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(1)AGH Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydział: Inżynierii Materiałowej i Ceramiki Katedra: Biochemii i Neurobiologii. Rozprawa doktorska. Quantitative analysis of phosphoproteome of the selected brain structures in morphine dependence. Filip Sucharski. Promotor : Prof. dr hab. Jerzy Silberring. Kraków 2012.

(2) AGH University of Science and Technology. Doctoral Thesis. Quantitative analysis of phosphoproteome of the selected brain structures in morphine dependence. Filip Sucharski Under the Supervision of Professor Jerzy Silberring. Department of Biochemistry and Neurobiology Faculty of Materials Science and Ceramics AGH University of Science and Technology 30 Mickiewicza Av., 30-059 Cracow.

(3) This work was supported by “Doctus – Lesser Poland fellowship program for PhD students” (2009-2012). Additional support was provided by Cracow City Council’s fellowship for the best PhD students (2010) and “Lesser Poland Doctoral Fellowship” from voivodship marshal (2009)..

(4) Acknowledgements. First of all, I would like to thank Professor Jerzy Silberring, for giving me the opportunity to participate in the research, a large number of advices that he gave to me and invaluable help in preparing this manuscript I would like to thank Doctor Marek Noga, for great help in programming scripts and tons of emails we have exchanged to find out details about files structure, data analysis and other strange stuff. Without Marek’s help I wouldn’t have successfully finish this project. I would like to thank as well, all of the members of Biochemistry and Neurobiology Department: Anna Bodzoń – Kułakowska, Anna Drabik, Magdalena Niedziółka, Piotr Suder, Hana Raoof, Marek Smoluch and Przemek Mielczarek for their help and friendly atmosphere which they create. Moreover, I would like to thank Knud Nairz, Bernd Bodenmiller and Paola Picotti from ETH Zurich and University of Zurich for teaching me phosphoproteomics and quantitative mass spectrometry and for showing me some tips and tricks..

(5) 1.. INDEX OF ABBREVIATIONS. 8. 2.. THEORETICAL PART. 9. 2.1.. Introduction. 2.2.. Process of addiction – a global view. 10. 2.3.. Opioid receptors. 13. 2.4.. Morphine. 15. 9. 2.5. Molecular mechanism of morphine action 2.5.1. Protein phosphorylation as an adaptive process to drug dependence. 16 18. 2.6.. 21. Phosphorylation. 2.7. Identification of phosphoproteome 2.7.1. Enrichment of intact phosphoproteins 2.7.2. Enrichment of phosphopeptides 2.7.2.1. Affinity enrichment 2.7.2.1.1. Immobilized Metal Affinity Chromatography (IMAC) 2.7.2.1.2. Titanium Dioxide (TiO2) 2.7.2.2. Chemical derivatization 2.7.2.3. Charge-based fractionation 2.7.3. Mass Spectrometry 2.7.3.1. Electron transfer dissociation (ETD) for phosphoproteomics 2.7.3.2. Mass spectrometry-based identification 2.7.3.2.1. Mascot 2.7.3.2.2. PeptideProphet (within TPP) 2.7.3.3. Mass spectrometry-based quantitation 2.7.3.3.1. Stable-isotope labeling 2.7.3.3.2. Label-free quantitation. 22 23 23 24 24 25 27 28 28 31 31 33 35 37 38 40. 3.. AIMS OF WORK. 42. 4.. MATERIALS AND METHODS. 43. 4.1. Sample preparation 4.1.1. Experiments on animals 4.1.2. Protein extraction 4.1.3. Isotopic labeling 4.1.4. Phosphopeptides isolation 4.1.4.1. Titanium dioxide (TiO2) 4.1.4.2. Immobilized metal affinity chromatography (IMAC). 43 43 43 44 45 45 45. 4.2.. 46. LC-MS analysis. 4.3. Data analysis and bioinformatics 4.3.1. Raw data processing 4.3.2. mgf files conversion 4.3.3. Mascot database search 4.3.4. Trans-proteomic pipeline (TPP). 5. 5.1.. EXPERIMENTAL PART Experimental details. 46 47 48 48 49. 52 52.

(6) 5.2.. 6.. Data analysis. RESULTS. 55. 57. 6.1.. Analysis of Hippocampus. 57. 6.2.. Analysis of Striatum. 58. 6.3.. Analysis of Prefrontal cortex. 59. 6.4.. Simultaneous analysis of all of the isolated brain structures. 60. 7.. DISCUSSION. 62. 7.1.. Sample preparation. 62. 7.2.. Stable isotope labeling. 63. 7.3.. Phosphopeptides enrichment. 64. 7.4.. LC-MS/MS analysis. 64. 7.5.. Data analysis and bioinformatics. 65. 7.6.. Conclusions on biological results. 66. 7.6.1.. Regulated proteins – in depth analysis. 67. 7.6.2.. Regulated proteins – the global view. 69. 8.. REFERENCES. 71. 9.. APPENDIX. 76. 9.1. Modified Data Analysis script “Combine CID and ETD.m” for extracting and combining CID/ETD peak list. 76 9.2.. Python script “mgfDA2TPP.py”. 78. 9.3.. Hippocampus – the first day of the morphine treatment. 82. 9.4.. Hippocampus – the third day of the morphine treatment. 84. 9.5.. Hippocampus – the fifth day of the morphine treatment. 87. 9.6.. Hippocampus – the seventh day of the morphine treatment. 90. 9.7.. Striatum – the first day of the morphine treatment. 93. 9.8.. Striatum – the third day of the morphine treatment. 95. 9.9.. Striatum – the fifth day of the morphine treatment. 98. 9.10.. Striatum – the seventh day of the morphine treatment. 100. 9.11.. PFC – the first day of the morphine treatment. 102.

(7) 9.12.. PFC – the third day of the morphine treatment. 105. 9.13.. PFC– the fifth day of the morphine treatment. 107. 9.14.. PFC – the seventh day of the morphine treatment. 110. 9.15.. Analysis of all of the isolated brain structures in the first day of the treatment. 111. 9.16.. Analysis of all of the isolated brain structures in the third day of the treatment. 115. 9.17.. Analysis of all of the isolated brain structures in the fifth day of the treatment. 118. 9.18.. Analysis of all of the isolated brain structures in the seventh day of the treatment. 120. 10.. ABSTRACT. 122. 11.. ABSTRAKT. 123.

(8) INDEX OF ABBREVIATIONS _________________________________________________________________________________________________________________. 1. INDEX OF ABBREVIATIONS. 2DE – Two dimensional electrophoresis ACN – Acetonitrile CID – Collision induced dissociation CNS – Central nervous system Da – Dalton DAMGO- ([D-Ala2, N-MePhe4, Gly-ol]-enkephalin) a synthetic opioid peptide with high µopioid receptor specificity DHB - 2,5-dihydroxybenzoic acid DIPPL - Differential phosphoproteins labeling ESI – Electrospray ionization ETD – Electron transfer dissociation FT-ICR - Fourier transform ion cyclotron resonance HPLC – High performance liquid chromatography IMAC - Immobilized metal affinity chromatography LC-MS – Liquid chromatography coupled with mass spectrometry mgfDA2TPP.py – Name of the python script for .mgf files conversion MeOH – Methanol MS – Mass spectrometry MS/MS – Tandem mass spectrometry MSn – Tandem mass spectrometry with n isolation/fragmentation steps MS3-NL – Neutral loss triggered MS3 fragmentation nanoLC – Nano-flow liquid chromatography PAC - Phosphoramidate chemistry pSer, pThr, pTyr – Phosphorylated serine, threonine, tyrosine residues PTM – Post-translational modification PBS – Phosphate buffered saline SCX - Strong cation exchanger TFA – Trifluoroacetic acid TPP – Trans-proteomics pipeline. -8-.

(9) THEORETICAL PART _________________________________________________________________________________________________________________. 2. THEORETICAL PART 2.1. Introduction In the 21st century health care became one of the fundamentals of modern civilization. Nowadays governments need to face sociological problems of drugs abuse and bear the costs, which are estimated to be at the level of billions of US dollars. Almost in every country certain legislation has been developed to outlaw different types of drug use. These so-called “controlled substances” are strictly licensed and their unrestricted production, distribution and possession, and use are illegal. Even for simple possession a death penalty can be sentenced in some countries. Modern views have focused on three types of drug use: occasional, controlled or social use, drug abuse, and drug addiction. Drug addiction, also referred to as a substance dependence, is a chronically relapsing disorder that is characterized by: (1) compulsion to seek and take the drug, (2) loss of control in limiting intake, and (3) emergence of negative emotional state (e.g. dysphoria, anxiety, irritability), when access to the drug is prevented [68]. The occasional but limited use of an abusive drug is clinically different from escalated drug use, loss of control over limiting drug intake, and the emergence of chronic compulsive drug-seeking that characterizes addiction. The drug abuse is a term, which generally means a repetitive use of abusive substance despite the evident harm to the user. Although the above definitions are well settled in precepts of the law, there is still an ongoing discussion how a given substance should be classified. What constitutes a “drug” is debatable. For instance GHB (gamma-hydroxybutyric acid), or cannabinoids - naturally occurring substances in CNS are considered as illegal drugs, whereas nicotine (with high addictive potential) is not considered as a drug.. -9-.

(10) THEORETICAL PART _________________________________________________________________________________________________________________. 2.2. Process of addiction – a global view Historically, definitions of addiction began with theory of physical dependence. Eventually this concept evolved into definition of dependence that also describes psychic dependence to capture behavioral aspects of addiction and emphasize processes, where no physical signs were observed (e.g. gambling). Modern definitions of addiction resemble a combination of physical and psychic dependence with more emphasis focused on the motivational aspects of withdrawal, rather than physical symptoms of withdrawal only. Drug abuse is a much more complex phenomenon than previously thought, and patterns leading to dependence are diverse, due to the fact that drug abusers represent a highly heterogeneous group. Differences such as temperament, social development, comorbidity, protective factors and genetics are fields of intensive research. A reasonable assumption is that the initiation of drug abuse is more associated with social and environmental factors, whereas development of addiction is connected with neurobiological factors. Personality and temperament have been identified as causes of vulnerability to drug abuse and may cover behavioral activation, novelty- or sensation-seeking [68]. Some of comorbid psychiatric disorders like mood disorders, anxiety disorders, antisocial personality disorders or ADHD have been strongly associated with drug abuse [70]. Generally, a relatively high percentage of abusers suffer from different comorbid psychiatric disorders [68] (data from the International Consortium in Psychiatric Epidemiology). Genetic contributions to addiction can result in complex differences that range from alleles that control drug metabolism to hypothesized genetic control over drug sensitivity and environmental influences. Studies have shown that genetic factors can account for approximately 40% of the total variability of the phenotype. It should be noted that not only genetic and environmental factors can convey vulnerability, but they may also play a protective role against drug abuse. Some Asian populations missing one or more alleles coding for acetaldehyde dehydrogenase show significantly less vulnerability to alcoholism [69]. Taking into account the complexity of drug addiction and the fact that drug dependence is often associated with other disorders, it becomes extremely difficult to apprehend and explain molecular mechanisms of drug addiction. Numerous experiments have been conducted using animal models and functional brain imaging on humans in order to reveal the mechanisms underlying drug addiction. An important goal of current neurobiological research is to understand the neuropharmacological and neuroadaptive mechanisms within specific. - 10 -.

(11) THEORETICAL PART _________________________________________________________________________________________________________________. neurocircuits that mediate the transition between occasional, controlled drug use and the loss of behavioral control over drug-seeking and drug-taking that defines chronic addiction. Experiments performed on animals involving intracranial self-stimulation allowed to identify brain structures involved in the rewarding system. These structures are: ventral tegmental area in the midbrain (VTA), nucleus accumbens (Nac) at the base of the striatum in forebrain, and prefrontal cortex (PFC). All belong to the mesocorticolimbic dopamine system. More evidence is pointing towards the role of hippocampus in drug addiction because of its importance in learning and memory consolidation. Under normal conditions the reward system works as behavioral activation for eating, drinking and sexual behavior essential for species survival. Additionally, these brain structures could also be activated in an artificial way, with specific chemical substances that increase dopamine level several folds acting as a shortcut circuit. Different types of drugs influence mesolimbic system in many ways, but still inducing the reward effect. In general, many drugs of abuse engage several neurotransmitter pathways. Usually, an increased level of dopamine in brain structures related to the reward circuit, causes feeling of euphoria and this is believed to be the one of the reasons for drugs use (positive reinforcement). The experiment confirming the. above. assumption. is. dopamine depletion. by a. neurotoxin,. 6-. hydroxydopamine) in rats that resulted in the inhibition of self-administration of drugs of abuse [1]. This happens because no dopamine action is induced in the reward system, thus its reinforcing properties are lost. Another example that challenges this theory might be nicotine, with high addictive potential, but with almost no euphoric effect. The repeated exposure to drugs may intensify the euphoria. This phenomenon is called sensitization and is mostly observed at the beginning of drug use. As a consequence, addictive drugs become increasingly desired. Sensitization is also responsible for some persistent factors in the nervous system, which can cause return to the drug-taking habit even after a long time of abstinence [2]. When addictive drugs are present in the nervous system, they tend to change mechanisms in the reward system in such a way that motivation is oriented towards procuring the drug, more efficiently than natural rewards [3]. The natural biological stimuli, such as food, sex, or social interactions are less motivating than drug seeking. The transition from recreational drug use into addiction occurs gradually (Fig. 1.), and is modulated by the effect of neuroplasticity of the neurons found in the reward system.. - 11 -.

(12) THEORETICAL PART _________________________________________________________________________________________________________________. Figure 1. Stages of addiction to drugs of abuse. This mechanism involves formation of new synapses between communicating neurons as well as changes at molecular level, such as increased gene expression and altered cell signaling. On the way to full addiction, cravings are produced by release of dopamine in the prefrontal cortex. As process of addiction is progressing, the release of dopamine in the nucleus accumbens is unnecessary to produce cravings, while increased metabolic activity in orbitofrontal cortex plays this role. At the early stage of addiction, when drug-seeking behavior is exhibited, the neuroplasticity is still a reversible process. However, in advanced form it is irreversible. After a long exposure to drugs, neuronal circuits involved in addiction trigger development of tolerance to the effect of the drugs, which results in a need for a higher dose to maintain the same effect of pleasure and euphoria. This phenomenon can be caused by various mechanisms and is a result of neuroadaptive changes in response to chronic drug administration.. - 12 -.

(13) THEORETICAL PART _________________________________________________________________________________________________________________. The repeated use of some drugs may also increase activity of enzymes involved in their metabolism, which in turn may decrease their global concentration. The second mechanism is related to desensitization of receptors, or to the decrease in their number. Another effects of chronic drug use are withdrawal symptoms when administration of the drug is stopped. During drug stimulation there are some compensation changes in the system, which serve to maintain equilibrium by reducing drug effects. In the absence of a drug, this adaptation is unmasked and may produce opposite effects [4]. In case of opiates, withdrawal symptoms are related to the reduced levels of dopamine in the nucleus accumbens. As a result, psychological symptoms like anxiety, depression, dysphoria, anhedonia are observed. Strictly physical symptoms are reported as well: hypertension, diarrhea, muscle cramps, sweating, and fever.. 2.3. Opioid receptors Opioid receptors are a group of G protein-coupled receptors with opioids as ligands, and consist of seven transmembrane helical domains as it is shown in Figure 2. N-terminal fragment of the receptors is located at the extracellular side, whereas C-terminus is placed inside the cell. The N-terminal chain together with extracellular loops form a ligand binding site. The intracellular part of the receptor is responsible for the activation of other proteins, and finally triggers specific pathways in the second messenger system. Neurotransmitter. alpha helix. extracellular side. intracellular side. G-protein. Figure 2. G-protein coupled receptor. - 13 -.

(14) THEORETICAL PART _________________________________________________________________________________________________________________. The activation of the G-protein coupled receptor (GPCR) by a ligand (L) causes an exchange of GDP for GTP in the α subunit. This switches Gα to the active conformation and results in the dissociation of Gα and Gβγ from the receptor to signal to downstream effectors. The switch is turned off by intrinsic GTP hydrolysis (GTPase) activity of Gα, which leads to its reassociation with Gβγ and the receptor. The regulators of G-protein signaling (RGS) play a key role in inactivating G-protein signaling. The activators of G-protein signaling (AGS) activate G-proteins by several mechanisms, as it is shown in Figure 3. Pharmacological experiments proved existence of different types of opioids receptors. Their names were given according to the substances, which were used during the experiments. •. µ-type (mu, MOP, or OP3) shows highest affinity to morphine and is responsible for the rewarding effect of morphine and for phenomena, such as analgesia, hypothermia, respiratory depression, intestinal constipation, and meiosis.. •. κ-type (kappa, KOP or OP2) - (for ketocyclazocine), induces depression of flexor reflexes and sedation. Its agonists produce dysphoria and negative affective states caused by the activation of nucleus accumbens opioid receptors, and inhibition of dopamine release. This system is involved in regulation of basal dopamine release [5].. •. δ – type (delta, DOP or OP1) - (for DAMGO). is responsible for analgesia. Recent findings suggest its role in emotional reactivity during drug addiction because its antagonists do not cause somatic withdrawal syndrome.. •. NOP (OP4) (orphanin/nociceptin FQ receptor) – induces hyperalgesia, depression, anxiety, appetite and takes part in the development of tolerance to µ agonists. NOP belongs to the opioid receptors family due to its close structural similarity. However, its endogenous ligand nociceptin/orphanin FQ shows anti-opioid properties.. - 14 -.

(15) THEORETICAL PART _________________________________________________________________________________________________________________. L. Inactive. L. Gα GDP. Gα GDP. ββγγ. AGS. GTP. ββγγ. GDP. Active L. L. Gα GDP. RGS. Gα GDP. βγ. βγ. Pi Effectors. Figure 3. G-protein coupled receptor – ligand binding mechanism. L – ligand, Gα,βγ – G protein sub-units, AGS - activators of G-protein signaling, RGS – regulators of G-protein signaling, Pi – phosphate. 2.4. Morphine Morphine is the most abundant alkaloid found in opium, the dried milky juice from the unripe seed capsule of the poppy, the Papaver somniferum. (Fig. 4.).. Figure 4. Morphine structure. Morphine is an opiate analgesic and in clinical use it is regarded as the gold standard of analgesics used to relieve severe or agonizing pain. Similarly to other opioids, it acts directly on the central nervous system (CNS). At low doses it can suppress pain, cause euphoria, and. - 15 -.

(16) THEORETICAL PART _________________________________________________________________________________________________________________. relaxation. Additionally, morphine causes respiratory depression, affects body temperature and produces sweating and constipations.. 2.5. Molecular mechanism of morphine action Chronic exposure to opiates may lead to addiction, manifested by dependence, drug craving, and tolerance [6-9]. Significant effects of chronic use have been identified for many drugs at all levels: molecular, cellular, structural and functional. At the cellular level, effects can be induced via G protein-coupled receptors, among them the µ opioid receptor [10]. The addicted brain is distinct from the non-addicted brain as manifested by changes in brain metabolic activity, receptor density, gene expression, and responsiveness to environmental signals. It has been reported [11], that the repeated opiate administration alters gene expression in different brain regions of rodents. It is believed that this effect may contribute to changes in plasticity, associated with addictive behavior. It is known that morphine administration does not lead to major alterations in the gene expression of µ opioid receptors. It influences transcriptional regulation of proteins involved in µ opioid receptors trafficking, such as GRK2 as well as alters expression of other receptors, e.g. dopamine receptors, NMDA receptors, GABA(A) receptor and α(2A) adrenoreceptor. Previous studies [11,12] reported clusters of morphine-responsive genes. It was shown that single dose of morphine down-regulated genes involved in metabolic functions. Increased doses led to morphine tolerance and revealed up-regulation of genes that alter patterns of synaptic connectivity, such as arc or homer group proteins – especially ania-3 [11]. It should be noted that both gene expression and protein full-range profiling should be carried out to reveal discrete and fluctuating expression of the genes, and finally proteins such as the mentioned above gene clusters, transcription factors, or downstream kinases. Nowadays, there are few, already known cellular adaptation mechanisms to the repeated opiate exposure, such as regulation of cAMP signal transduction, rise in ERK activation, and changes in the rate of opioid receptor endocytosis. [13,14]. Initially opioids inhibit the functional activity of the cAMP pathway (indicated by cellular levels of cAMP and cAMPdependent protein phosphorylation). With continued opioid exposure, functional activity of cAMP pathway recovers, and eventually increases over the control levels, following removal of the opioid (Fig. 5.).. - 16 -.

(17) THEORETICAL PART _________________________________________________________________________________________________________________. Figure 5. Regulation of the cAMP pathway as a mechanism of opioid tolerance and dependence. (based on Koob et al. [68]).. Several studies show that the transcription factor cAMP response binding protein (CREB), has a role in the up-regulation of adenylyl cyclase and protein kinase A (PKA) [68]. CREB is acutely inhibited by opioids and increased during opioid withdrawal. Another transcription factor activated by chronic administration of opioids and other drugs of abuse is ∆FosB. Generally, overexpression of ∆FosB increases sensitivity to the rewarding effects of morphine. The observed effects of drug abuse on ∆FosB are stable for weeks and months (Fig. 6.), and it is therefore postulated that ∆FosB could be a specific molecular switch that helps to initiate and maintain a state of addiction [72]. The cellular specificity and synchronization of these processes is currently under research.. Figure 6. Activity levels of CREB and ∆FosB during chronic drug exposure. Based on Koob et al. [68].. - 17 -.

(18) THEORETICAL PART _________________________________________________________________________________________________________________. 2.5.1. Protein phosphorylation as an adaptive process to drug dependence It was reported, that a group of proteins, such as G protein receptor kinases (GRKs), βarrestins, and regulators of G protein signaling (RGS proteins), have influence on different aspects of opiate action by modulating signaling duration and desensitization of µ opioid receptors [6-9,11,15,16]. Moreover, G protein-coupled receptor (GPCR) signaling is strongly modulated by receptor-binding partners and by the multiprotein complexes that are formed under specific conditions. As an effect, β-arrestins (especially β-arrestin-2) or spinophilin may regulate signaling depending on other factors [17]. It has been shown that β-arrestins interact with a variety of different signaling molecules in the cytoplasm in an agonist dependent manner (Fig. 7.). Agonist exposure stimulates activation of GPCR, which leads to the dissociation of G-proteins into activated α subunit and βγ dimers and triggers the activation of various effectors, such as adenylate cyclase and phospholipase C. The agonistoccupied GPCR is phosphorylated by GRKs, leading to signal desensitization, binding of βarrestin to the activated, phosphorylated GPCR and subsequent endocytosis of the receptor. Moreover, β-arrestins act as endocytic adaptors, interacting with a number of endocytic proteins, including trafficking regulators such as clathrin and its adaptor AP2, GTPase, ARF6 and ARNO. They can also bind agonist-occupied β2-adrenergic receptor and Src, a tyrosine kinase involved in the Ras-dependent activation of MAPK pathways, and finally induce MAPK activation. β-arrestins act as signal transducers to guide signals from the cell membrane to the MAPK, p53 and NF-κB pathways through the formation of scaffolding complexes with signaling molecules such as Src, Raf-1. Akt, ERK1/2, JNK3, MDM2 and IκB, [71].. - 18 -.

(19) THEORETICAL PART _________________________________________________________________________________________________________________. Figure 7. Scheme for β-arrestin associated G-protein coupled receptor endocytosis. L – ligand, Gα,βγ – G protein sub-units, GRK2 – G protein receptor kinase 2, βArr – β arrestin, P – phosphorylation. Spinophilin is a phosphoprotein, containing two PKA phosphorylation sites, which interacts with few elements of the G protein-coupled receptor signaling network, protein phosphatase (PP1), RGS proteins, dopamine D2 receptors [18]. Moreover, it has recently been shown, that spinophilin can play a role in modulation of a α2-adrenergic action, by blocking association of GRK2 with receptor and therefore, by antagonizing β-arrestin-2 function [19].. - 19 -.

(20) THEORETICAL PART _________________________________________________________________________________________________________________. Figure 8. Molecular mechanisms of neuroadaptation to opioids. (based on GF. Koob, M. Le Moal [68]). As it was mentioned previously, one of the responses for chronic opiate exposure is an increase of extracellular signal-regulated kinases (ERK) signaling. This activation plays a role in regulation of neuronal function, including long-term synaptic plasticity. Additionally, ERK activation is an important puzzle in various models of learning and memory [20] Recent studies show that the ERK pathway is crucial mediator of striatal plasticity, and therefore may explain some of the long-lasting changes induced by drugs of abuse [21]. Thus, ERK phosphorylation is increased in the reward-related brain regions by drugs, including psychostimulants and morphine [22]. In this way, ERK is involved in the induction of longlasting behavioral responses to these drugs, such as psychomotor sensitization [23], conditioned place preference and cue-induced drug seeking [24].. - 20 -.

(21) THEORETICAL PART _________________________________________________________________________________________________________________. 2.6. Phosphorylation Biological activity of many proteins is regulated by post-translational modifications (PTMs). More than 200 different types of PTMs are known, of which one of the most widespread is phosphorylation. Protein phosphorylation is a key process regulating a large number of fundamental biochemical reactions in living organisms [27,28]. Phosphorylation can affect 3D structure and function of proteins in many ways, by e.g.: (1) modifying biological activity; (2) stabilizing their structure and making them susceptible or resistant to degradation; (3) promoting or inhibiting intracellular movements, and (4) initiating or disrupting proteinprotein interactions. Phosphorylation can affect substrate recognition, as well as complex formation and degradation. Because of that, phosphorylation has strong influence on fundamental cellular processes including metabolic maintenance, gene expression, cell division, signal transduction, cytoskeletal regulation, and apoptosis. Understandably, anomalous phosphorylation sometimes occurs as a result of e.g. mutations in kinases or phosphatases. Deregulation of protein kinase function can be caused by many factors and may change its activity what often triggers tumor formation. Therefore, kinase-signaling pathways are important primary targets in biomedical research. Such attempts already resulted in a discovery of few protein kinase inhibitors. It can be concluded that many mechanisms of response to chronic drugs administration, as well as many other biological processes are regulated by phosphorylation. It can be assumed that some of the phosphorylation sites are known, but they represent only a small fraction of the regulatory phosphorylation events in this system. This illustrates why it is extremely important to reveal, which sites underwent phosphorylation, and what the resulting effects are. It is noteworthy that this system not only includes directly connected kinase pathways such as ERK, but also phospho-dependent regulatory proteins like spinophilin and many others [18,19]. An important fact is that drug dependence has a huge impact not only on a function of the rewarding system but on many other systems yet not understood, e.g. psychological mechanisms of addiction. The reward system engages a variety of brain structures, cell types, receptors, and finally many molecular systems. Therefore, it is extremely important to map phosphoproteome with a global, quantitative approach. This can provide an insight into molecular signalling mechanisms and pathways, that so far have been unknown or were not linked to drug dependence.. - 21 -.

(22) THEORETICAL PART _________________________________________________________________________________________________________________. 2.7. Identification of phosphoproteome Since it was known how challenging phosphoproteins analysis is, many efforts have been spent over the time on the development of various analytical methods to fulfill this task. The key step in phosphoproteome analysis is the efficient isolation and enrichment of phosphoproteins from the whole lysate. The yield of isolation method should be high enough to allow for identification of low-abundant phosphoproteins. Furthermore, the dynamic range of concentrations in biological samples may differ by 7-10 orders of magnitude, and the relatively low abundant proteins are usually hidden among a number of proteins of higher abundance. Additionally, stoichiometry of phosphorylation is often low, and only a fraction of the expressed protein may be phosphorylated at a given time. Therefore, specific phosphoproteins may exist as the differently phosphorylated forms, with large variety of a number of phosphorylation sites. The main objectives of phosphoproteomic research are not only detection and identification of phosphoproteins but also quantification of phosphoproteins and mapping of phosphorylation sites. Numerous methods for phosphoproteome analysis have been developed in the past decade, such as 32P labeling, semi-specific electrophoresis gel staining (Pro-Q Diamond), and antibodies, but only mass spectrometry meets the requirements for the fast and thorough identification of the modified proteins. Therefore, in most of the studies a general, unified approach to analyze protein phosphorylation in complex samples remains the same (Fig. 9.). In the first step, intact phosphoproteins are enriched. This phase is required only if very low abundant phosphoproteins are being analyzed. In the second step, proteins are digested with proteolytic enzymes. The third step consists of separation of phosphopeptides from the non-phosphorylated species, which often is the most important step for successful identification and description of phosphoproteins. Finally, the enriched sample is analyzed by LC-MS, utilizing special LC settings and data acquisition mode.. - 22 -.

(23) THEORETICAL PART _________________________________________________________________________________________________________________. Figure 9. A general analysis scheme for identification of phosphoproteins.. 2.7.1. Enrichment of intact phosphoproteins In general, intact phosphoproteins can be enriched with several affinity methods. The most efficient are techniques based on antiphosphotyrosine antibodies and IMAC (Immobilized Metal Affinity Chromatography). First approach utilizes antiphosphotyrosine antibodies to enrich phosphoproteins from complex samples. The method combines high efficiency of enrichment with high specificity, and allows for qualitative and quantitative analysis [29, 30]. In contrast, phosphoserine and phosphothreonine antibodies are of rather poor affinity and low specificity. Therefore, successful application of this method was reported in a limited number of studies [31]. Second approach is based on the immobilized metal ion affinity chromatography – IMAC. It takes advantage of the affinity of phosphate groups to Fe3+ or Ga3+ - chelated stationary phases. This method may suffer from the low specificity, depending on the protein sequences. This is due to the high affinity of acidic groups (aspartic-, and glutamic acids) and electron donors, such as histidine to the beads.. 2.7.2. Enrichment of phosphopeptides In most of the studies, phosphopeptides enrichment is the first step where phosphorylated species are isolated from the non-phosphorylated forms. Therefore, this procedure becomes crucial for further, successful analysis of phosphorylation state. There is a large number of methods that can be employed for phosphopeptides enrichment, and in. - 23 -.

(24) THEORETICAL PART _________________________________________________________________________________________________________________. general, they can be classified in three major groups: affinity enrichment, chemical derivatization, and charge-based fractionation.. 2.7.2.1. Affinity enrichment. 2.7.2.1.1. Immobilized Metal Affinity Chromatography (IMAC) Immobilized metal affinity chromatography (IMAC) was introduced by Andersson and Porath in 1986 [38] and is a common strategy for selective enrichment of phosphopeptides. Depending on the aim of research, IMAC can be used for intact phosphoproteins as well as phosphopeptides isolation from the non-phosphorylated species. Generally, IMAC method is more efficient for phosphopeptides enrichment than for intact phosphoproteins. The principles of IMAC technique are based on the electrostatic interactions between negatively charged phosphopeptides and positively charged metal ions, mostly Fe3+, Ga3+, Al3+, Zr4+, which are bound to the column stationary phase (Fig. 10.).. Figure 10. IMAC – phosphate binding scheme. S/T/Y – Serine, Threonine, Tyrosine residues.. As it was mentioned in [40,42-46] the results, mostly selectivity, can be influenced by pH, ionic strength and organic compositions of solvents used. In general, phosphopeptides are bound to the beads in acidic conditions to ionize phosphate moieties, and eluted in the presence of PBS or under basic conditions using ammonia. However, application of PBS is strongly discouraged if the IMAC separation is followed by the ESI-MS analysis, because. - 24 -.

(25) THEORETICAL PART _________________________________________________________________________________________________________________. phosphates polymerize in the ESI ion source that causes formation of pyrophosphates and – eventually – system clogging. IMAC coupled to mass spectrometry has been successfully used for both off-line [42] and on-line [47,48] applications, and offers one of the highest sample recovery among the methods frequently used in phosphoproteome analysis. The major drawback of IMAC separations is low specificity in presence of acidic peptides. Such peptides have strong negative charge in acidic conditions during column loading, and they are easily bound to the positively charged beads. Another issue is high affinity of electron donors e.g. histidine to the beads. In addition, recovery of multiply phosphorylated peptides is significantly higher than monophosphorylated species. This appears to be related to the type of metal ion, column material, buffers [49], and bench protocol for loading and eluting. Prior modification of C-terminus and acidic residues of glutamate and aspartate to methylesters, as described by Ficarro et al. [42], can solve the above issues.. 2.7.2.1.2. Titanium Dioxide (TiO2) Another strategy for phosphopeptides purification, introduced by Pinkse et al. [52] is titanium dioxide (TiO2). It is successfully used as an alternative to IMAC [53-55]. The principles of this method are based on electrostatic interactions between negatively charged phosphopeptides and titanium lone electron pair, as it is shown in Figure 11.. Figure 11. Phosphate binding to titanium dioxide .. Generally, sample purification using titanium dioxide is based on its adsorption in highly acidic medium and elution by strong alkalization or competitive elution by phosphate (PBS). Idea of loading sample at low pH is caused by a need of fully ionized phosphate. - 25 -.

(26) THEORETICAL PART _________________________________________________________________________________________________________________. moieties of phosphopeptides, to allow interactions between those groups and titanium dioxide stationary phase. The major problem in loading step, which lowers the specificity of the method is frequent retention of strongly acidic peptides through their carboxylic groups, which are also well ionized under these conditions. To overcome this problem and to increase method specificity, the ionized acidic residues should be converted to their neutral forms. It can be done in two different ways. First solution utilizes difference in pK values of phosphoric acid and carboxyl groups. pK of phosphoric acid is 1.8 and pI of Glu and Asp, the acidic amino acids are 4.30 and 4.60, respectively, thus optimal pH of the loading buffer should be within the limits from 1.8 and at least less than 4.3. The pH of 0.1M acetic acid seems to be about 2.7-2.9, but as it was reported by Ficarro et al. [42] and Pinkse et al. [52] a large number of acidic peptides is retained either on TiO2 or IMAC under those conditions. Larsen et al. [57] proposed application of 0.1% TFA which pH is around 1.9, thus phosphates should still be well ionized, and carboxylic moieties should be neutral. As it was shown, specificity with 0.1% TFA was higher, although still non-phosphorylated species were observed [57]. Another approach introduced by Ficarro et al. [42] assumes esterification of carboxylic residues to methyl esters that do not interact with titanium stationary phase. However, as it was shown by Larsen et al. [57] this derivatization step, due to the reaction yield, may increase sample complexity and reduce sensitivity. Another problem with Omethyl esterification is partial deamidation, followed by methylation of Asn and Gln residues. These products complicate further data analysis significantly. One of the most important developments for enhancing selective enrichment of phosphopeptides based on titanium dioxide, was presented by Larsen et al. [57]. The procedure assumes loading peptides in a saturated solution of 2,5-dixydroxybenzoic acid (DHB). As it was proved, DHB efficiently increases specificity of isolation. The DHB effect can be explained by competition for binding sites on titanium dioxide between acidic peptides and DHB molecules. The presence of DHB at high molar excess in comparison to sample amount, allows for effective competition with non-phosphorylated peptides. Elution of phosphopeptides from titanium dioxide can be performed in two different modes, taking into consideration a possibility of application in either on-line or off-line systems. In the on-line system phosphopeptides are eluted by substantial increase of pH up to the values ranging from 9.0 to 10.5. Usually phosphopeptides are eluted with ammonium hydroxide. - 26 -.

(27) THEORETICAL PART _________________________________________________________________________________________________________________. In the off-line system the elution can also be performed with phosphate buffer (0.1M KH2PO4 –K2HPO4) as proposed by Kuroda et al. [60] or by 0.05M borate buffer (pH 8.0) as it was proposed by Sano et al. [61]. However, the presence of phosphate or borate buffer adds an additional step of desalting, prior to MS analysis.. 2.7.2.2. Chemical derivatization Because of the lack of highly specific method for phosphopeptides isolation much efforts have been made to develop approaches based on chemical derivatization. For this purpose unique organic chemistry was employed to modify specific residues. All of the chemical derivatization methods in phosphoproteomics are based on β-elimination reaction and following Michael’s addition reaction (Fig. 12.). This approach leads to phosphate elimination, resulting in dehydroalanine and dehydroaminobutyric acid, respectively from pSer, and pThr. There are few other methods based on β-elimination reaction.. Figure 12. β-elimination reaction and Michael’s addition.. First method, as it was reported by Knight et al., [35] assumes conversion of pSer and pThr residues into analogs of lysine what results in the new cleavage sites for a lysine-specific protease. Second method, described by Van der Veken et al., [36] is based on connecting biotin through acid-labile linker to dehydroalanine or dehydroaminobutyric acid, which come from β-elimination. Afterwards, samples are pulled down on the avidin resin beads, which is known for its very high affinity to biotin tags. In the last step biotin tags are hydrolyzed from peptides at acidic conditions, and subsequently analyzed by MS.. - 27 -.

(28) THEORETICAL PART _________________________________________________________________________________________________________________. Third method reported by Tao et al., [37] is based on coupling phosphopeptides to dendrimer or polyallylamine molecules, which is followed by cut-off filtration. In this approach phosphopeptides bound to macromolecules are separated from the nonphosphorylated species by simple filtration, which allows low mass molecules to penetrate the membrane, while high mass molecules stay in supernatant. One of the drawbacks of the methods based on chemical derivatization is that not only phosphate groups are lost during β-elimination reaction, but O-glycans are often affected too, resulting in false positives. Another disadvantage is a need for methylation that can result in decreased yield. Additional major drawback of chemical derivatization based on βelimination reaction is a fact that only phosphoserine-, and phosphothreonine- containing peptides can be analyzed.. 2.7.2.3. Charge-based fractionation Phosphate groups on phosphopeptides carry negative charge, which can be used for phosphopeptides isolation. The method is based on the assumption that two thirds of tryptic peptides have a net charge state of +2 at low pH. Phosphate group decreases a net charge of the entire tryptic phosphopeptide, and at low pH the net charge is reduced to +1. Nonphosphorylated peptides have higher affinity to SCX (Strong Cation Exchanger) stationary phase due to their net charge, therefore they are bound stronger, and as a result phosphopeptides elute first. This method suffers from a very low specificity.. 2.7.3. Mass Spectrometry Fast development of mass spectrometric methods in recent years has resulted in a number of specific applications for phosphoproteomics. In general, phosphorylation leads to a mass increment in comparison to the unmodified residue, and therefore it is easily detected by MS. Mass spectrometric analysis of phosphoproteins has specific challenges. First of all, it is not known a priori, which residues in a given protein are phosphorylated. Therefore, to map all phosphorylation sites in targeted protein, all peptides should be detected and analyzed (i.e. 100% protein sequence coverage). This task is more difficult than simple protein identification, where only one or few peptides are sufficient to annotate a protein in the database.. - 28 -.

(29) THEORETICAL PART _________________________________________________________________________________________________________________. Furthermore, phosphorylation changes a net charge of the generated peptides, which in turn, affects fragmentation behavior, thus complicating the analysis of the produced fragments. Finally, multiplicity of all possible phosphorylation sites, together with other modifications in a single peptide, drastically complicates assignment of the obtained spectra to sequence databases. The need to effectively describe phosphate on/off phenomena, which are key biological regulatory processes, is of major interest. Mass spectrometry techniques for phosphoproteomics have been developing vividly, and several of them have emerged recently as promising tools in analysis of complex samples. However, unlike standard proteomics, where proteins analysis by MS is mostly a routine, phosphoproteomics requires specific approaches and remains a challenging approach. In comparison, mass spectrometry-based techniques can easily provide accurate information on the molecular mass of phosphorylated proteins or peptides, depending on the method. These data, supported by calculations, and in some cases by treatment of phosphoproteins with phosphatases, allow assigning the average number of phosphate groups. A detailed analysis of the phosphorylation sites by MS is based on further MS2 or MS3 analysis of peptide fragments generated by digestion of phosphoproteins by site-specific proteases. Phosphorylation can be measured in a wide range of mass spectrometers. The most popular are ESI-IT or ESI-Q-IT (particularly equipped with nano-electrospray source), and MALDI-TOF. The most useful and powerful instruments for phosphoproteomics applications are Orbitrap and FT-ICR, although the latter is less popular because of its high costs. Standard proteomic analysis by mass spectrometry can be performed either directly from unseparated peptide mixtures, or after separation by liquid chromatography. In case of phosphoproteome analysis, which is particularly demanding, the procedure is much more complex. Because of the low abundance of phosphopeptides in peptide maps (usually a few percent) there is a special need for their efficient separation prior to MS analysis. Particularly in phosphoproteomics the main role plays a nano-LC system coupled to ESI-IT mass spectrometer. It offers incomparable sensitivity, and, most important, is a less sample consuming technique. The most common way to investigate protein phosphorylation is detection of a mass difference of -98 Da in pSer, pThr and -80 Da in pTyr species. pTyr is rather stable, and phosphorylation state of Tyr residues should be done by database search or by use of the characteristic immonium ion of pTyr (m/z 216.043), which was suggested as a possible - 29 -.

(30) THEORETICAL PART _________________________________________________________________________________________________________________. marker for phosphotyrosine-containing peptides when using triple quadrupole configuration [65]. Moreover, observation of the mass difference of -80 Da does not directly indicate phosphorylation, as a very similar mass shift may be caused by sulfonation. The common property of the collision-induced dissociation (CID) in tandem mass spectrometry is neutral loss of phosphoric acid (-98 Da) in case of phosphoserine and phosphothreonine. Gas phase β-elimination leads to conversion of those amino acids to dehydroalanine and dehydroaminobutyric acid, respectively. In a first instance this kind of reaction is more preferable than typical cleavage of peptide's backbone [66]. Thus, it is the main reason why MS2 spectra of phosphopeptides are far less informative than for typical peptides. In case of phosphopeptides comparable information to MS2 spectra of a typical nonphosphorylated peptides can be achieved by MS3 fragmentation. It seems to be a yet another reason why phosphopeptides analysis is much more difficult. Acquiring MS3 spectra is more time consuming and every step of isolation in IT is decreasing the sensitivity. Furthermore, the peaks eluted from a nanoLC column are very narrow and a total analysis time for such component might not be sufficient to perform a complete MS3 run. To overcome this problem, a specific data acquisition method, named data-dependent MS3-NL scan, was developed. Using the MS3–NL scan, an MS3 spectrum is automatically collected by isolating and fragmenting the neutral loss fragment ion from the MS2. In fact, phosphopeptides analysis in mixtures with non-phosphorylated peptides remains difficult. Positive mode ionization of phosphorylated peptides is rather poor, especially in comparison to the non-phosphorylated species. Basically, the ionization efficiency of a peptide highly depends on its basicity, which is in turn, depends on the number of basic and acidic residues in the peptide sequence. The acidity of peptides dramatically increases after phosphorylation, resulting in a decreasing amount of protonated species in solution at low pH typically used in LC-MS. It is obvious that more pronounced phosphorylation implies worse protonation, hence the worse ionization and a more difficult detection. This problem can be partially solved by applying ionization in a negative ion mode, however performance of MS2 under those conditions is relatively poor, and the presently available software is not fully compatible with this mode [67]. Some mass spectrometers offer specific option for screening phosphopeptides in a negative ion mode where they are well visible, and then the fast switch to the positive ion mode where MS2 or MS3 is preformed. Unfortunately, in majority of cases of very complex biological samples, elution of the following peptides is so fast that switching, fragmenting, switching - 30 -.

(31) THEORETICAL PART _________________________________________________________________________________________________________________. back, and finally screening for other phosphopeptides is too slow and such analysis of complex samples is impossible.. 2.7.3.1. Electron transfer dissociation (ETD) for phosphoproteomics A new fragmentation method referred to as electron transfer dissociation (ETD) [66] has been introduced as a powerful tool for phosphopeptides analysis. The method utilizes chemical ionization to generate a radical anion, which transfers electrons to the multiply charged peptide cations trapped in the mass analyzer. ETD induces different pattern of fragmentation (c and z ion series) of the peptide backbone than CID (b and y ion series), providing better sequence coverage. In contrast to CID, no loss of phosphoric acid or phosphate from the parent peptide is observed, and therefore ETD is perfectly suited for phosphopeptides sequencing. The method allows for direct assignment of phosphorylation sites from MS2 data. Although ETD is a powerfull tool for single phosphopeptide sequencing, it is still much slower than CID. Therefore, in complex analysis, even with a long LC gradient, as long as 2-3 hours run might not be sufficient to fragment all of the eluting peptides. To overcome this problem a special hybrid CID-ETD data dependent modes were developed. In general, the system works in a full scan mode, and selects the most abundant peaks, which are directed to MS2 fragmentation (CID). If a desired neutral loss has appeared in the MS2 spectrum, the system isolates phosphopeptide and then performs ETD fragmentation. With this approach the user can take advantage of fast CID scans and highly informative ETD spectra.. 2.7.3.2. Mass spectrometry-based identification Most of the proteomics approaches utilize mass spectrometry to identify proteins of interest. Despite the fact, that modern instruments are able to measure masses of intact proteins and even protein complexes, the information about mass of a molecule is not sufficient for its unambiguous identification. Therefore, additional data are required to achieve a reliable identification of proteins. The most common approaches break molecule into fragments and identify the protein by measuring masses of its fragments. For this purpose two different approaches were developed. The “bottom-up” strategy utilizes proteolytic digestion of proteins of interest, creating unique peptide maps. Protein identification,. - 31 -.

(32) THEORETICAL PART _________________________________________________________________________________________________________________. quantification and mapping of phosphorylation sites is performed indirectly, utilizing MS spectra of peptides derived from protein digestion. The second approach termed “top-down” refers to direct MS analysis of intact proteins, which are fragmented in the mass spectrometer. Phosphorylation analysis of entire proteins revealing the overall state of whole protein is mostly done using FT-ICR or Orbitrap mass spectrometers. Such analysis require an extremely high resolution, mass accuracy, and special fragmentation capabilities. As the “bottom-up” strategy seems to be more robust for analysis of complex samples it is often a method of choice, and therefore a detailed description can be found in the next paragraphs.. Figure 13. Protein identification scheme in the bottom-up approach.. The “bottom-up” strategy of identification involves several steps at all stages of sample analysis. In general, investigated proteins are digested with proteolytic enzyme, resulting in creation of a unique peptide map. In the next step, m/z values of peptides are measured by a mass spectrometer. As specificity of the used enzyme is available, it is possible to predict theoretical peptide map of any protein whose sequence is known. Identification of a given protein is achieved by direct comparison of theoretical peptide maps generated for all sequences in the database with results of an experiment. In the last step of identification, special algorithm evaluates how well an experimental peptide map matches the theoretical data. To reflect how likely this specific match is a random event, special parameters are. - 32 -.

(33) THEORETICAL PART _________________________________________________________________________________________________________________. calculated. There are a few programs utilizing different algorithms for data analysis, the most commonly used are: Mascot, Sequest and X!Tandem. All of these differ in theoretical spectra generation, evaluation of an experimental/theoretical data match and parameters reflecting the results of a match. For some specific applications additional tools such as TPP might be used to recalculate and identify peptides/proteins with higher certainty.. 2.7.3.2.1. Mascot Mascot uses probability-based scoring, which enables for a simple judgment whether a result is significant or not. There are few parameters that Mascot calculates to show how the results are significant. The most important is the score parameter, which is the probability that the observed match is a random event. Mascot reports scores (Mowse score) as 10*LOG10(P), where P is the absolute probability. Thus, probability of 10-30 becomes a score of 300. Typically, all matches with probability below 5% are considered significant. The other important parameter is an expectation value, which is the number of matches with the scores equal or better that expected to occur by chance alone. The lower the expectation value, the more significant the score. Another group - next to the search results parameters are those, which need to be set prior to the Mascot database search. This group of factors strongly depends on the type of the measured sample, the instrument used for the analysis, and the type of mass spectrometric data. The most important are: •. Protein sequence database. •. Fixed and variable peptides modifications. •. Proteolytic enzyme used for proteins digestion. •. Maximum number of missed cleavages. •. Peptide charge. •. Peptide mass tolerance. •. Fragment mass tolerance. There is a wide range of different databases, that can contain either real protein sequences or theoretical protein sequences, derived directly from nucleic acid databases. Protein sequence databases can be either comprehensive containing various taxonomies or restricted to specific taxonomy.. - 33 -.

(34) THEORETICAL PART _________________________________________________________________________________________________________________. Protein sequence database should be chosen accordingly to the origin of the measured samples. In general, selection of certain database should consider a few factors. First of all, a large size of the database increases chance for successful identification of a given protein. On the other hand, the bigger is the database the bigger is the chance for false positive identification, and therefore a search algorithm needs to be more restrictive, thus reducing protein scores. In some situations some low-scored proteins present in the sample can be discarded. Selection of the database should be chosen with the most advantageous compromise. Thus, the size of the database should be reasonably reduced, especially when low abundant proteins are in the field of interest. The reduction can be done in two approaches. First and the simplest, consists a selection of proper taxonomy in the Mascot search form. The second, more sophisticated requires search of, or creation and finally addition of a database dedicated to specific sample type. Other important parameters that need to be set are peptides modifications. These parameters are dependent on sample preparation and on general sample character. There are two categories, which peptide modifications can be classified by. First are fixed modifications, where mass increment (difference) of a certain modification is calculated for every single peptide. There is no computational complication associated with fixed modification, it is simply equivalent to using a different mass for the modified residue or terminus. The example of a common fixed modification is Carboxyamidomethylation (+57.0214 Da), which is a result of specific sample preparation. Variable modifications are those, which may or may not be present in a given peptide. Mascot tests all possible arrangements of variable modifications to find the best match. For example, if Oxidation (M) is selected, and a peptide contains 3 methionines, Mascot will test for a match with experimental data for that peptide containing 0, 1, 2, or 3 oxidized methionines. This greatly increases the complexity of the search, resulting in longer search times and reduced specificity, therefore variable modifications should be used sparingly. Another important parameter that needs to be set is an enzyme used for proteolytic digestion. There is a number of different possibilities and the selection depends on sample preparation stage. Enzyme parameter gives information to the search algorithm how to create a theoretical list of peptides from the selected protein sequence database, depending on the sites of the cleavage. In proteomic data Trypsin is the most commonly used enzyme. However, when simple protein identification is the major goal the semi-Trypsin option usually provides better results.. - 34 -.

(35) THEORETICAL PART _________________________________________________________________________________________________________________. The next important parameter in Mascot search form is a number of maximum missed cleavages. The experience shows that digested mixtures usually include some peptides with missed cleavage sites. A reasonable number of missed cleavages in typical proteomics data should be set to 1 or 2. The higher selected numbers simply increase the number of random matches, and so reduce discrimination. The other three parameters: peptide charge, peptide mass tolerance and fragment mass tolerance are strictly dependent on the type of the instrument used for measurements. They also depend on actual calibration and the data acquisition method i.e. fragmentation method.. 2.7.3.2.2. PeptideProphet (within TPP) When interpreting the results of the analysis using popular programs (Mascot, Sequest, X!Tandem). it. may. appear. doubtful. whether. the. identification. is. correct.. A. common and uncritical approach is to accept all identified peptides/proteins, for which the score value is above the predefined threshold. Finding of a one high scored-peptide may be enough for reliable protein identification (if a sequence coverage is relatively high). However, in some cases few identified peptides may be insufficient for protein identification. Thus, it becomes problematic to determine an acceptance threshold, which depends on the nature of the analyzed samples, databases, the searched sequences, as well as the method of analysis in the mass spectrometer. The popular algorithms that are used for protein identification have different systems for evaluation of the results and there is no possibility for their direct comparison. In addition, these programs have different acceptance thresholds, dependent on many factors. Therefore, analyzing the same spectrum by different algorithms, often leads to different results [73]. Additionally, the constant acceptance threshold may cause problems due to the differences in the quality of the fragment spectra, even within the same instrument. Thus, comparison of the results from different types of spectrometers leads to errors. The same applies to the data from samples with different origin (i.e. cell lines, tissues etc.) that are to be combined and compared. Usually proteins are expressed at slightly various level in different cell types, tissues etc., therefore they may be differently scored by algorithm. This in turn may result in some issues in direct comparisons. To overcome the above problems a special software which is a part of TPP, called PeptideProphet was developed. This program operates at a different level than previously. - 35 -.

(36) THEORETICAL PART _________________________________________________________________________________________________________________. described algorithms for fragment mass spectra analysis. It uses results from search algorithms (Sequest, Mascot, X!Tandem) and performs specific calculations. Firstly, different types of results parameters (scores) from different programs are changed into the one unified discriminant score. This approach allows for direct comparison of the results between different search algorithms. Results recalculation is a complicated process, which not only takes into account the previously obtained score values, but also other parameters such as accuracy. ProteinProphet creates a dependency between a number of spectra with certain discriminant score and the discriminant scores (Fig. 14). In the resulting histogram the program plots curves describing the distribution of correct and incorrect matches (curves blue and red, respectively). These calculations are based directly on the results from the search algorithms (Mascot, Sequest, X!Tandem). [86].. 200 180. “incorrect”. Number of spectra in each bin. 160 140 120 100. “correct”. 80 60 40 20 0 -3.9. -2.3. -0.7. 0.9. 2.5. 4.1. 5.7. 7.3. Discriminant score (D) Figure 14. PeptideProphet’s histogram of the distributions of correct and incorrect matches.. Based on the obtained distributions and Bayesian statistics the probability of correct identification for certain discriminant score is calculated. Actually, at this point the acceptance threshold is calculated (depending on many parameters). Such an approach, based on variable threshold of acceptance, results in the higher sensitivity of identification. This is. - 36 -.

(37) THEORETICAL PART _________________________________________________________________________________________________________________. because each time the threshold is optimally adapted to the conditions of analysis. In this way the best compromise between sensitivity and incorrect assignments is obtained. As a result, PeptideProphet with the same error rate is able to identify up to 30% more spectra than Sequest (according to the TPP course materials). Thus, by replacing the rigid evaluation system of identifications, it becomes possible to compare results from different types of spectrometers, databases, and algorithms.. 2.7.3.3. Mass spectrometry-based quantitation Nowadays, there are two main quantitation approaches in proteomics. First, is closely related to electrophoresis separation and utilizes intensity of gel spots for proteins quantitation. The second approach uses mass spectrometry in a quantitative manner. Recent developments in mass spectrometry allowed for collection of thousands of spectra in a single LC-MS run, due to the faster spectra acquisition. Therefore, it became possible to utilize mass spectrometry for protein quantitation. However, MS-based protein quantitation needs to face several technical issues. First of all, signal intensities observed in MS spectra not only depend on the quantity of specific compound but also on other factors, such as: •. different ionization efficiencies. Different peptides originated from a single protein may be observed with intensities that span across few orders of magnitude.. •. ion suppression effects in ESI. If one peptide is co-eluting with a compound of higher ionization efficiency it may be observed with much lower intensity.. •. different spraying conditions in ESI. Within few runs even with the same LC method used, there are some differences in mobile phase composition resulting in minor differences in elution and ionization. Therefore, two subsequent LC-MS runs of the same sample may show differences in mass chromatograms.. To overcome these problems, internal standards are used as a reference for quantitation. There are two major quantitation approaches: stable-isotope labeling and label-free quantitation. Both methods require normalization, which is realized in completely different ways.. - 37 -.

(38) THEORETICAL PART _________________________________________________________________________________________________________________. 2.7.3.3.1. Stable-isotope labeling Stable-isotope labeling utilizes specific substitution of several atoms with other nonradioactive isotopes, such as. 13. C or. 15. N. Such an exchange does not cause any difference in. physical or chemical properties of the molecule, but due to the mass shift, both the analyte and the internal standard can be distinguished by mass spectrometry. Stable-isotope labeling may be used for absolute or relative protein quantitation. In the first approach, synthetic labeled peptides are spiked to the sample at certain quantity, allowing for absolute quantitation of the corresponding peptides. This method was firstly showed and applied in proteomics by Gerber, introducing the AQUA (Absolute QUAntitation) [74]. Nowadays, this approach is mostly associated with SRM technique (Selected Reaction Monitoring) as a targeted mass spectrometry complement to shotgun methods [75]. Unfortunately this method requires at least one labeled peptide to be synthesized for each quantified protein. Taking into account a significant cost of synthetic labeled peptides, this method can be used in the large-scale proteomic studies only by few laboratories worldwide. As an alternative, a relative quantitation strategy is used. Generally, two samples are labeled with distinct isotopes, and at some point of analysis, samples are pooled together and measured. In this approach, proteins or peptides from one sample serve as an internal standard for the other sample. Isotopic labels can be introduced into samples at different stages. Firstly, isotopic tags can be incorporated into the samples in vitro. In such an approach, called metabolic labeling, isotopically-labeled amino acids are added into culture media and then introduced into the newly synthesized compounds e.g. proteins. One of the most widespread metabolic labeling method, which was introduced by Ong et al. [76] is called SILAC (Stable Isotope Labeling by Amino Acids in Cell Culture), and it is based on a replacement of one of the amino acids in the cell culture – typically Lysine or Arginine. This methodology is limited to cell cultures or entire living organisms and requires time-consuming passaging for high yield isotopic incorporation. Isotopic labeling can also be performed at protein or at peptide level. In this case there is a large number of different labeling methods. One of the first labeling strategy - ICAT (IsotopeCoded Affinity Tag) was introduced by Gygi et al. [77]. This method takes advantage of bifunctional tags. On the one side of a molecule there is a biotin affinity tag that is used in the following pull-downs, and on the other side there is a thiol-specific group that allows for. - 38 -.

(39) THEORETICAL PART _________________________________________________________________________________________________________________. binding to cysteine. The middle part of ICAT reagent consists of polyether chain with either 8 hydrogen or deuterium atoms. Generally, two peptide maps to be compared are labeled with different ICAT reagents – one with light form and second with heavy form. In the next step samples are pooled together, purified on a streptavidin beads and analyzed with MS. As a result, corresponding peptides from different samples, one – labeled with light form and second labeled with heavy form of ICAT reagent are visible in the spectra as a doublet with 8 Da mass shift. The obtained ratio of chromatographic peak areas for light- and heavy-labeled peptide corresponds to a change in quantity of specific protein. The biggest advantage of this strategy is fact that, due to the high yield of biotin-based purification, this method can be successfully used for quantification of low abundant proteins. On the other hand, only proteins that contain cysteine are considered. Usually there is a limited number of peptides containing cysteine per protein, therefore a low number of peptides per protein can be quantified, thus resulting in a weak quantitation statistics. In contrast to the methods based on modification of amino acids side chains in peptides, there is a large number of labeling possibilities at free N-terminal amino group and/or Cterminal carboxyl group of the peptides. These approaches allow for a higher coverage of protein sequences, because almost every peptide in the sample can be labeled. These strategies are based on acylation of amino groups with acetic [78] and succinic [79] anhydride, or N-acetoxysuccinicamide [80], or eventually methyl [37], or ethyl [82] esterification of carboxyl groups. As an alternative to the previously described methods, isotopic labels can be introduced during the proteolysis. In this strategy, enzymatic digestion of a protein is performed in H218O, which results in incorporation of a single. 18. O atom into every C-terminal carboxyl. group [83,84]. This method may suffer from unstable labels at certain conditions, because in the following steps the carboxyl groups remain active. Therefore, some spontaneous oxygen exchanges with water molecules from the solvent are possible. Finally, this limited stability causes some peptides to loose their isotopic labels, and in extreme situations some of them may gain extra labels (on side chains), what in turn complicates MS data analysis. All of the previously described methods for relative protein quantitation are based on a direct comparison between two simple forms –light and heavy. Thus, only two samples can be simultaneously analyzed. Another strategy, which enables analysis of multiple samples is called iTRAQ (Isobaric Tag for Relative and Absolute Quantitation). In this approach, peptides are labeled at N-terminus and at lysine amino groups with a special tag, consisting of two parts, which are easily cleavable in MS2. First part is called a reporter and the second – a - 39 -.

(40) THEORETICAL PART _________________________________________________________________________________________________________________. balance group. Masses of the reporter groups differ between each other (1 Da difference), and masses of the balance groups are adjusted in such a way that the total mass of a tag remains constant. As a result, corresponding peptides originated from different samples are observed as single peaks on a mass chromatogram. As only detected peak is subjected to fragmentation, the tags break apart resulting in a release of all the reporter ions in the low mass range (masses of observed reporter ions are 113, 114, 115 and 116 Da). Finally, the relative quantitation of a certain peptide is done through a direct comparison between reporter ions intensities. In the same time, the other part of a fragmentation spectrum is used for peptide identification. The last developments in this method allowed for introduction of up to 8 isobaric tags in so called eight-channel iTRAQ [85].. 2.7.3.3.2. Label-free quantitation In contrast to the quantitative methods based on chemical labeling, there are also other methods that allow for relative quantitation with no need of prior labeling. These strategies were introduced because all classical labeling methods, despite their usefulness, shared a common issue - application of these methods requires additional analytical steps. Thus, much effort was spent over the time to simplify the analytical procedures and finally the quantitation process. In general, in the label-free quantitation experiments, samples to be compared are analyzed separately with the same instrument setup, methods and even tuning parameters. The most important is to preserve exactly the same conditions in LC separations, as quantitation process is based on mass chromatograms and retention times. Label-free methods aim to compare two or more experiments by either comparing the direct mass spectrometric signal intensity for any given peptide or by using the number of acquired spectra matching to a peptide/protein as an indicator of their respective amounts in a given sample. There are two main approaches in the label-free quantitation. First is so called spectral counting and it is based on a simple counting of the spectra identified for a given peptide in different biological samples. The general rule is based on the empirical observation that the more of a particular protein is present in a sample, the more MS2 spectra are collected for peptides of that protein. Hence, relative quantification can be achieved by comparing the number of such spectra between a set of experiments.. - 40 -.