcellular analysis
    DOI:  https://doi.org/10.1074/mcp.RA119.001502
  10. Mol Cell Proteomics. 2020 Jan;pii: S1535-9476(20)30016-5. [Epub ahead of print]19(1): 209-222
    Doellinger J, Schneider A, Hoeller M, Lasch P.
      The main challenge of bottom-up proteomic sample preparation is to extract proteomes in a manner that enables efficient protein digestion for subsequent mass spectrometric analysis. Today's sample preparation strategies are commonly conceptualized around the removal of detergents, which are essential for extraction but strongly interfere with digestion and LC-MS. These multi-step preparations contribute to a lack of reproducibility as they are prone to losses, biases and contaminations, while being time-consuming and labor-intensive. We report a detergent-free method, named Sample Preparation by Easy Extraction and Digestion (SPEED), which consists of three mandatory steps, acidification, neutralization and digestion. SPEED is a universal method for peptide generation from various sources and is easily applicable even for lysis-resistant sample types as pure trifluoroacetic acid (TFA) is used for highly efficient protein extraction by complete sample dissolution. The protocol is highly reproducible, virtually loss-less, enables very rapid sample processing and is superior to the detergent/chaotropic agent-based methods FASP, ISD-Urea and SP3 for quantitative proteomics. SPEED holds the potential to dramatically simplify and standardize sample preparation while improving the depth of proteome coverage especially for challenging samples.
    Keywords:  TFA; automation; bacteria; detergent-free; digestion; label-free quantification; lysis; mass spectrometry; microbiome; pathogens; protein denaturation; proteomics; sample preparation
    DOI:  https://doi.org/10.1074/mcp.TIR119.001616
  11. Mol Cell Proteomics. 2020 Jun;pii: S1535-9476(20)34998-7. [Epub ahead of print]19(6): 1058-1069
    Prianichnikov N, Koch H, Koch S, Lubeck M, Heilig R, Brehmer S, Fischer R, Cox J.
      Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides and proteins in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org.
    Keywords:  Bioinformatics; bioinformatics software; label-free quantification; mass spectrometry; quantification
    DOI:  https://doi.org/10.1074/mcp.TIR119.001720
  12. Mol Cell Proteomics. 2019 Aug;pii: S1535-9476(20)34067-6. [Epub ahead of print]18(8): 1700-1702
    Kiweler M, Looso M, Graumann J.
      In the context of publishing data sets acquired by mass spectrometry or works based on such molecular screens, metadata documenting the instrument settings are of central importance to the evaluation and reproduction of results. A single experiment may be linked to hundreds of data acquisitions, which are frequently stored in proprietary file formats. Together with community-, repository-, as well as publisher-specific reporting standards, this state of affairs frequently leads to manual -and thus error prone-metadata extraction and formatting. Data extracted from a single file also often stand in for an entire file set, implying a risk for unreported parameter divergence. To support quality control and data reporting, the C# application MARMoSET extracts and reduces publication relevant metadata from Thermo Fischer Scientific RAW files. It is integrated with an R package for easy reporting. The tool is expected to be particularly useful to high throughput environments such as service facilities with large project numbers and/or sizes.
    Keywords:  Bioinformatics; Bioinformatics software; Data standards; Mass Spectrometry; Quality control and metrics
    DOI:  https://doi.org/10.1074/mcp.TIR119.001505
  13. Semin Cancer Biol. 2021 Jan 09. pii: S1044-579X(21)00005-5. [Epub ahead of print]
    Varela-López A, Vera-Ramírez L, Giampieri F, Navarro-Hortal MD, Forbes-Hernández TY, Battino M, Quiles JL.
      Evidence demonstrates the importance of lipid metabolism and signaling in cancer cell biology. De novo lipogenesis is an important source of lipids for cancer cells, but exogenous lipid uptake remains essential for many cancer cells. Dietary lipids can modify lipids present in tumor microenvironment affecting cancer cell metabolism. Clinical trials have shown that diets rich in polyunsaturated fatty acids (PUFA) can negatively affect tumor growth. However, certain n-6 PUFAs can also contribute to cancer progression. Identifying the molecular mechanisms through which lipids affect cancer progression will provide an opportunity for focused dietary interventions that could translate into the development of personalized diets for cancer control. However, the effective mechanisms of action of PUFAs have not been fully clarified yet. Mitochondria controls ATP generation, redox homeostasis, metabolic signaling, apoptotic pathways and many aspects of autophagy, and it has been recognized to play a key role in cancer. The purpose of this review is to summarize the current evidence linking dietary lipids effects on mitochondrial aspects with consequences for cancer progression and the molecular mechanisms that underlie this association.
    Keywords:  Apoptosis; autophagy; bioenergetics; lipogenesis; redox biology
    DOI:  https://doi.org/10.1016/j.semcancer.2021.01.001
  14. Mol Cell Proteomics. 2020 Dec;pii: S1535-9476(20)60012-3. [Epub ahead of print]19(12): 2068-2090
    Kurimchak AM, Kumar V, Herrera-Montávez C, Johnson KJ, Srivastava N, Davarajan K, Peri S, Cai KQ, Mantia-Smaldone GM, Duncan JS.
      Endometrial carcinoma (EC) is the most common gynecologic malignancy in the United States, with limited effective targeted therapies. Endometrial tumors exhibit frequent alterations in protein kinases, yet only a small fraction of the kinome has been therapeutically explored. To identify kinase therapeutic avenues for EC, we profiled the kinome of endometrial tumors and normal endometrial tissues using Multiplexed Inhibitor Beads and Mass Spectrometry (MIB-MS). Our proteomics analysis identified a network of kinases overexpressed in tumors, including Serine/Arginine-Rich Splicing Factor Kinase 1 (SRPK1). Immunohistochemical (IHC) analysis of endometrial tumors confirmed MIB-MS findings and showed SRPK1 protein levels were highly expressed in endometrioid and uterine serous cancer (USC) histological subtypes. Moreover, querying large-scale genomics studies of EC tumors revealed high expression of SRPK1 correlated with poor survival. Loss-of-function studies targeting SRPK1 in an established USC cell line demonstrated SRPK1 was integral for RNA splicing, as well as cell cycle progression and survival under nutrient deficient conditions. Profiling of USC cells identified a compensatory response to SRPK1 inhibition that involved EGFR and the up-regulation of IGF1R and downstream AKT signaling. Co-targeting SRPK1 and EGFR or IGF1R synergistically enhanced growth inhibition in serous and endometrioid cell lines, representing a promising combination therapy for EC.
    Keywords:  affinity proteomics; cancer biomarker(s); combination therapies; endometrial carcinoma; kinases; kinome; pathway analysis; splicing; therapeutic targets; tissue proteomics
    DOI:  https://doi.org/10.1074/mcp.RA120.002012
  15. Mol Cell Proteomics. 2019 Jul;pii: S1535-9476(20)31547-4. [Epub ahead of print]18(7): 1396-1409
    Narimatsu Y, Joshi HJ, Schjoldager KT, Hintze J, Halim A, Steentoft C, Nason R, Mandel U, Bennett EP, Clausen H, Vakhrushev SY.
      Most proteins trafficking the secretory pathway of metazoan cells will acquire GalNAc-type O-glycosylation. GalNAc-type O-glycosylation is differentially regulated in cells by the expression of a repertoire of up to twenty genes encoding polypeptide GalNAc-transferase isoforms (GalNAc-Ts) that initiate O-glycosylation. These GalNAc-Ts orchestrate the positions and patterns of O-glycans on proteins in coordinated, but poorly understood ways - guided partly by the kinetic properties and substrate specificities of their catalytic domains, as well as by modulatory effects of their unique GalNAc-binding lectin domains. Here, we provide the hereto most comprehensive characterization of nonredundant contributions of individual GalNAc-T isoforms to the O-glycoproteome of the human HEK293 cell using quantitative differential O-glycoproteomics on a panel of isogenic HEK293 cells with knockout of GalNAc-T genes (GALNT1, T2, T3, T7, T10, or T11). We confirm that a major part of the O-glycoproteome is covered by redundancy, whereas distinct O-glycosite subsets are covered by nonredundant GalNAc-T isoform-specific functions. We demonstrate that the GalNAc-T7 and T10 isoforms function in follow-up of high-density O-glycosylated regions, and that GalNAc-T11 has highly restricted functions and essentially only serves the low-density lipoprotein-related receptors in linker regions (C6XXXTC1) between the ligand-binding repeats.
    Keywords:  ETD; GALNT; Glycoproteomics; Glycosylation; Mass Spectrometry; Post-translational modifications*; Tandem Mass Spectrometry
    DOI:  https://doi.org/10.1074/mcp.RA118.001121
  16. Front Oncol. 2020 ;10 596197
    Otto AM.
      The metabolism of cancer cells is an issue of dealing with fluctuating and limiting levels of nutrients in a precarious microenvironment to ensure their vitality and propagation. Glucose and glutamine are central metabolites for catabolic and anabolic metabolism, which is in the limelight of numerous diagnostic methods and therapeutic targeting. Understanding tumor metabolism in conditions of nutrient depletion is important for such applications and for interpreting the readouts. To exemplify the metabolic network of tumor cells in a model system, the fate 13C6-glucose was tracked in a breast cancer cell line growing in variable low glucose/low glutamine conditions. 13C-glucose-derived metabolites allowed to deduce the engagement of metabolic pathways, namely glycolysis, the TCA-cycle including glutamine and pyruvate anaplerosis, amino acid synthesis (serine, glycine, aspartate, glutamate), gluconeogenesis, and pyruvate replenishment. While the metabolic program did not change, limiting glucose and glutamine supply reduced cellular metabolite levels and enhanced pyruvate recycling as well as pyruvate carboxylation for entry into the TCA-cycle. Otherwise, the same metabolic pathways, including gluconeogenesis, were similarly engaged with physiologically saturating as with limiting glucose and glutamine. Therefore, the metabolic plasticity in precarious nutritional microenvironment does not require metabolic reprogramming, but is based on dynamic changes in metabolite quantity, reaction rates, and directions of the existing metabolic network.
    Keywords:  13C-glucose tracing; TCA-cycle; anaplerosis; glutamine; glycolysis; metabolic network; nutrient deprivation; pyruvate replenishment
    DOI:  https://doi.org/10.3389/fonc.2020.596197
  17. Biochim Biophys Acta Mol Cell Res. 2021 Jan 12. pii: S0167-4889(21)00019-7. [Epub ahead of print] 118965
    Yu Y, Moretti IF, Grzeschik NA, Sibon OCM, Schepers H.
      Coenzyme A (CoA) is a key molecule in cellular metabolism including the tricarboxylic acid cycle, fatty acid synthesis, amino acid synthesis and lipid metabolism. Moreover, CoA is required for biological processes like protein post-translational modifications (PTMs) including acylation. CoA levels affect the amount of histone acetylation and thereby modulate gene expression. A direct influence of CoA levels on other PTMs, like CoAlation and 4'-phosphopantetheinylation has been relatively less addressed and will be discussed here. Increased CoA levels are associated with increased CoAlation, whereas decreased 4'-phosphopantetheinylation is observed under circumstances of decreased CoA levels. We discuss how these two PTMs can positively or negatively influence target proteins depending on CoA levels. This review highlights the impact of CoA levels on post-translational modifications, their counteractive interplay and the far-reaching consequences thereof.
    DOI:  https://doi.org/10.1016/j.bbamcr.2021.118965
  18. Mol Cell Proteomics. 2019 Aug 09. pii: S1535-9476(20)32760-2. [Epub ahead of print]18(8S1): S37-S51
    Zhan X, Cheng J, Huang Z, Han Z, Helm B, Liu X, Zhang J, Wang TF, Ni D, Huang K.
      Tumors are heterogeneous tissues with different types of cells such as cancer cells, fibroblasts, and lymphocytes. Although the morphological features of tumors are critical for cancer diagnosis and prognosis, the underlying molecular events and genes for tumor morphology are far from being clear. With the advancement in computational pathology and accumulation of large amount of cancer samples with matched molecular and histopathology data, researchers can carry out integrative analysis to investigate this issue. In this study, we systematically examine the relationships between morphological features and various molecular data in breast cancers. Specifically, we identified 73 breast cancer patients from the TCGA and CPTAC projects matched whole slide images, RNA-seq, and proteomic data. By calculating 100 different morphological features and correlating them with the transcriptomic and proteomic data, we inferred four major biological processes associated with various interpretable morphological features. These processes include metabolism, cell cycle, immune response, and extracellular matrix development, which are all hallmarks of cancers and the associated morphological features are related to area, density, and shapes of epithelial cells, fibroblasts, and lymphocytes. In addition, protein specific biological processes were inferred solely from proteomic data, suggesting the importance of proteomic data in obtaining a holistic understanding of the molecular basis for tumor tissue morphology. Furthermore, survival analysis yielded specific morphological features related to patient prognosis, which have a strong association with important molecular events based on our analysis. Overall, our study demonstrated the power for integrating multiple types of biological data for cancer samples in generating new hypothesis as well as identifying potential biomarkers predicting patient outcome. Future work includes causal analysis to identify key regulators for cancer tissue development and validating the findings using more independent data sets.
    Keywords:  Breast cancer; Cell cycle; Computational pathology; Imaging genomics; Immune response; Morphology; Omics; Proteogenomics; Systems biology; Tumor microenvironment
    DOI:  https://doi.org/10.1074/mcp.RA118.001232
  19. Mol Cell Proteomics. 2019 Aug 09. pii: S1535-9476(20)32771-7. [Epub ahead of print]18(8S1): S193-S201
    Brademan DR, Riley NM, Kwiecien NW, Coon JJ.
      Here we present IPSA, an innovative web-based spectrum annotator that visualizes and characterizes peptide tandem mass spectra. A tool for the scientific community, IPSA can visualize peptides collected using a wide variety of experimental and instrumental configurations. Annotated spectra are customizable via a selection of interactive features and can be exported as editable scalable vector graphics to aid in the production of publication-quality figures. Single spectra can be analyzed through provided web forms, whereas data for multiple peptide spectral matches can be uploaded using the Proteomics Standards Initiative file formats mzTab, mzIdentML, and mzML. Alternatively, peptide identifications and spectral data can be provided using generic file formats. IPSA provides supports for annotating spectra collecting using negative-mode ionization and facilitates the characterization of experimental MS/MS performance through the optional export of fragment ion statistics from one to many peptide spectral matches. This resource is made freely accessible at http://interactivepeptidespectralannotator.com, whereas the source code and user guides are available at https://github.com/coongroup/IPSA for private hosting or custom implementations.
    Keywords:  Bioinformatics; Bioinformatics software; Data evaluation; Mass Spectrometry; Peptides*; Quality control and metrics; Tandem Mass Spectrometry
    DOI:  https://doi.org/10.1074/mcp.TIR118.001209
  20. Mol Cell Proteomics. 2021 Jan 11. pii: S1535-9476(20)35149-5. [Epub ahead of print] 100035
    Geyer PE, Mann SP, Treit PV, Mann M.
      The goal of clinical proteomics is to identify, quantify, and characterize proteins in body fluids or tissue to assist diagnosis, prognosis, and treatment of patients. In this way, it is similar to more mature omics technologies, such as genomics, that are increasingly applied in biomedicine. We argue that, similar to those fields, proteomics also faces ethical issues related to the kinds of information that is inherently obtained through sample measurement, although their acquisition was not the primary purpose. Specifically, we demonstrate the potential to identify individuals both by their characteristic, individual-specific protein levels and by variant peptides reporting on coding single nucleotide polymorphisms. Furthermore, it is in the nature of blood plasma proteomics profiling that it broadly reports on the health status of an individual - beyond the disease under investigation. Finally, we show that private and potentially sensitive information, such as ethnicity and pregnancy status, can increasingly be derived from proteomics data. Although this is potentially valuable not only to the individual, but also for biomedical research, it raises ethical questions similar to the incidental findings obtained through other omics technologies. We here introduce the necessity of - and argue for the desirability for - ethical and human rights-related issues to be discussed within the proteomics community. Those thoughts are more fully developed in our accompanying manuscript. Appreciation and discussion of ethical aspects of proteomic research will allow for deeper, better-informed, more diverse, and, most importantly, wiser guidelines for clinical proteomics.
    DOI:  https://doi.org/10.1074/mcp.RA120.002359
  21. Mol Cell Proteomics. 2020 Jan;pii: S1535-9476(20)30014-1. [Epub ahead of print]19(1): 181-197
    Barkovits K, Pacharra S, Pfeiffer K, Steinbach S, Eisenacher M, Marcus K, Uszkoreit J.
      Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level. In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios. We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches. Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956.
    Keywords:  Bioinformatics software; Label-free quantification; Mass Spectrometry; Quantification; Target identification; data-independent acquisition (DIA); peptide identification; proteomics; spectral library
    DOI:  https://doi.org/10.1074/mcp.RA119.001714
  22. Anal Chem. 2021 Jan 12.
    Zhang Y, Gao B, Valdiviez L, Zhu C, Gallagher T, Whiteson K, Fiehn O.
      Stable isotope tracers are applied for in vivo and in vitro studies to reveal the activity of enzymes and intracellular metabolic pathways. Most often, such tracers are used with gas chromatography coupled to mass spectrometry (GC-MS) owing to its ease of operation and reproducible mass spectral databases. Differences in isotope tracer performance of the classic GC-quadrupole MS instrument and newer time-of-flight instruments are not well studied. Here, we used three commercially available instruments for the analysis of identical samples from a stable isotope labeling study that used [U-13C6] d-glucose to investigate the metabolism of the bacterium Rothia mucilaginosa with respect to 29 amino acids and hydroxyl acids involved in primary metabolism. The prokaryote R. mucilaginosa belongs to the family of Micrococcaceae and is present and metabolically active in the airways and sputum of cystic fibrosis patients. Overall, all three GC-MS instruments (low-resolution GC-SQ MS, low-resolution GC-TOF MS, and high-resolution GC-QTOF MS) can be used to perform stable isotope tracing studies for glycolytic intermediates, tricarboxylic acid (TCA) metabolites, and amino acids, yielding similar biological results, with high-resolution GC-QTOF MS offering additional capabilities to identify the chemical structures of unknown compounds that might show significant isotope enrichments in biological studies.
    DOI:  https://doi.org/10.1021/acs.analchem.0c04013
  23. Mol Cell Proteomics. 2019 Jul;pii: S1535-9476(20)31548-6. [Epub ahead of print]18(7): 1410-1427
    Nguyen EV, Pereira BA, Lawrence MG, Ma X, Rebello RJ, Chan H, Niranjan B, Wu Y, Ellem S, Guan X, Wu J, Skhinas JN, Cox TR, Risbridger GP, Taylor RA, Lister NL, Daly RJ.
      In prostate cancer, cancer-associated fibroblasts (CAF) exhibit contrasting biological properties to non-malignant prostate fibroblasts (NPF) and promote tumorigenesis. Resolving intercellular signaling pathways between CAF and prostate tumor epithelium may offer novel opportunities for research translation. To this end, the proteome and phosphoproteome of four pairs of patient-matched CAF and NPF were characterized to identify discriminating proteomic signatures. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) with a hyper reaction monitoring data-independent acquisition (HRM-DIA) workflow. Proteins that exhibited a significant increase in CAF versus NPF were enriched for the functional categories "cell adhesion" and the "extracellular matrix." The CAF phosphoproteome exhibited enhanced phosphorylation of proteins associated with the "spliceosome" and "actin binding." STRING analysis of the CAF proteome revealed a prominent interaction hub associated with collagen synthesis, modification, and signaling. It contained multiple collagens, including the fibrillar types COL1A1/2 and COL5A1; the receptor tyrosine kinase discoidin domain-containing receptor 2 (DDR2), a receptor for fibrillar collagens; and lysyl oxidase-like 2 (LOXL2), an enzyme that promotes collagen crosslinking. Increased activity and/or expression of LOXL2 and DDR2 in CAF were confirmed by enzymatic assays and Western blotting analyses. Pharmacological inhibition of CAF-derived LOXL2 perturbed extracellular matrix (ECM) organization and decreased CAF migration in a wound healing assay. Further, it significantly impaired the motility of co-cultured RWPE-2 prostate tumor epithelial cells. These results indicate that CAF-derived LOXL2 is an important mediator of intercellular communication within the prostate tumor microenvironment and is a potential therapeutic target.
    Keywords:  Cancer biomarker(s); Cancer-associated fibroblasts; Fibroblasts; LOXL2; Non-malignant prostate fibroblasts; Phosphoproteome; Prostate cancer; Prostate cancer biomarkers; Tumor microenvironment*
    DOI:  https://doi.org/10.1074/mcp.RA119.001496
  24. Mol Cell Proteomics. 2019 Mar;pii: S1535-9476(20)31861-2. [Epub ahead of print]18(3): 594-605
    Lin Z, Wei L, Cai W, Zhu Y, Tucholski T, Mitchell SD, Guo W, Ford SP, Diffee GM, Ge Y.
      Determining changes in protein expression and post-translational modifications (PTMs) is crucial for elucidating cellular signal transduction and disease mechanisms. Conventional antibody-based approaches have inherent problems such as the limited availability of high-quality antibodies and batch-to-batch variation. Top-down mass spectrometry (MS)-based proteomics has emerged as the most powerful method for characterization and quantification of protein modifications. Nevertheless, robust methods to simultaneously determine changes in protein expression and PTMs remain lacking. Herein, we have developed a straightforward and robust top-down liquid chromatography (LC)/MS-based targeted proteomics platform for simultaneous quantification of protein expression and PTMs with high throughput and high reproducibility. We employed this method to analyze the sarcomeric subproteome from various muscle types of different species, which successfully revealed skeletal muscle heterogeneity and cardiac developmental changes in sarcomeric protein isoform expression and PTMs. As demonstrated, this targeted top-down proteomics platform offers an excellent 'antibody-independent' alternative for the accurate quantification of sarcomeric protein expression and PTMs concurrently in complex mixtures, which is generally applicable to different species and various tissue types.
    Keywords:  HPLC; Label-free quantification; Mass Spectrometry; Post-translational modifications*; Sarcomere; Tandem Mass Spectrometry; Top-down mass spectrometry
    DOI:  https://doi.org/10.1074/mcp.TIR118.001086
  25. Mol Cell Proteomics. 2019 Nov;pii: S1535-9476(20)31756-4. [Epub ahead of print]18(11): 2149-2164
    Hartel NG, Chew B, Qin J, Xu J, Graham NA.
      Protein methylation has been implicated in many important biological contexts including signaling, metabolism, and transcriptional control. Despite the importance of this post-translational modification, the global analysis of protein methylation by mass spectrometry-based proteomics has not been extensively studied because of the lack of robust, well-characterized techniques for methyl peptide enrichment. Here, to better investigate protein methylation, we compared two methods for methyl peptide enrichment: immunoaffinity purification (IAP) and high pH strong cation exchange (SCX). Using both methods, we identified 1720 methylation sites on 778 proteins. Comparison of these methods revealed that they are largely orthogonal, suggesting that the usage of both techniques is required to provide a global view of protein methylation. Using both IAP and SCX, we then investigated changes in protein methylation downstream of protein arginine methyltransferase 1 (PRMT1). PRMT1 knockdown resulted in significant changes to 127 arginine methylation sites on 78 proteins. In contrast, only a single lysine methylation site was significantly changed upon PRMT1 knockdown. In PRMT1 knockdown cells, we found 114 MMA sites that were either significantly downregulated or upregulated on proteins enriched for mRNA metabolic processes. PRMT1 knockdown also induced significant changes in both asymmetric dimethyl arginine (ADMA) and symmetric dimethyl arginine (SDMA). Using characteristic neutral loss fragmentation ions, we annotated dimethylarginines as either ADMA or SDMA. Through integrative analysis of methyl forms, we identified 18 high confidence PRMT1 substrates and 12 methylation sites that are scavenged by other non-PRMT1 arginine methyltransferases in the absence of PRMT1 activity. We also identified one methylation site, HNRNPA1 R206, which switched from ADMA to SDMA upon PRMT1 knockdown. Taken together, our results suggest that deep protein methylation profiling by mass spectrometry requires orthogonal enrichment techniques to identify novel PRMT1 methylation targets and highlight the dynamic interplay between methyltransferases in mammalian cells.
    Keywords:  Post-translational modifications; affinity proteomics; arginine; immunoaffinity; label-free quantification; lysine; methylation; strong cation exchange; systems biology; tandem mass spectrometry
    DOI:  https://doi.org/10.1074/mcp.RA119.001625
  26. Mol Cell Proteomics. 2019 Sep;pii: S1535-9476(20)31787-4. [Epub ahead of print]18(9): 1899-1915
    Harney DJ, Hutchison AT, Su Z, Hatchwell L, Heilbronn LK, Hocking S, James DE, Larance M.
      Unbiased and sensitive quantification of low abundance small proteins in human plasma (e.g. hormones, immune factors, metabolic regulators) remains an unmet need. These small protein factors are typically analyzed individually and using antibodies that can lack specificity. Mass spectrometry (MS)-based proteomics has the potential to address these problems, however the analysis of plasma by MS is plagued by the extremely large dynamic range of this body fluid, with protein abundances spanning at least 13 orders of magnitude. Here we describe an enrichment assay (SPEA), that greatly simplifies the plasma dynamic range problem by enriching small-proteins of 2-10 kDa, enabling the rapid, specific and sensitive quantification of >100 small-protein factors in a single untargeted LC-MS/MS acquisition. Applying this method to perform deep-proteome profiling of human plasma we identify C5ORF46 as a previously uncharacterized human plasma protein. We further demonstrate the reproducibility of our workflow for low abundance protein analysis using a stable-isotope labeled protein standard of insulin spiked into human plasma. SPEA provides the ability to study numerous important hormones in a single rapid assay, which we applied to study the intermittent fasting response and observed several unexpected changes including decreased plasma abundance of the iron homeostasis regulator hepcidin.
    Keywords:  Chromatography; Hormones*; Insulin resistance; Plasma or serum analysis; Serum/Plasma*; chemokine; hepcidin; insulin; intermittent fasting
    DOI:  https://doi.org/10.1074/mcp.TIR119.001562
  27. Mol Cell Proteomics. 2020 Jul;pii: S1535-9476(20)34974-4. [Epub ahead of print]19(7): 1088-1103
    Pino LK, Just SC, MacCoss MJ, Searle BC.
      Data independent acquisition (DIA) is an attractive alternative to standard shotgun proteomics methods for quantitative experiments. However, most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. In addition to working with non-human samples, studies of splice junctions, sequence variants, or simply working with small sample yields can make developing DDA-based spectrum libraries impractical. Here we illustrate how to acquire, queue, and validate DIA data without spectrum libraries, and provide a workflow to efficiently generate DIA-only chromatogram libraries using gas-phase fractionation (GPF). We present best-practice methods for collecting DIA data using Orbitrap-based instruments and develop an understanding for why DIA using an Orbitrap mass spectrometer should be approached differently than when using time-of-flight instruments. Finally, we discuss several methods for analyzing DIA data without libraries.
    Keywords:  DIA; Data evaluation; data independent acquisition; label-free quantification; mass spectrometry; protein identification; quantification
    DOI:  https://doi.org/10.1074/mcp.P119.001913
  28. Mol Cell Proteomics. 2020 Sep;pii: S1535-9476(20)35104-5. [Epub ahead of print]19(9): 1575-1585
    Yu F, Haynes SE, Teo GC, Avtonomov DM, Polasky DA, Nesvizhskii AI.
      Ion mobility brings an additional dimension of separation to LC-MS, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to TOF mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most used to analyze these data. However, because of the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (∼30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (∼10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 min on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation before MS/MS analysis.
    Keywords:  Bioinformatics; PASEF; algorithms; ion mobility; label-free quantification; protein identification; tandem mass spectrometry
    DOI:  https://doi.org/10.1074/mcp.TIR120.002048
  29. Mol Cell Proteomics. 2020 Jan;pii: S1535-9476(20)30009-8. [Epub ahead of print]19(1): 114-127
    Captur G, Heywood WE, Coats C, Rosmini S, Patel V, Lopes LR, Collis R, Patel N, Syrris P, Bassett P, O'Brien B, Moon JC, Elliott PM, Mills K.
      Hypertrophic cardiomyopathy (HCM) is defined by pathological left ventricular hypertrophy (LVH). It is the commonest inherited cardiac condition and a significant number of high risk cases still go undetected until a sudden cardiac death (SCD) event. Plasma biomarkers do not currently feature in the assessment of HCM disease progression, which is tracked by serial imaging, or in SCD risk stratification, which is based on imaging parameters and patient/family history. There is a need for new HCM plasma biomarkers to refine disease monitoring and improve patient risk stratification. To identify new plasma biomarkers for patients with HCM, we performed exploratory myocardial and plasma proteomics screens and subsequently developed a multiplexed targeted liquid chromatography-tandem/mass spectrometry-based assay to validate the 26 peptide biomarkers that were identified. The association of discovered biomarkers with clinical phenotypes was prospectively tested in plasma from 110 HCM patients with LVH (LVH+ HCM), 97 controls, and 16 HCM sarcomere gene mutation carriers before the development of LVH (subclinical HCM). Six peptides (aldolase fructose-bisphosphate A, complement C3, glutathione S-transferase omega 1, Ras suppressor protein 1, talin 1, and thrombospondin 1) were increased significantly in the plasma of LVH+ HCM compared with controls and correlated with imaging markers of phenotype severity: LV wall thickness, mass, and percentage myocardial scar on cardiovascular magnetic resonance imaging. Using supervised machine learning (ML), this six-biomarker panel differentiated between LVH+ HCM and controls, with an area under the curve of ≥ 0.87. Five of these peptides were also significantly increased in subclinical HCM compared with controls. In LVH+ HCM, the six-marker panel correlated with the presence of nonsustained ventricular tachycardia and the estimated five-year risk of sudden cardiac death. Using quantitative proteomic approaches, we have discovered six potentially useful circulating plasma biomarkers related to myocardial substrate changes in HCM, which correlate with the estimated sudden cardiac death risk.
    Keywords:  Cardiovascular Disease; Cardiovascular Function or Biology; Diagnostic; Mass Spectrometry; Multiple Reaction Monitoring
    DOI:  https://doi.org/10.1074/mcp.RA119.001586
  30. Mol Cell Proteomics. 2019 Aug 09. pii: S1535-9476(20)32756-0. [Epub ahead of print]18(8S1): S1-S4
    Zhang B, Kuster B.
      
    DOI:  https://doi.org/10.1074/mcp.E119.001693
  31. Mol Cell Proteomics. 2020 Jun;pii: S1535-9476(20)34990-2. [Epub ahead of print]19(6): 944-959
    Tsai TH, Choi M, Banfai B, Liu Y, MacLean BX, Dunkley T, Vitek O.
      In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.
    Keywords:  Statistics; bioinformatics; biostatistics; computational biology; label-free quantification; mass spectrometry; multiple reaction monitoring; quantification; selected reaction monitoring; targeted mass spectrometry
    DOI:  https://doi.org/10.1074/mcp.RA119.001792
  32. Mol Cell Proteomics. 2019 Sep;pii: S1535-9476(20)31782-5. [Epub ahead of print]18(9): 1836-1850
    Hüttenhain R, Choi M, Martin de la Fuente L, Oehl K, Chang CY, Zimmermann AK, Malander S, Olsson H, Surinova S, Clough T, Heinzelmann-Schwarz V, Wild PJ, Dinulescu DM, Niméus E, Vitek O, Aebersold R.
      Protein biomarkers for epithelial ovarian cancer are critical for the early detection of the cancer to improve patient prognosis and for the clinical management of the disease to monitor treatment response and to detect recurrences. Unfortunately, the discovery of protein biomarkers is hampered by the limited availability of reliable and sensitive assays needed for the reproducible quantification of proteins in complex biological matrices such as blood plasma. In recent years, targeted mass spectrometry, exemplified by selected reaction monitoring (SRM) has emerged as a method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing plasma-based protein biomarkers for epithelial ovarian cancer and illustrate how the SRM platform, when combined with rigorous experimental design and statistical analysis, can result in detection of predictive analytes. Our biomarker development strategy first involved a discovery-driven proteomic effort to derive potential N-glycoprotein biomarker candidates for plasma-based detection of human ovarian cancer from a genetically engineered mouse model of endometrioid ovarian cancer, which accurately recapitulates the human disease. Next, 65 candidate markers selected from proteins of different abundance in the discovery dataset were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive a 5-protein signature for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.
    Keywords:  Cancer biomarker(s); ovarian cancer; plasma or serum analysis; quantification; selected reaction monitoring; statistics; targeted mass spectrometry
    DOI:  https://doi.org/10.1074/mcp.RA118.001221
  33. Mol Cell Proteomics. 2020 Oct;pii: S1535-9476(20)35114-8. [Epub ahead of print]19(10): 1706-1723
    Huang T, Choi M, Tzouros M, Golling S, Pandya NJ, Banfai B, Dunkley T, Vitek O.
      Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.
    Keywords:  Mass spectrometry; TMT; bioinformatics software; hypothesis testing; mathematical modeling; multiple mixtures; protein quantification; quantification; statistics
    DOI:  https://doi.org/10.1074/mcp.RA120.002105
  34. Mol Cell Proteomics. 2019 May;pii: S1535-9476(20)31606-6. [Epub ahead of print]18(5): 968-981
    Lapek JD, Jiang Z, Wozniak JM, Arutyunova E, Wang SC, Lemieux MJ, Gonzalez DJ, O'Donoghue AJ.
      Proteolysis is an integral component of life and has been implicated in many disease processes. To improve our understanding of peptidase function, it is imperative to develop tools to uncover substrate specificity and cleavage efficiency. Here, we combine the quantitative power of tandem mass tags (TMTs) with an established peptide cleavage assay to yield quantitative Multiplex Substrate Profiling by Mass Spectrometry (qMSP-MS). This assay was validated with papain, a well-characterized cysteine peptidase, to generate cleavage efficiency values for hydrolysis of 275 unique peptide bonds in parallel. To demonstrate the breath of this assay, we show that qMSP-MS can uncover the substrate specificity of minimally characterized intramembrane rhomboid peptidases, as well as define hundreds of proteolytic activities in complex biological samples, including secretions from lung cancer cell lines. Importantly, our qMSP-MS library uses synthetic peptides whose termini are unmodified, allowing us to characterize not only endo- but also exo-peptidase activity. Each cleaved peptide sequence can be ranked by turnover rate, and the amino acid sequence of the best substrates can be used for designing fluorescent reporter substrates. Discovery of peptide substrates that are selectively cleaved by peptidases which are active at the site of disease highlights the potential for qMSP-MS to guide the development of peptidase-activating drugs for cancer and infectious disease.
    Keywords:  Lung cancer; Mass Spectrometry; Proteases*; Proteolysis*; Rhomboid; Secretome; Substrate profiling; Tandem mass tag
    DOI:  https://doi.org/10.1074/mcp.TIR118.001099
  35. Mol Cell Proteomics. 2019 Jul;pii: S1535-9476(20)31552-8. [Epub ahead of print]18(7): 1468-1478
    Zecha J, Satpathy S, Kanashova T, Avanessian SC, Kane MH, Clauser KR, Mertins P, Carr SA, Kuster B.
      Isobaric stable isotope labeling using, for example, tandem mass tags (TMTs) is increasingly being applied for large-scale proteomic studies. Experiments focusing on proteoform analysis in drug time course or perturbation studies or in large patient cohorts greatly benefit from the reproducible quantification of single peptides across samples. However, such studies often require labeling of hundreds of micrograms of peptides such that the cost for labeling reagents represents a major contribution to the overall cost of an experiment. Here, we describe and evaluate a robust and cost-effective protocol for TMT labeling that reduces the quantity of required labeling reagent by a factor of eight and achieves complete labeling. Under- and overlabeling of peptides derived from complex digests of tissues and cell lines were systematically evaluated using peptide quantities of between 12.5 and 800 μg and TMT-to-peptide ratios (wt/wt) ranging from 8:1 to 1:2 at different TMT and peptide concentrations. When reaction volumes were reduced to maintain TMT and peptide concentrations of at least 10 mm and 2 g/l, respectively, TMT-to-peptide ratios as low as 1:1 (wt/wt) resulted in labeling efficiencies of > 99% and excellent intra- and interlaboratory reproducibility. The utility of the optimized protocol was further demonstrated in a deep-scale proteome and phosphoproteome analysis of patient-derived xenograft tumor tissue benchmarked against the labeling procedure recommended by the TMT vendor. Finally, we discuss the impact of labeling reaction parameters for N-hydroxysuccinimide ester-based chemistry and provide guidance on adopting efficient labeling protocols for different peptide quantities.
    Keywords:  Labeling Efficiency; NHS Ester Chemistry; Peptides*; Phosphoproteome; Post-Translational Modifications*; Quantification; Stable Isotope Labeling; Tandem Mass Spectrometry; Tandem Mass Tags
    DOI:  https://doi.org/10.1074/mcp.TIR119.001385
  36. Cells. 2021 Jan 07. pii: E89. [Epub ahead of print]10(1):
    Kim H, Oh B, Park-Min KH.
      Bone is a dynamic tissue and is constantly being remodeled by bone cells. Metabolic reprogramming plays a critical role in the activation of these bone cells and skeletal metabolism, which fulfills the energy demand for bone remodeling. Among various metabolic pathways, the importance of lipid metabolism in bone cells has long been appreciated. More recent studies also establish the link between bone loss and lipid-altering conditions-such as atherosclerotic vascular disease, hyperlipidemia, and obesity-and uncover the detrimental effect of fat accumulation on skeletal homeostasis and increased risk of fracture. Targeting lipid metabolism with statin, a lipid-lowering drug, has been shown to improve bone density and quality in metabolic bone diseases. However, the molecular mechanisms of lipid-mediated regulation in osteoclasts are not completely understood. Thus, a better understanding of lipid metabolism in osteoclasts can be used to harness bone cell activity to treat pathological bone disorders. This review summarizes the recent developments of the contribution of lipid metabolism to the function and phenotype of osteoclasts.
    Keywords:  cholesterol; fatty acids; lipids; metabolism; osteoclasts; statin
    DOI:  https://doi.org/10.3390/cells10010089
  37. Metabolites. 2021 Jan 08. pii: E41. [Epub ahead of print]11(1):
    Rangholia N, Leisner TM, Holly SP.
      The primacy of lipids as essential components of cellular membranes is conserved across taxonomic domains. In addition to this crucial role as a semi-permeable barrier, lipids are also increasingly recognized as important signaling molecules with diverse functional mechanisms ranging from cell surface receptor binding to the intracellular regulation of enzymatic cascades. In this review, we focus on ether lipids, an ancient family of lipids having ether-linked structures that chemically differ from their more prevalent acyl relatives. In particular, we examine ether lipid biosynthesis in the peroxisome of mammalian cells, the roles of selected glycerolipids and glycerophospholipids in signal transduction in both prokaryotes and eukaryotes, and finally, the potential therapeutic contributions of synthetic ether lipids to the treatment of cancer.
    Keywords:  alkylglycerol; apoptosis; cancer; ether lipid; glycerolipid; glycerophospholipid; platelet; signal transduction; signaling
    DOI:  https://doi.org/10.3390/metabo11010041