bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2019‒09‒29
thirty-one papers selected by
Giovanny Rodriguez Blanco
The Beatson Institute for Cancer Research


  1. Cancer Res. 2019 Sep 27. pii: canres.0644.2019. [Epub ahead of print]
    McGregor GH, Campbell AD, Fey SK, Tumanov S, Sumpton D, Rodriguez Blanco G, Mackay G, Nixon C, Vazquez A, Sansom OJ, Kamphorst JJ.
      Statins are widely prescribed inhibitors of the mevalonate pathway, acting to lower systemic cholesterol levels. The mevalonate pathway is critical for tumorigenesis and is frequently upregulated in cancer. Nonetheless, reported effects of statins on tumor progression are ambiguous, making it unclear if statins, alone or in combination, can be used for chemotherapy. Here, using advanced mass spectrometry and isotope tracing, we showed that statins only modestly affected cancer cholesterol homeostasis. Instead, they significantly reduced synthesis and levels of another downstream product, the mitochondrial electron carrier coenzyme Q, both in cultured cancer cells and tumors. This compromised oxidative phosphorylation, causing severe oxidative stress. To compensate, cancer cells upregulated antioxidant metabolic pathways, including reductive carboxylation, proline synthesis, and cystine import. Targeting cystine import with an xCT transporter-lowering MEK inhibitor, in combination with statins, caused profound tumor cell death. Thus, statin-induced ROS production in cancer cells can be exploited in a combinatorial regimen.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-19-0644
  2. Metabolites. 2019 Sep 23. pii: E200. [Epub ahead of print]9(10):
    Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S.
      Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
    Keywords:  CRAN; NMR spectroscopy; R; bioconductor; compound identification; data integration; feature selection; lipidomics; mass Spectrometry; metabolite networks; metabolomics; signal processing; statistical data analysis
    DOI:  https://doi.org/10.3390/metabo9100200
  3. Expert Rev Mol Med. 2019 Sep 27. 21 e4
    Unterlass JE, Curtin NJ.
      Warburg and coworkers' observation of altered glucose metabolism in tumours has been neglected for several decades, which, in part, was because of an initial misinterpretation of the basis of their finding. Following the realisation that genetic alterations are often linked to metabolism, and that the tumour micro-environment imposes different demands on cancer cells, has led to a reinvestigation of cancer metabolism in recent years. Increasing our understanding of the drivers and consequences of the Warburg effect in cancer and beyond will help to identify new therapeutic strategies as well as to identify new prognostic and therapeutic biomarkers. Here we discuss the initial findings of Warburg and coworkers regarding cancer cell glucose metabolism, how these studies came into focus again in recent years following the discovery of metabolic oncogenes, and the therapeutic potential that lies within targeting the altered metabolic phenotype in cancer. In addition, another essential nutrient in cancer metabolism, glutamine, will be discussed.
    Keywords:  Cancer metabolism; Warburg effect; glucose metabolism; glutamine metabolism; isocitrate dehydrogenase; phosphoglycerate dehydrogenase
    DOI:  https://doi.org/10.1017/erm.2019.4
  4. Front Oncol. 2019 ;9 848
    Pupo E, Avanzato D, Middonti E, Bussolino F, Lanzetti L.
      Tumors driven by mutant KRAS are among the most aggressive and refractory to treatment. Unfortunately, despite the efforts, targeting alterations of this GTPase, either directly or by acting on the downstream signaling cascades, has been, so far, largely unsuccessful. However, recently, novel therapeutic opportunities are emerging based on the effect that this oncogenic lesion exerts in rewiring the cancer cell metabolism. Cancer cells that become dependent on KRAS-driven metabolic adaptations are sensitive to the inhibition of these metabolic routes, revealing novel therapeutic windows of intervention. In general, mutant KRAS fosters tumor growth by shifting cancer cell metabolism toward anabolic pathways. Depending on the tumor, KRAS-driven metabolic rewiring occurs by up-regulating rate-limiting enzymes involved in amino acid, fatty acid, or nucleotide biosynthesis, and by stimulating scavenging pathways such as macropinocytosis and autophagy, which, in turn, provide building blocks to the anabolic routes, also maintaining the energy levels and the cell redox potential (1). This review will discuss the most recent findings on mutant KRAS metabolic reliance in tumor models of pancreatic and non-small-cell lung cancer, also highlighting the role that these metabolic adaptations play in resistance to target therapy. The effects of constitutive KRAS activation in glycolysis elevation, amino acids metabolism reprogramming, fatty acid turnover, and nucleotide biosynthesis will be discussed also in the context of different genetic landscapes.
    Keywords:  KRAS; NSCLC; PDAC; gluocose metabolism in cancer; glycolysis; metabolic adaptability in cancer; metabolic rewiring
    DOI:  https://doi.org/10.3389/fonc.2019.00848
  5. Front Oncol. 2019 ;9 825
    Lindell Jonsson E, Erngren I, Engskog M, Haglöf J, Arvidsson T, Hedeland M, Petterson C, Laurell G, Nestor M.
      Head and neck squamous cell carcinoma (HNSCC) is the sixth most common form of cancer worldwide. Radiotherapy, with or without surgery, represents the major approach to curative treatment. However, not all tumors are equally sensitive to irradiation. It is therefore of interest to apply newer system biology approaches (e.g., metabolic profiling) in squamous cancer cells with different radiosensitivities in order to provide new insights on the mechanisms of radiation response. In this study, two cultured HNSCC cell lines from the same donor, UM-SCC-74A and UM-SCC-74B, were first genotyped using Short Tandem Repeat (STR), and assessed for radiation response by the means of clonogenic survival and growth inhibition assays. Thereafter, cells were cultured, irradiated and collected for subsequent metabolic profiling analyses using liquid chromatography-mass spectrometry (LC-MS). STR verified the similarity of UM-SCC-74A and UM-SCC-74B cells, and three independent assays proved UM-SCC-74B to be clearly more radioresistant than UM-SCC-74A. The LC-MS metabolic profiling demonstrated significant differences in the intracellular metabolome of the two cell lines before irradiation, as well as significant alterations after irradiation. The most important differences between the two cell lines before irradiation were connected to nicotinic acid and nicotinamide metabolism and purine metabolism. In the more radiosensitive UM-SCC-74A cells, the most significant alterations after irradiation were linked to tryptophan metabolism. In the more radioresistant UM-SCC-74B cells, the major alterations after irradiation were connected to nicotinic acid and nicotinamide metabolism, purine metabolism, the methionine cycle as well as the serine, and glycine metabolism. The data suggest that the more radioresistant cell line UM-SCC-74B altered the metabolism to control redox-status, manage DNA-repair, and change DNA methylation after irradiation. This provides new insights on the mechanisms of radiation response, which may aid future identification of biomarkers associated with radioresistance of cancer cells.
    Keywords:  mass spectrometry; metabolomics; radioresistance; radiosensitivity; redox status
    DOI:  https://doi.org/10.3389/fonc.2019.00825
  6. Cancer Discov. 2019 Sep 25. pii: CD-19-0329. [Epub ahead of print]
    Wang L, Babikir H, Muller S, Yagnik G, Shamardani K, Catalan F, Kohanbash G, Alvarado B, Di Lullo E, Kriegstein A, Shah S, Wadhwa H, Chang SM, Philips JJ, Aghi MK, Diaz AA.
      Although tumor-propagating cells can be derived from glioblastomas (GBMs) of the proneural and mesenchymal subtypes, a glioma stem-like cell (GSC) of the classical subtype has not been identified. It is unclear if mesenchymal GSCs (mGSCs) and/or proneural GSCs (pGSCs) alone are sufficient to generate the heterogeneity observed in GBM. We performed single-cell/nuclei RNA-sequencing of 28 gliomas, and single-cell ATAC-sequencing for 8 cases. We find that GBM GSCs reside on a single axis of variation, ranging from proneural to mesenchymal. In silico lineage tracing using both transcriptomics and genetics supports mGSCs as the progenitors of pGSCs. Dual inhibition of pGSC-enriched and mGSC-enriched growth and survival pathways provides a more complete treatment than combinations targeting one GSC phenotype alone. This study sheds light on a long-standing debate regarding lineage relationships among GSCs and presents a paradigm by which personalized combination therapies can be derived from single-cell RNA signatures, to overcome intra-tumor heterogeneity.
    DOI:  https://doi.org/10.1158/2159-8290.CD-19-0329
  7. Anal Chem. 2019 Sep 26.
    Shi X, Wang S, Jasbi P, Turner C, Hrovat J, Wei Y, Liu J, Gu H.
      Targeted mass spectrometry (MS) is an important measurement approach in metabolomics with strong analytical performance given its specificity, sensitivity, and quantitative capacity. However, traditional targeted-MS relies heavily on chemical standards for the development of various detection panels and, thus, its metabolite coverage is often limited to those well-known and commercially available compounds. To address this fundamental gap, we [Gu et al. Anal. Chem. 2015, 87, 12355-62] previously developed a novel approach, globally optimized targeted (GOT)-MS, which enables reliable metabolic analysis with broad coverage using a single triple quadrupole instrument. In the present study, we further developed and optimized an innovative targeted MS approach, database assisted globally optimized targeted (dGOT)-MS, which utilizes the HMDB and METLIN databases to significantly improve both identification and metabolite coverage. As it is well-known, these metabolomics databases have a comprehensive collection of metabolites and their tandem MS spectra; therefore, in this study multiple reaction monitoring transitions (MRMs) were directly obtained from the databases and, after optimizing MS parameters for those MRMs, 927 metabolites were measured from a plasma aqueous extract sample with high reliability by dGOT-MS. Of these, 310 were confirmed using pure chemical standards while the rest were annotated by identification level using database entries. Furthermore, using breast cancer diagnosis as a proof-of-principle metabolomics application, we showed dGOT-MS to significantly outperform a traditional large-scale targeted MS assay for potential biomarker discovery. In principle, dGOT-MS is able to cover all metabolites (including lipids) that have been characterized in these comprehensive metabolomics databases from various types of biological samples. Therefore, dGOT-MS opens a novel avenue for MS measurements and may play an important role in many future metabolomics studies.
    DOI:  https://doi.org/10.1021/acs.analchem.9b03107
  8. Anal Chim Acta. 2019 Dec 04. pii: S0003-2670(19)30971-7. [Epub ahead of print]1086 90-102
    Drotleff B, Illison J, Schlotterbeck J, Lukowski R, Lämmerhofer M.
      Lipidomics has gained rising attention in recent years. Several strategies for lipidomic profiling have been developed, with targeted analysis of selected lipid species, typically utilized for lipid quantification by low-resolution triple quadrupole MS/MS, and untargeted analysis by high-resolution MS instruments, focusing on hypothesis generation for prognostic, diagnostic and/or disease-relevant biomarker discovery. The latter methodologies generally yield relative quantification data with limited inter-assay comparability. In this work we aimed to combine untargeted analysis and absolute quantification to enhance data quality and to obtain independent results for optimum comparability to previous studies or database entries. For the lipidomic analysis of mouse plasma, RP-UHPLC hyphenated to a high-resolution quadrupole TOF mass spectrometer in comprehensive data-independent SWATH acquisition mode was employed. This way, quantifiable data on the MS and the MS/MS level were recorded, which increases assay specificity and quantitative performance. Due to the lack of an appropriate blank matrix for untargeted lipidomics, we herein established a sophisticated strategy for lipid class-specific calibration with stable isotope labeled standards (surrogate calibrants). LLOQs were in the range between 10 and 50 ng mL-1 for LPC, LPE, PI, PS, PG, SM, PC, PE, DAG) or 100-700 ng mL-1 (MAG, TAG), except for cholesterol and CE (1-20 μg mL-1). Acceptable values for accuracy and precision well below ±15% bias were reached for the majority of surrogate calibrants. However, to achieve sufficient accuracy for target lipids, response factors to corresponding surrogate calibrants are required. An approach to estimate response factors via a standard reference material (NIST SRM 1950) was therefore conducted. Furthermore, a useful workflow for post-acquisition re-calibration, involving response factor determination and iteratively built libraries, is suggested. In comparison to single-point calibration, the presented surrogate calibrant method was shown to yield results with improved accuracy that are largely in accordance with standard addition. Quantitative results of real samples (high-fat diet vs control diet) were then compared to two previously published dietary mouse plasma studies that provided absolute lipid levels and showed similar trends.
    Keywords:  Data-independent acquisition; Lipidomics; Obesity; SWATH; Surrogate calibration; Untargeted quantification
    DOI:  https://doi.org/10.1016/j.aca.2019.08.030
  9. Cells. 2019 Sep 20. pii: E1113. [Epub ahead of print]8(10):
    Gkiouli M, Biechl P, Eisenreich W, Otto AM.
      In cancers, tumor cells are exposed to fluctuating nutrient microenvironments with limiting supplies of glucose and glutamine. While the metabolic program has been related to the expression of oncogenes, only fractional information is available on how variable precarious nutrient concentrations modulate the cellular levels of metabolites and their metabolic pathways. We thus sought to obtain an overview of the metabolic routes taken by 13C-glucose-derived metabolites in breast cancer MCF-7 cells growing in combinations of limiting glucose and glutamine concentrations. Isotopologue profiles of key metabolites were obtained by gas chromatography/mass spectrometry (GC/MS). They revealed that in limiting and standard saturating medium conditions, the same metabolic routes were engaged, including glycolysis, gluconeogenesis, as well as the TCA cycle with glutamine and pyruvate anaplerosis. However, the cellular levels of 13C-metabolites, for example, serine, alanine, glutamate, malate, and aspartate, were highly sensitive to the available concentrations and the ratios of glucose and glutamine. Notably, intracellular lactate concentrations did not reflect the Warburg effect. Also, isotopologue profiles of 13C-serine as well as 13C-alanine show that the same glucose-derived metabolites are involved in gluconeogenesis and pyruvate replenishment. Thus, anaplerosis and the bidirectional flow of central metabolic pathways ensure metabolic plasticity for adjusting to precarious nutrient conditions.
    Keywords:  13C-metabolites; TCA cycle; Warburg effect; anaplerosis; breast cancer cells; gluconeogenesis; glycolysis; isotopologue distribution; nutrient deprivation
    DOI:  https://doi.org/10.3390/cells8101113
  10. J Steroid Biochem Mol Biol. 2019 Sep 24. pii: S0960-0760(19)30090-1. [Epub ahead of print] 105476
    Laforest S, Pelletier M, Denver N, Poirier B, Nguyen S, Walker BR, Durocher F, Homer NZM, Diorio C, Tchernof A, Andrew R.
      The presence of estrogens, androgens and glucocorticoids as well as their receptors and steroid converting enzymes in adipose tissue has been established. Their contribution to diseases such as obesity, diabetes and hormone-dependent cancers is an active area of research. Our objective was to develop a LC-MS/MS method to quantify bioactive estrogens and glucocorticoids simultaneously in human adipose tissue. Estrogens and glucocorticoids were extracted from adipose tissue samples using solid-phase extraction. Estrogens were derivatized using 1-(5-fluoro-2,4-dinitrophenyl)-4-methylpiperazine (PPZ) and methyl iodide to generate a permanently charged molecule (MPPZ). Steroids were separated and quantified by LC-MS/MS. The limit of quantitation for the steroids was between 15 and 100 pg per sample. Accuracy and precision were acceptable (< 20%). Using this method, estradiol, estrone, cortisone and cortisol were quantified in adipose tissue from women with and without breast cancer. This novel assay of estrogens and glucocorticoids by LC-MS/MS coupled with derivatization allowed simultaneous quantification of a panel of steroids in human adipose tissue across the endogenous range of concentrations encountered in health and disease.
    Keywords:  adipose.; cortisol; cortisone; derivatization; estradiol; estrone
    DOI:  https://doi.org/10.1016/j.jsbmb.2019.105476
  11. Cells. 2019 Sep 24. pii: E1141. [Epub ahead of print]8(10):
    Lagies S, Bork T, Kaminski MM, Troendle K, Zimmermann S, Huber TB, Walz G, Lienkamp SS, Kammerer B.
      Diabetic kidney disease is a major complication in diabetes mellitus, and the most common reason for end-stage renal disease. Patients suffering from diabetes mellitus encounter glomerular damage by basement membrane thickening, and develop albuminuria. Subsequently, albuminuria can deteriorate the tubular function and impair the renal outcome. The impact of diabetic stress conditions on the metabolome was investigated by untargeted gas chromatography-mass spectrometry (GC-MS) analyses. The results were validated by qPCR analyses. In total, four cell lines were tested, representing the glomerulus, proximal nephron tubule, and collecting duct. Both murine and human cell lines were used. In podocytes, proximal tubular and collecting duct cells, high glucose concentrations led to global metabolic alterations in amino acid metabolism and the polyol pathway. Albumin overload led to the further activation of the latter pathway in human proximal tubular cells. In the proximal tubular cells, aldo-keto reductase was concordantly increased by glucose, and partially increased by albumin overload. Here, the combinatorial impact of two stressful agents in diabetes on the metabolome of kidney cells was investigated, revealing effects of glucose and albumin on polyol metabolism in human proximal tubular cells. This study shows the importance of including highly concentrated albumin in in vitro studies for mimicking diabetic kidney disease.
    Keywords:  GC-MS; albumin stress; diabetic complication; diabetic kidney disease; diabetic nephropathy; metabolomics; podocyte; polyol metabolism; tubule
    DOI:  https://doi.org/10.3390/cells8101141
  12. Methods Mol Biol. 2020 ;2051 115-132
    Henneman A, Palmblad M.
      In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in bottom-up proteomics.
    Keywords:  Liquid chromatography; Mass spectrometry; Peptide; Prediction; Protein identification; Retention time
    DOI:  https://doi.org/10.1007/978-1-4939-9744-2_4
  13. Proc Natl Acad Sci U S A. 2019 Sep 23. pii: 201911992. [Epub ahead of print]
    Körner A, Zhou E, Müller C, Mohammed Y, Herceg S, Bracher F, Rensen PCN, Wang Y, Mirakaj V, Giera M.
      Targeting metabolism through bioactive key metabolites is an upcoming future therapeutic strategy. We questioned how modifying intracellular lipid metabolism could be a possible means for alleviating inflammation. Using a recently developed chemical probe (SH42), we inhibited distal cholesterol biosynthesis through selective inhibition of Δ24-dehydrocholesterol reductase (DHCR24). Inhibition of DHCR24 led to an antiinflammatory/proresolving phenotype in a murine peritonitis model. Subsequently, we investigated several omics layers in order to link our phenotypic observations with key metabolic alterations. Lipidomic analysis revealed a significant increase in endogenous polyunsaturated fatty acid (PUFA) biosynthesis. These data integrated with gene expression analysis, revealing increased expression of the desaturase Fads6 and the key proresolving enzyme Alox-12/15 Protein array analysis, as well as immune cell phenotype and functional analysis, substantiated these results confirming the antiinflammatory/proresolving phenotype. Ultimately, lipid mediator (LM) analysis revealed the increased production of bioactive lipids, channeling the observed metabolic alterations into a key class of metabolites known for their capacity to change the inflammatory phenotype.
    Keywords:  PUFA; cholesterol; desmosterol; inflammation resolution; lipid mediator
    DOI:  https://doi.org/10.1073/pnas.1911992116
  14. Methods Mol Biol. 2020 ;2051 161-197
    Matthiesen R, Carvalho AS.
      Protein quantitation by mass spectrometry has always been a resourceful technique in protein discovery, and more recently it has leveraged the advent of clinical proteomics. A single mass spectrometry analysis experiment provides identification and quantitation of proteins as well as information on posttranslational modifications landscape. By contrast, protein array technologies are restricted to quantitation of targeted proteins and their modifications. Currently, there are an overwhelming number of quantitative mass spectrometry methods for protein and peptide quantitation. The aim here is to provide an overview of the most common mass spectrometry methods and algorithms used in quantitative proteomics and discuss the computational aspects to obtain reliable quantitative measures of proteins, peptides and their posttranslational modifications. The development of a pipeline using commercial or freely available software is one of the main challenges in data analysis of many experimental projects. Recent developments of R statistical programming language make it attractive to fully develop pipelines for quantitative proteomics. We discuss concepts of quantitative proteomics that together with current R packages can be used to build highly customizable pipelines.
    Keywords:  Label-free quantitation; Liquid chromatography; Mass spectrometry; Peptide quantitation; Protein quantitation; Stable isotope labeling
    DOI:  https://doi.org/10.1007/978-1-4939-9744-2_7
  15. Methods Mol Biol. 2020 ;2051 373-387
    Čuklina J, Pedrioli PGA, Aebersold R.
      Systematic technical variation in high-throughput studies consisting of the serial measurement of large sample cohorts is known as batch effects. Batch effects reduce the sensitivity of biological signal extraction and can cause significant artifacts. The systematic bias in the data caused by batch effects is more common in studies in which logistical considerations restrict the number of samples that can be prepared or profiled in a single experiment, thus necessitating the arrangement of subsets of study samples in batches. To mitigate the negative impact of batch effects, statistical approaches for batch correction are used at the stage of experimental design and data processing. Whereas in genomics batch effects and possible remedies have been extensively discussed, they are a relatively new challenge in proteomics because methods with sufficient throughput to systematically measure through large sample cohorts have only recently become available. Here we provide general recommendations to mitigate batch effects: we discuss the design of large-scale proteomic studies, review the most commonly used tools for batch effect correction and overview their application in proteomics.
    Keywords:  Batch effects; Experimental design; Quantitative proteomics; Statistical analysis
    DOI:  https://doi.org/10.1007/978-1-4939-9744-2_16
  16. Methods Mol Biol. 2020 ;2051 145-159
    Prieto G, Vázquez J.
      Shotgun proteomics is the method of choice for large-scale protein identification. However, the use of a robust statistical workflow to validate such identification is mandatory to minimize false matches, ambiguities, and amplification of error rates from spectra to proteins. In this chapter we emphasize the key concepts to take into account when processing the output of a search engine to obtain reliable peptide or protein identifications. We assume that the reader is already familiar with tandem mass spectrometry so we can focus on the use of statistical confidence methods. After introducing the key concepts we present different software tools and how to use them with an example dataset.
    Keywords:  Bioinformatics; False discovery rate; Peptide identification; Protein inference; Shotgun proteomics; Target–decoy approach
    DOI:  https://doi.org/10.1007/978-1-4939-9744-2_6
  17. Methods Mol Biol. 2020 ;2051 1-58
    Matthiesen R, Bunkenborg J.
      Mass spectrometry, a technology to determine the mass of ionized molecules and biomolecules, is increasingly applied for the global identification and quantification of proteins. Proteomics applies mass spectrometry in many applications, and each application requires consideration of analytical choices, instrumental limitations and data processing steps. These depend on the aim of the study and means of conducting it. Choosing the right combination of sample preparation, MS instrumentation, and data processing allows exploration of different aspects of the proteome. This chapter gives an outline for some of these commonly used setups and some of the key concepts, many of which later chapters discuss in greater depth. Understanding and handling mass spectrometry data is a multifaceted task that requires many user decisions to obtain the most comprehensive information from an MS experiment. Later chapters in this book deal in-depth with various aspects of the process and how different tools addresses the many analytical challenges. This chapter revises the basic concept in mass spectrometry (MS)-based proteomics.
    Keywords:  Data formats; Mass spectrometry; Proteomics; Sample preparation
    DOI:  https://doi.org/10.1007/978-1-4939-9744-2_1
  18. Methods Mol Biol. 2020 ;2051 389-405
    Mohammed Y, Palmblad M.
      Scripting languages such as Python and Bash are appreciated for solving simple, everyday tasks in bioinformatics. A more recent, object-oriented command shell and scripting language, PowerShell, has many attractive features: an object-oriented interactive command line, fluent navigation and manipulation of XML files, ability to explore and consume Web services from the command line, consistent syntax and grammar, rich regular expressions, and advanced output formatting. The key difference between classical command shells and scripting languages, such as bash, and object-oriented ones, such as PowerShell, is that in the latter the result of a command is a structured object with inherited properties and methods rather than a simple stream of characters. Conveniently, PowerShell is included in all new releases of Microsoft Windows and is available for Linux and macOS, making any data processing script portable. In this chapter we demonstrate how PowerShell in particular allows easy interaction with mass spectrometry data in XML formats, connection to Web services for tools such as BLAST, and presentation of results as formatted text or graphics. These features make PowerShell much more than "yet another scripting language."
    Keywords:  Object-oriented scripting; PowerShell; Web services; XML parsing
    DOI:  https://doi.org/10.1007/978-1-4939-9744-2_17
  19. Nat Chem. 2019 Sep 23.
    Huang H, Banerjee S, Qiu K, Zhang P, Blacque O, Malcomson T, Paterson MJ, Clarkson GJ, Staniforth M, Stavros VG, Gasser G, Chao H, Sadler PJ.
      Hypoxic tumours are a major problem for cancer photodynamic therapy. Here, we show that photoredox catalysis can provide an oxygen-independent mechanism of action to combat this problem. We have designed a highly oxidative Ir(III) photocatalyst, [Ir(ttpy)(pq)Cl]PF6 ([1]PF6, where 'ttpy' represents 4'-(p-tolyl)-2,2':6',2''-terpyridine and 'pq' represents 3-phenylisoquinoline), which is phototoxic towards both normoxic and hypoxic cancer cells. Complex 1 photocatalytically oxidizes 1,4-dihydronicotinamide adenine dinucleotide (NADH)-an important coenzyme in living cells-generating NAD• radicals with a high turnover frequency in biological media. Moreover, complex 1 and NADH synergistically photoreduce cytochrome c under hypoxia. Density functional theory calculations reveal π stacking in adducts of complex 1 and NADH, facilitating photoinduced single-electron transfer. In cancer cells, complex 1 localizes in mitochondria and disrupts electron transport via NADH photocatalysis. On light irradiation, complex 1 induces NADH depletion, intracellular redox imbalance and immunogenic apoptotic cancer cell death. This photocatalytic redox imbalance strategy offers a new approach for efficient cancer phototherapy.
    DOI:  https://doi.org/10.1038/s41557-019-0328-4
  20. Nat Commun. 2019 Sep 24. 10(1): 4329
    Tabassum R, Rämö JT, Ripatti P, Koskela JT, Kurki M, Karjalainen J, Palta P, Hassan S, Nunez-Fontarnau J, Kiiskinen TTJ, Söderlund S, Matikainen N, Gerl MJ, Surma MA, Klose C, Stitziel NO, Laivuori H, Havulinna AS, Service SK, Salomaa V, Pirinen M, , Jauhiainen M, Daly MJ, Freimer NB, Palotie A, Taskinen MR, Simons K, Ripatti S.
      Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P <5 ×10-8), 10 of which associate with CVD risk including five new loci-COL5A1, GLTPD2, SPTLC3, MBOAT7 and GALNT16 (false discovery rate<0.05). We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD.
    DOI:  https://doi.org/10.1038/s41467-019-11954-8
  21. Methods Mol Biol. 2020 ;2051 79-114
    Rockwood AL, Palmblad M.
      Isotopic information determined by mass spectrometry can be used in a wide variety of applications. Broadly speaking these could be classified as "passive" applications, meaning that they use naturally occurring isotopic information, and "active" applications, meaning that the isotopic distributions are manipulated in some way. The classic passive application is the determination of chemical composition by comparing observed isotopic patterns of molecules to theoretically calculated isotopic patterns. Active applications include isotope exchange experiments of a variety of types, as well as isotope labeling in tracing studies and to provide references for quantitation. Regardless of the type of application considered, the problem of theoretical calculation of isotopic patterns almost invariably arises. This chapter reviews a number of application examples and computational approaches for isotopic studies in mass spectrometry.
    Keywords:  Isotopes; Isotopic distributions; Mass spectrometry
    DOI:  https://doi.org/10.1007/978-1-4939-9744-2_3
  22. Methods Mol Biol. 2020 ;2051 277-295
    Mohan SV, Nayakanti DS, Sathe G, George IA, Gowda H, Kumar P.
      Mass spectrometry based proteomics approaches are routinely used to discover candidate biomarkers. These studies often use small number of samples to discover candidate proteins that are later validated on a large cohort of samples. Targeted proteomics has emerged as a powerful method for quantification of multiple proteins in complex biological matrix. Parallel reaction monitoring (PRM) and selected reaction monitoring (SRM) are two main methods of choice for quantifying and validating proteins across hundreds to thousands of samples. Over the years, many software tools have become available that enable the users to carry out the analysis. In this chapter, we describe selection of proteotypic peptides, sample preparation, generating a response curve, data acquisition and analysis of PRM data using Skyline software for targeted proteomics to quantify candidate markers in urine.
    Keywords:  PRM; Parallel reaction monitoring; Skyline; Targeted proteomics; Urine
    DOI:  https://doi.org/10.1007/978-1-4939-9744-2_12
  23. Int J Mol Sci. 2019 Sep 25. pii: E4751. [Epub ahead of print]20(19):
    Kim N, Kang MS, Nam M, Kim SA, Hwang GS, Kim HS.
      EPA, an omega-3 polyunsaturated fatty acid, exerts beneficial effects on human health. However, the molecular mechanisms underlying EPA function are poorly understood. The object was to illuminate molecular mechanism underlying EPA's role. Here, 1H-NMR-based metabolic analysis showed enhanced branched-chain amino acids (BCAAs) and lactate following EPA treatment in skeletal muscle cells. EPA regulated mitochondrial oxygen consumption rate. Furthermore, EPA induced calcium/calmodulin-dependent protein kinase kinase (CaMKK) through the generation of intracellular calcium. This induced the phosphorylation of AMP-activated protein kinase (AMPK) and p38 mitogen-activated protein kinase (p38 MAPK) that led to glucose uptake, and the translocation of glucose transporter type 4 (GLUT4) in muscles. In conclusion, EPA exerts benign effects on glucose through the activation of AMPK-p38 MAPK signaling pathways in skeletal muscles.
    Keywords:  AMPK; EPA; GLUT4; oxygen consumption
    DOI:  https://doi.org/10.3390/ijms20194751
  24. Cells. 2019 Sep 25. pii: E1149. [Epub ahead of print]8(10):
    Ni Y, Hagras MA, Konstantopoulou V, Mayr JA, Stuchebrukhov AA, Meierhofer D.
      Complex I (CI) is the first enzyme of the mitochondrial respiratory chain and couples the electron transfer with proton pumping. Mutations in genes encoding CI subunits can frequently cause inborn metabolic errors. We applied proteome and metabolome profiling of patient-derived cells harboring pathogenic mutations in two distinct CI genes to elucidate underlying pathomechanisms on the molecular level. Our results indicated that the electron transfer within CI was interrupted in both patients by different mechanisms. We showed that the biallelic mutations in NDUFS1 led to a decreased stability of the entire N-module of CI and disrupted the electron transfer between two iron-sulfur clusters. Strikingly interesting and in contrast to the proteome, metabolome profiling illustrated that the pattern of dysregulated metabolites was almost identical in both patients, such as the inhibitory feedback on the TCA cycle and altered glutathione levels, indicative for reactive oxygen species (ROS) stress. Our findings deciphered pathological mechanisms of CI deficiency to better understand inborn metabolic errors.
    Keywords:  complex I (CI) deficiency; electron tunneling (ET); metabolome and proteome profiling; reactive oxygen species (ROS); respirasome assembly
    DOI:  https://doi.org/10.3390/cells8101149
  25. Methods Mol Biol. 2020 ;2051 199-230
    Bunkenborg J, Matthiesen R.
      Tandem mass spectrometry provides a sensitive means of analyzing the amino acid sequence of peptides and modified peptides by providing accurate mass measurements of precursor and fragment ions. Modern mass spectrometry instrumentation is capable of rapidly generating many thousands of tandem mass spectra and protein database search engines have been developed to match the experimental data to peptide candidates. In most studies there is a schism between discarding perfectly valid data and including nonsensical peptide identifications-this is currently managed by establishing a false discovery rate (FDR) but for modified peptides it calls for an understanding of tandem mass spectrometry data. Manual evaluation of the data and perhaps experimental cross-checking of the MS data can save many months of experimental work trying to do biological follow-ups based on erroneous identifications. Especially for posttranslationally modified peptides there is a need for careful consideration of the data because search algorithms seldom have been optimized for the identification of modified peptides and because there are many pitfalls for the unwary. This chapter describes some of the issues that should be considered when interpreting and validating tandem mass spectra and gives some useful tables to aid in this process.
    Keywords:  Database searching; Mass spectrometry; Posttranslational modifications; Proteomics
    DOI:  https://doi.org/10.1007/978-1-4939-9744-2_8
  26. Cancer Discov. 2019 Sep 27.
      Restoration of p53 led to an accumulation of αKG in mouse pancreatic cancer cells.
    DOI:  https://doi.org/10.1158/2159-8290.CD-RW2019-148
  27. Methods Mol Biol. 2020 ;2051 345-371
    Walzer M, Vizcaíno JA.
      In any analytical discipline, data analysis reproducibility is closely interlinked with data quality. In this book chapter focused on mass spectrometry-based proteomics approaches, we introduce how both data analysis reproducibility and data quality can influence each other and how data quality and data analysis designs can be used to increase robustness and improve reproducibility. We first introduce methods and concepts to design and maintain robust data analysis pipelines such that reproducibility can be increased in parallel. The technical aspects related to data analysis reproducibility are challenging, and current ways to increase the overall robustness are multifaceted. Software containerization and cloud infrastructures play an important part.We will also show how quality control (QC) and quality assessment (QA) approaches can be used to spot analytical issues, reduce the experimental variability, and increase confidence in the analytical results of (clinical) proteomics studies, since experimental variability plays a substantial role in analysis reproducibility. Therefore, we give an overview on existing solutions for QC/QA, including different quality metrics, and methods for longitudinal monitoring. The efficient use of both types of approaches undoubtedly provides a way to improve the experimental reliability, reproducibility, and level of consistency in proteomics analytical measurements.
    Keywords:  Cloud technology; Computational mass spectrometry; Large scale data analysis; Quality control approaches; Reproducible analysis pipelines
    DOI:  https://doi.org/10.1007/978-1-4939-9744-2_15
  28. Methods Mol Biol. 2020 ;2051 59-77
    Matthiesen R.
      Peak extraction from raw data is the first step in LC-MS data analysis. The quality of this procedure can have dramatic effects on the quality and accuracy of all subsequent data analysis steps such as database searches and peak quantitation. The most important and most accurately measured physical entity provided by mass spectrometers is m/z. Peak processing algorithms must extract m/z values unaffected from overlapping peaks to avoid confusing downstream algorithms. The aim of this chapter is to provide a discussion of peak processing methods and furthermore discuss some of the yet unresolved or neglected issues. The chapter mainly discusses possible software developed in R for spectra processing and free software to generate Mascot generic files (mgf-see Chapter 1 ).
    Keywords:  Decharging; Deisotoping; Noise filtering; Peak extraction
    DOI:  https://doi.org/10.1007/978-1-4939-9744-2_2
  29. Mol Syst Biol. 2019 Sep;15(9): e8994
    Mendes ML, Fischer L, Chen ZA, Barbon M, O'Reilly FJ, Giese SH, Bohlke-Schneider M, Belsom A, Dau T, Combe CW, Graham M, Eisele MR, Baumeister W, Speck C, Rappsilber J.
      We present a concise workflow to enhance the mass spectrometric detection of crosslinked peptides by introducing sequential digestion and the crosslink identification software xiSEARCH. Sequential digestion enhances peptide detection by selective shortening of long tryptic peptides. We demonstrate our simple 12-fraction protocol for crosslinked multi-protein complexes and cell lysates, quantitative analysis, and high-density crosslinking, without requiring specific crosslinker features. This overall approach reveals dynamic protein-protein interaction sites, which are accessible, have fundamental functional relevance and are therefore ideally suited for the development of small molecule inhibitors.
    Keywords:  crosslinking mass spectrometry; protein-protein interactions; proteomics; software; structural biology
    DOI:  https://doi.org/10.15252/msb.20198994
  30. Methods Mol Biol. 2020 ;2051 241-264
    Deb B, George IA, Sharma J, Kumar P.
      Phosphorylation is one of the most extensively studied posttranslational modifications (PTM), which regulates cellular functions like cell growth, differentiation, apoptosis, and cell signaling. Kinase families cover a wide number of oncoproteins and are strongly associated with cancer. Identification of driver kinases is an intense area of cancer research. Thus, kinases serve as the potential target to improve the efficacy of targeted therapies. Mass spectrometry-based phosphoproteomic approach has paved the way to the identification of a large number of altered phosphorylation events in proteins and signaling cascades that may lead to oncogenic processes in a cell. Alterations in signaling pathways result in the activation of oncogenic processes predominantly regulated by kinases and phosphatases. Therefore, drugs such as kinase inhibitors, which target dysregulated pathways, represent a promising area for cancer therapy.
    Keywords:  Cancer signaling; Mass spectrometry; Molecular therapeutics; Targeted therapy
    DOI:  https://doi.org/10.1007/978-1-4939-9744-2_10