The Health School
Polytechnic Institute of Guarda
R E P O R T O F P R O F E S S I O N A L
I N T E R N S H I P I I
LIONEL MENDES DIAS
1st CYCLE DEGREE ON PHARMACY
The Health School
Polytechnic Institute of Guarda
1ST CYCLE DEGREE ON PHARMACY
4TH ACADEMIC YEAR / 2ND SEMESTER
REPORT OF PROFESSIONAL INTERNSHIP II
THE INSTITUTE OF CANCER RESEARCH
CLINICAL PHARMACOLOGY - DMPK
LIONEL MENDES DIAS
COORDINATOR: DR. FLORENCE I RAYNAUD
TUTORING PROFESSOR: DR. FÀTIMA ROQUE
Page | 2
ABSTRACT
Cancer is fundamentally a disorder of cell growth that involves a large volume of raw material such as nucleic acids, proteins, and lipids to proliferate. Lipid metabolism, as well as fatty acid synthesis, is regulated by common oncogenic signalling pathways, involving Polyunsaturated Fatty Acids (PUFAs). PUFAs participate in intracellular cell signalling control and have biological functions that elicit pro- and anti-inflammatory responses for regulation of cell proliferation, apoptosis, and angiogenesis. Lipidomics is a lipid-targeted metabolomics approach to quantify lipids in biological systems. In order to circunvent the difficult analysis of fatty acids we have used N-(4-aminomethylphenyl) pyridinium (AMPP) for derivatization and increment of mass spectrometry sensitivity. Through this project we show that endogenous fatty acids and eicosanoids are successfully able to be quantified in human and mouse plasma with higher sensitivity through positive ESI in LC-MS/MS.
Page | 3 ACKNOWLEDGEMENTS
Approximately one year have past since I arrived at the Institute of Cancer Research (ICR) for the first time. I cannot let this opportunity go without thank my coordinator Florence I Raynaud for giving me the opportunity to have my professional internship at the ICR. I am really glad to be part of the amazing team that DMPK is. It will be impossible to find a better place to improve my skills and learn so much about what laboratory work is all about.
I would also like to thank Dr. Raynaud for all the support that she gave to me. There is no way to show my gratitude for all your words and all the tolerance that you have had with me. Thank you very much!
This project would never have been performed without the contribution and support of my supervisors Yasmin Asad and Ching Thai. Thank you very much for all the patience and dedication in sharing your vast knowledge and experience with me. I know that I am not an easy person to teach but you have taught me well.
I would like to acknowledge the contribution of other members of the DMPK team by the way I was welcomed and for all the attention and availability given. It is been a pleasure to work with all of you.
I must not forget to thank the The Health School of the Polytechnic Institute of Guarda, where I have been a student since 2010. Without the support of the IPG Offices of Mobility and Cooperation it would not have been possible to make this experience so enriching for my professional life. Many thanks to Professor Fátima Roque and André Araújo for all the encouragement and support given during this Internship.
Last but not least, I want to really thank my family for being so amazing for me and supporting all my decisions. I really appreciate everything that you have done for me in my life.
To all, thank you for everything!
Page | 4 QUOTE
“Every sickness is a musical problem and every cure a musical solution.” W. H. Auden
Page | 5 CONTENTS ABSTRACT ... 2 ACKNOWLEDGEMENTS ... 3 QUOTE ... 4 LIST OF FIGURES ... 7 LIST OF TABLES ... 8 ABBREVIATIONS ... 9 1 – INTRODUCTION ... 10
1.1 – CANCER THERAPEUTICS EVOLUTION ... 10
1.2 – LIPID METABOLISM IN CANCER ... 11
1.3 – LIPIDOMICS, A METABOLOMIC FIELD ... 13
1.4 – LIPIDOMICS IN DRUG AND BIOMARKER DEVELOPMENT ... 15
1.5 – FATTY ACIDS DERIVATIZATION AND SENSITIVIY ENHANCEMENT ... 17
1.6 – AIM OF THE PROFESSIONAL INTERNSHIP II ... 19
2 – MATERIAL... 20
3 – METHODS ... 21
3.1 – SAMPLES AND SOLUTION‟S PREPARATION ... 21
3.1.1 – Individual stock solutions ... 21
3.1.2 – Stock calibration standard mix ... 21
3.1.3 – Fatty acids extraction ... 21
3.2 – STANDARD DERIVATIZATION ... 22
3.3 – ASSAY PREPARATION ... 23
3.3.1 – Plasma stability assay ... 23
3.3.2 – Plasma dilution assay ... 23
3.3.3 – Gender and fatty acids plasma levels measurement ... 23
Page | 6 3.4 – SAMPLE ANALYSIS ... 24 3.4.1 – Chromatography ... 24 3.4.2 – Mass spectrometry ... 25 3.4.3 – Data processing ... 25 4 – RESULTS ... 26
4.1 – LC-MS/MS ANALYSIS OF PLASMA DERIVATIZED FATTY ACIDS ... 26
4.2 – OPIMIZATION OF FATTY ACIDS PLASMA EXTRACTION ... 28
4.3 – PLASMA FATTY ACIDS STABILITY ASSAY ... 29
4.4 – PLASMA DILUTION ASSAY ... 30
4.5 – GENDER AND FATTY ACIDS PLASMA LEVELS ... 31
4.6 – HUMAN AND MOUSE PLASMA COMPARISON OF FATTY ACIDS ... 32
5 – DISCUSSION AND CONCLUSION OF RESULTS ... 33
6 – REFERENCES ... 36
APPENDIX A ... 38
Page | 7
LIST OF FIGURES
Figure 1.1 – Metabolic pathway of PUFA.
Figure 1.2 – Arachidonic acid and eicosapentaenoic acid metabolism contribute to cancer
risk and progression through pro- and anti-inflammatory lipid metabolites that stimulate cell proliferation, angiogenesis and migration.
Figure 1.3 – Lipidomics, systems-level scale analysis of lipids and their interactors. Figure 1.4 – ESI: desolvation and ionisation of analyte molecules.
Figure 1.5 – Structure of AMPP and an AMPP amide along with the reagents used for
derivatization.
Figure 1.6 – Representative chromatograms of various fatty acids and eicosanoids in buffer
(2.5μM concentration).
Figure 1.7 – Calibration line of ALA results processed by TargetLynx program. Figure 1.8 – Representative peak of ALA (2.5μM).
Figure 1.9 – LC-MS/MS results of FA and eicosanoids plasma optimization extraction. Figure 1.10 – Illustrative graphic of plasma stability assay of fatty acids.
Figure 1.11 – Plasma dilution assay and fatty acids levels.
Figure 1.12 – Human plasma fatty acids levels in male and female gender.
Page | 8
LIST OF TABLES
Table 1.1 – Conditions of reagents preparation.
Table 1.2 – Gradient used for fatty acids and eicosanoids chromatography. Table 1.3 – Derivatized product m/z of fatty acids and eicosanoids. Table 1.4 – Plasma stability assay and fatty acids levels.
Table 1.5 – Comparison between FA and eicosanoids values from literature with the
experimental results.
Page | 9
ABBREVIATIONS
AA – Arachidonic Acid;
ADME – Absorption, Distribution, Metabolism and Excretion; ALA – Alpha-Linolenic Acid;
AMPP – N-(4-aminomethylphenyl) pyridinium; CID – Collision-Induced Dissociation;
DHA – DocosaHexaenoic Acid;
DMPK – Drug Metabolism and PharmacoKinetic teams; EPA – EicosaPentaenoic Acid;
ESI – ElectroSpray Ionization; FA – Fatty Acids;
GC – Gas Chromatography; ICR – Institute of Cancer Research; LA – Linoleic Acid;
LC – Liquid Chromatography; LTB4 – Leukotriene B4;
MRM – Multiple Reaction Monitoring;
MS – Mass Spectrometry; PD – Pharmacodynamic; PGE2 – ProstaGlandin E2; PK – Pharmacokinetic;
PUFAs – Polyunsaturated Fatty Acids;
Page | 10
1 – INTRODUCTION
1.1 – CANCER THERAPEUTICS EVOLUTION
Cancer, according to the World Health Organization, occupies the 7th position of the top 10 causes of death in the world. Comparison of the mortality rate between 2000 and 2011 from cancer or non-communicable disease show that Lung cancers (along with trachea and bronchus cancers) caused 1.5 million (2.7%) deaths in 2011, an increase from 1.2 million (2.2%) deaths in 2000. 1
Cancer history starts in 460–370 B.C century in Greece, when a physician called Hippocrates described carcinoma tumours, giving them the name of “karkinos”, although, he was not the first to discover this disease. 2 Some of the earliest evidence of human bone cancer was found in mummies in ancient Egypt and in ancient manuscripts dated about 1600 B.C.2 The oldest recorded case of breast cancer hails from ancient Egypt in 1500 BC where it was recorded that there was no treatment for the cancer, only palliative care.2
Over time cancer diagnosis and therapeutics has greatly evolved and contributed to an increase of the average life expectancy. There have been many theories about cancer including Humoral theory, Lymph theory, Blastema, Trauma and Parasite theory before 1911, when George Clowes of the Roswell Park Memorial Institute, in New York, developed the first transplantable tumour system in rodents and new era of cancer diagnosis and therapy began.3 In 1939 Charles Huggins introduced a hormonal therapy for prostate cancer showing responses by decreasing the acid phosphatase levels.3 Historically chemotherapeutics such as arsenics, nitrogen mustards, vinca alkaloids, antifolates and thiopurines, have been the main treatment of cancer until the concept of chemotherapy. 3 Previously the aim of the drugs was to kill the cancer cells and erase the tumour from the body, but they also killed some normal cells, especially proliferating tissue. In 1990‟s new targeted cancer treatments appeared such as growth signal inhibitors, drugs that induce apoptosis and endogenous angioinhibitors. 2 One of the examples, and the Institute Of Cancer Research (ICR) biggest success stories is Abiraterone. Abiraterone acetate is a prodrug, an orally active inhibitor of the enzyme CYP17α (17α-hydroxylase/C17, 20-lyase) and acts as an androgen biosynthesis inhibitor by blocking two important enzymatic activities
Page | 11 in the synthesis of testosterone, thus preventing testosterone being made anywhere in the body.4,5
The growth in knowledge of cancer biology from the middle of the 20 th century has led to remarkable progress in cancer early detection, treatment and prevention although the complexity of cancer requires scientific battles to fight against tumours on several frontiers.
1.2 – LIPID METABOLISM IN CANCER
Cancer is fundamentally a disorder of cell growth and proliferation, requiring cellular building blocks, such as nucleic acids, proteins, and lipids.6 Cancer cells often have abnormal metabolism permitting them to accumulate metabolic intermediates as sources of these building blocks. We know that cancer cells need a higher rate of metabolism to support their accelerated proliferation rate.6 The most known metabolic change is a phenomenon called “Warburg effect” first described by Otto Warburg in 1920s. He reported that cancer cells take up and utilize much more glucose for glycolysis, compared with normal cells, even in normoxic conditions.7
Lipid metabolism, as well as fatty acid synthesis, plays an important role in the cell‟s activity. Lipid metabolism in cancer cells is regulated by common oncogenic signalling pathways, and is believed to be important for the initiation and progression of tumours.7 Most normal tissues utilize more circulating lipids for the synthesis of new structural lipids compared to tumours that express high levels of fatty acids synthesis and undergo significant endogenous fatty acid biosynthesis.8 Increased expression of the fatty acids synthesis gene and catalytic activity, may play a central role in neoplastic transformation due to their ability to compensate for an insufficiency of both oxygen and dietary fatty acids due to lack of angiogenesis in the early cancer development.8 This programmed fatty acids synthesis up-regulation is maintained in coordination with increased demand for fatty acid metabolism and/or membrane synthesis in response to cancer-related overexpression of growth factors and/or growth factor receptors.8
Lipids are the major component of biological membranes and have many important roles in biological systems including energy storage, signalling and as hormones precursors. They are hydrophobic or amphipathic small molecules that originate entirely or partly by
Page | 12 Fatty Acids (FAs).9 Structurally, natural FAs have saturated or unsaturated straight-hydrocarbon chains varying from 14 to 24 carbon atoms and possessing 0–6 double bonds 10 It has been widely reported that FA, and specific Polyunsaturated Fatty Acids (PUFAs), play interesting roles in cancer.11 PUFAs participate in intracellular cell signalling control and as nutritional components.11 The ω-3 family of PUFAs includes alpha-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) while the ω-6 family includes linolenic acid (LA) and arachidonic acid (AA).11,12 LA and ALA are considered essential fatty acids as they must be obtained from the diet since humans are unable to synthesize18-carbon PUFAs. 13 EPA and DHA are generated via ALA and they are precursors for anti-inflammatory lipid mediators while AA is generated via LA and is a precursor for pro-inflammatory lipid mediators (see Figure 1.1).14 The metabolism of AA by cyclooxygenase (COX), lipoxygenase (LOX) and P450 epoxygenase pathways generates eicosanoids, including prostanoids (e.g. PGE2) and leukotrienes (e.g. LTB4). EPA metabolism makes use of the same enzymes to generate derivatives which have a structure different from those produced from arachidonic acid (e.g LTB5 and PGE3).11,13,14
Figure 1.1 – Metabolic pathway of PUFAs.11
The metabolites most commonly linked with cancer progression are leukotrienes and prostaglandins. Individual PUFAs produce prostaglandins and leukotrienes with distinct biological functions that elicit pro- and anti-inflammatory responses through several signaling pathways responsible for regulation of cell proliferation, apoptosis, and angiogenesis (see Figure 1.2).11
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Figure 1.2 – Arachidonic acid and eicosapentaenoic acid metabolism contribute to cancer risk and progression through pro- and anti-inflammatory lipid metabolites that stimulate cell proliferation, angiogenesis and migration.11
Pro-inflammatory eicosanoids are produced by various cancer cells; they can alter tumour progression through several mechanisms, such as: directly activating their receptors on tumour epithelial cells to regulate cell proliferation, apoptosis, migration and invasion. They can also induce epithelial cells to secrete growth factors, pro-inflammatory mediators and angiogenic factors that switch a normal microenvironment to one that supports tumour growth and spread; and directly binding receptors on stromal cells to promote a tumour-supportive microenvironment by inducing angiogenesis and evading attack by the immune system.11,14
1.3 – LIPIDOMICS, A METABOLOMIC FIELD
Metabolomics is an “Omic” science of small molecules in a biofluid, tissue, organ or organism, used as an analytical tool to quantify and qualify the end-products of the cellular metabolism of both endogenous and exogenous substrates.15,16 This analysis is complicated because different cell populations contain variable amounts of metabolites and specialized biochemical systems for their biosynthesis. Thus, it seems that metabolomics might benefit
Page | 14 from a more targeted approach that will focus on specific groups of metabolites, and on a detailed consideration of the specificity of cell metabolite biochemistry.10
Lipidomics is a lipid-targeted metabolomics approach aiming at comprehensive analysis of lipids in biological systems.17 Similarly metabolomics is the quantitative cataloguing of the entire range of metabolites, (see Figure 1.3).18 There are two major points of biological significance of lipidomics: firstly lipid metabolites and/or lipid metabolic pathways in complex biological systems can be linked to individual metabolic health; secondly, to interpret the changes in the lipid metabolism or in the regulation of these pathways linked to metabolic and inflammatory diseases from a physiological and/or pathological perspective.19,20 Thus, lipidomic investigations usually focus on the measurement of alterations of lipids at systems-level indicative of disease, environmental perturbations or response to diet, drugs and toxins as well as genetics.17,19
Figure 1.3 – Lipidomics, systems-level scale analysis of lipids and their interactors.22
Recent advancements in mass spectrometry (MS) and innovations in chromatographic technologies have largely driven the development of lipidomics. Mass spectrometry is a technique that performs the molecular identification through determining the ratio of mass to charge (m/z) of a molecule.16 It relates to the production and subsequent separation and identification of charged species that are produced by a variety of ionisation methods.
Positive and negative ESI ionization mode are applied to analyze different types of lipid. The ion response even within a class of compounds can depend on the fatty-acid composition, which is a complicating factor for absolute quantification in mixtures.21,22
Most lipidomics experiments are conducted on tandem mass spectrometers MS/MS instruments. An MS/MS instrument consists of two mass analysers separated by a collision cell containing collision-gas. The most common MS/MS scan is the „„product-ion‟‟ scan
Page | 15 where a precursor-ion is selected by first mass analyser and fragmented in the collision cell by collision induced dissociation (CID) and the resultant product-ions mass (m/z) measured by second mass analyser.18
Gas Chromatography (GC) and Liquid Chromatography (LC) are the most used instruments in lipid research. GC tandem mass spectrometry has been used in lipid analysis for several decades.16 However the prerequisite condition of GC analysis is the volatility of the lipid; since many lipids are non-volatile in the natural state, volatile derivatives were needed for GC-MS analysis.16
LC combined with mass spectrometry is becoming increasingly important in biomolecule analysis. LC is a technique that is driven by the selectivity achieved using two interacting phases.23 LC separations involve both the mobile phase (a liquid) and the stationary phase (usually materials of varying hydrophobicity chemically bonded to a solid support called column) to retain the molecule of interest.23 The type of chromatography, normal phase or reverse phase chromatography, is usually defined by the polarity of the mobile phase.24 For most of LC-MS applications in lipidomics, the ultimate stationary phase is reversed phase and the mobile phase aqueous alcohol or acetonitrile.18
Despite recent advances the diversity of structures, properties and concentrations of lipids provide a massive challenge for the analytical methodology to use a single technological platform able to measure and identify all lipids in a single sample simultaneously. Hence multiple analytical approaches are often used in lipidomics.18
1.4 – LIPIDOMICS IN DRUG AND BIOMARKER DEVELOPMENT
Many successful drug classes, including cholesterol-lowering agents such as statins and cyclooxygenase inhibitors are directed against lipid-metabolizing enzymes. New modes of lipid function are subject to therapeutic interventions and lipidomics is an important consideration at several stages of drug development.22
One of the most obvious applications of lipidomics includes profiling lipid extracts in order to identify metabolic pathways and enzymes that are affected by a perturbation of interest (target identification).22 The discovery of new lipid binding domains promotes the characterization of specific receptors and the search for novel interactions.22
Page | 16 After target identification, assays for cell-free and in vivo studies must be developed to screen the target to find lead compounds that interfere with its function (Assay development).22 Enzyme activities can include lipid kinase and phosphatase activities that can be determined in cellular extracts. Thus, lipidomics is an excellent tool to determine levels of known metabolites in crude extracts and for the validation of target lipid enzymes and metabolic pathways.15,22
High-throughput cell imaging combined, for example, with an optical readout for lipid metabolism, or a process that is functionally linked to it, can be used to identify inhibitors (and targets) and contribute to lead identification and optimization.22 Most naturally occurring lipids are small molecules and their interaction with the enzymatic machinery, and associated proteins, is mediated by recognition of their polar and non-polar moieties.22 Knowledge of lipid structure facilitates the design of novel chemical entities that target lipid-interacting proteins and enzymes to specifically design lipid-like molecules that mimic natural ligands.22 This might result in more flexibility in molecular modelling of ligands into the pockets of lipid-binding proteins.15
A high proportion of existing small-molecule drugs are hydrophobic, which leads to side effects that are sometimes severe. Consequently, toxicity profiles and formulations for the delivery of drugs directed against lipid-based mechanisms are often different from those observed for drugs that act on soluble targets22 leading to new forms of prodrugs and drug delivery. Liposomes composed of phospholipids that carry a drug are already able to decompose preferentially at a site, and liberate the drug cargo very locally. This affects the pharmacokinetic properties of the drug compound, 22 including bioavailability, leading to differences at pharmacodynamic level.
The traditional evaluation of patients in clinical trials has involved clinical endpoints, characteristics or variables that describe how a patient feels, functions, or survives.15 Increased recognition of the value of biomarkers in a clinical setting, makes lipidomic analysis an important tool in provision of molecular signatures for a pathway or condition.15 In cancer biomarker discovery, one of the most obvious applications of lipidomics is to compare the lipid profiles of the healthy and disease state, thus to identify those lipid species with altered expression.15,19 There has been recognition that lipidomics may serve to identify off-target drug effects and facilitate understanding of the relationships between metabolite and enzyme leading to the development of more defined biomarkers.22
Page | 17 Lipidomics therefore adds an additional layer of information to data from proteomics and genomics, which will further our knowledge of lipid function and open new opportunities in drug and biomarker development, in particular at the various stages in the preclinical and early clinical categories.19
1.5 – FATTY ACIDS DERIVATIZATION AND SENSITIVIY ENHANCEMENT
In LC-ESI-MS/MS analysis, a major limitation is suppression of ionization, which might be circumvented by upfront chromatographic separation of mixtures or the analytes must possess some properties that enable ionization to make it sensitively detectable.22,25 i.e. the analytes must be ionic or ionisable in solution, since the gas phase ions in the ESI are mainly generated by transferring the ions in solution from a strong electrical field. It is also preferable for the analytes to have the appropriate hydrophobic structures, because the hydrophobic ions prefer to reside at the droplet surface generated by electrospray and these ions enter the gas phase more readily than those in the droplet interior and show higher signal intensities (see Figure 1.4).25 The hydrophobic compounds can be also well separated on the reversed phase column and are easily eluted with higher organic solvent content that is also more suitable for the stable generation of charged droplets by electrospray and thus gives the higher signal intensities (see Figure 1.4). It is also important for the analysis that the analyte has suitable structure for MS/MS detection to fragment efficiently upon CID and generates an intense and particular product ion.25,26
Page | 18 Not all the compounds can be favourably analysed by LC/ESI-MS/MS. Thus, chemical derivatization of the analyte is often used to enhance the detection sensitivity. Derivatization, by definition, is the chemical change induction of an analyte by transforming a compound into a product (derivative) with addition of a specific functional group.26
Initially, the derivatization reagents were used mainly for the improvement of the chargeability, stability and volatility on GC until Quirke et al. 25 reported the derivatization of alkyl halides, alcohols, phenols, thiols, and amines using a number of the reagents to achieve the enhancement of the signal intensities.25
The carboxylic acid moiety that is always present in fatty acids, are detectable by negative ESI-MS but with poor sensitivity. In addition the mobile phases for the carboxylic acids separation are not always compatible with ESI-MS. Therefore carboxylic acids are sometimes transformed to the hydrophobic and ionizable structures by derivatization.25,26
Recently, Bollinger et al reported the utilization of N-(4-aminomethylphenyl) pyridinium (AMPP) derivatization for fatty acids and eicosanoid analysis by LC-MS/MS with a dramatic improvement in sensitivity of 10- to 20-fold in tandem MS/MS and the production of structurally informative fragment ions during CID.16,27,28
The AMPP is coupled to free carboxylic acids through an amide bond linkage and converted to a cationic group so that the MS could detect in positive ion mode (see Figure 1.5). To optimize the reaction, the derivatization process makes use of high temperatures and the reagents 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC), 1-Hydroxy-7-azabenzotriazole (HOAt) or N-hydroxybenzotriazole (HOBt) and N-hydroxybenzotriazole (HOBt) with acetonitrile.16,27,28
Figure 1.5 – Structure of AMPP and an AMPP amide along with the reagents used for derivatization.16,27,28
The derivatization process is easy, simple and fast to execute and the resulting derivatives can be directly submitted to LC-ESI-MS/MS. One can anticipate that the AMPP derivatization method can be extended to other carboxylic acid analytes for enhanced sensitivity detection.16,27,28
Page | 19 1.6 – AIM OF THE PROFESSIONAL INTERNSHIP II
This internship is within the DMPK department at the Institute of Cancer Research (ICR) in Sutton, Greater London, United Kingdom (UK). It has a planned duration of one academic year and began on 2th September 2013. Dr. Florence I Raynaud is my coordinator in this internship, Yasmin Asad and Ching Thai are my supervisors together with the DMPK team.
As part of this internship, I am participating in this research project called “AMPP Project” to investigate whether we can increase the sensitivity and selectivity detection of fatty acids and eicosanoids presents in human plasma using the derivatization process with AMPP (see figure 1.5) and explained in the literature above.
The main aim of this project is to achieve a better way to quantify and qualify fatty acids and eicosanoids with the LC-MS method in order to measure them in human plasma and obtain more information about the human metabolome, the individual response to a new treatment, the prognostic of cancer disease and many more options to be used in health care. This project is a continuation of the experiments described by James G. Bollinger related to AMPP derivatization of endogenous fatty acids and eicosanoids and comparison with the literature values. At the same time, experiments for reproducibility, stability and comparison between human/mouse plasma were done to better understand about AMPP derivatization and fatty acids as part of human metabolome.
Page | 20
2 – MATERIAL
In order to accomplish this project, we used a Waters Xevo TQ-S Mass Spectrometer coupled to an Acquity UPLC H-class system. Other equipment and materials such as LC columns that were used are described in Appendix A.
The different reagents used in the experiments are mainly: 15mM of AMPP derivatizing reagent in acetonitrile; 5mM of HOBt in acetonitrile; 640mM in of EDC in water and 4:1 of acetonitrile/DMF. The AMPP compound was prepared by a chemist at ICR whereas the other reagents such as HOBt, EDC and acetonitrile /DMF were provided by Sigma-Aldrich, Gillingham, UK. All other chemicals such as Fatty acids and Eicosanoids were also obtained from Sigma-Aldrich and are described in Appendix A.
The plasma sample, (human or mouse), were obtained from healthy volunteers donors from the ICR.
Page | 21
3 – METHODS
3.1 – SAMPLES AND SOLUTION‟S PREPARATION 3.1.1 – Individual stock solutions
In order to complete this project, selected fatty acids, related to cancer, were purchased from Sigma-Aldrich as shown and explained in Material and Appendix A. All the fatty acids and eicosanoids were diluted from original concentrations to prepare a 10mM stock solution. The solvent used for the dilutions is absolute ethanol and all the stock solutions were stored in a freezer at -80°C.
3.1.2 – Stock calibration standard mix
Standard calibration stocks of a fatty acids and eicosanoids mixture were prepared to mimic the endogenous concentrations in plasma. Individual stock solutions were diluted to 320µM and 100µL from each fatty acids was added to the same vial. The final vial contains a 40µM of concentration of FA and eicosanoid. The mixture was stored at -80°C.
The standard curve with concentrations of 40µM, 20µM, 10µM, 4µM, 2µM, 1µM, 0.4µM, 0.2µM, 0.1µM and 0.04µM was prepared.
3.1.3 – Fatty acids extraction
To analyse endogenous fatty acids and eicosanoids, the FA needed to be extracted from plasma. In order to optimize the extraction we made an assay to compare about which of the organic solvent, absolute ethanol and acetonitrile, extracts better the endogenous FA. Thus, the extraction process was carried out with both organic solvents in this assay and we also tried to derivatize first the plasma than extracted the FA. In this way we compared the extraction with three different manners: extraction with absolute ethanol, extraction with acetonitrile and extraction after derivatization. No organic solvent was needed to be added to the samples as is already part of the derivatization reagents constitution. The proteins in 10µL of plasma were precipitated with30µL of organic solvent, mixed and centrifuged at 3700 rpm for 10 min.
Page | 22 3.2 – STANDARD DERIVATIZATION
The derivatization process is performed according to the described in the literature for AMPP derivatization of fatty acids and eicosanoids presented mainly by James G. Bollinger27,28. The concentration of the reagents is shown below.(Table 1.1)
Table 1.1 – Conditions of reagents preparation. 27,28
The process has been detailed in report I but briefly, 20µL of diluted stock solution in a vial were evaporated at 60ºC for approximately 15 minutes. Were added 20µL of DMF/ acetonitrile, 20µL of EDC.HCL, 20µL HOBt and 20µL of AMPP to the same vial. The vial was closed and maintained in the dri block at 60ºC for 30 minutes in order to optimize the derivatization reaction. Once derivatized, solutions can be stored at -20ºC for further use.
Reagent Concentration Solvent
AMPP 15mM Purified water
EDC.HCL 640mM Purified water
HOBt 5mM Acetonitrile
Page | 23 3.3 – ASSAY PREPARATION
3.3.1 – Plasma stability assay
To determine the stability of plasma after the frost/thaw cycle, two groups of four vials containing plasma obtained from four different donors in which one group (group frozen 1) were initially frozen and just used once for analysis whereas the other group (group frozen 2) were thawed and frozen twice before analysis. In the first group of samples, the plasma was frozen for 144 hours (6 days) and in group two that after 72 hours (3 days) were taken from the freezer to completely defrost and after frozen again for more 3 days. The freezer temperature was -80 ºC and the samples were analysed in the same day of preparation.
3.3.2 – Plasma dilution assay
At this stage of the project, we wanted to know if the concentration of fatty acids concentration were directly related to plasma concentration so we made up a plasma dilution using water as a solvent. Thus, a dilution factor of two, five and ten times dilution was made from the original plasma concentration. The samples were prepared and analysed in the same day.
3.3.3 – Gender and fatty acids plasma levels measurement
To make this assay, fresh human plasma was obtained from four different donors (two female and two male) in order to determine the fatty acids levels in plasma for both of gender. All the preparation was executed as explain before.
Page | 24 3.3.4 – Human and mouse plasma fatty acids levels comparison
In order to compare the endogenous fatty acids and eicosanoids levels between human and mouse plasma, female human plasma and female mouse plasma were used in this experiment. The assay procedure is as similar as before.
3.4 – SAMPLE ANALYSIS
3.4.1 – Chromatography
The LC methods and conditions were optimized and adapted to our instruments from the literature from James G. Bollinger27,28 articles about fatty acids and eicosanoids AMPP derivatization.
In all the experiments related in this report we used a flow rate of 0.4 mL/min and a run time of 15:00 minutes. The main column type used in all the assays was a C18 reversed-phase column (more details in APPENDIX A) where the solvent used were water (solvent A) and acetonitrile (solvent B) both with 0.1% of formic acid.
A linear gradient solvent program within a range between 90 to 0% of solvent A and 10 to 100% of solvent B was used to determine the LC solvent conditioning gradient in our experiments (see table 1.2).
Table 1.2 – Gradient used for fatty acids and eicosanoids chromatography.
%A %B Initial 90% 10% 5:00 90% 10% 5:10 80% 20% 10:00 30% 70% 12:00 0% 100% 15:00 90% 10%
Page | 25 3.4.2 – Mass spectrometry
To obtain the maximum sensitivity analysis with MS/MS, we decided to perform our experiments using single reaction monitoring (SRM) as the main method to scan for our precursor and product-ions. Both precursor and fragment ions were obtained from the literature values described by James G. Bollinger27,28 and they are shown on the table above.
Table 1.3 – Derivatized product m/z of fatty acids and eicosanoids. 27,28
3.4.3 – Data processing
All the data collected from LC-MS/MS analysis was processed using TargetLynxTM that is part of MassLynxTM software from WatersTM.
Name Precursor ion (m/z) Product ion (m/z) Cone Voltage (V) Collision Energy (eV) 1 ALA 445 337 64 38 2 LA 447 239 65 48 3 AA 471 239 70 50 4 EPA 469 239 62 42 5 DHA 495 239 64 36 6 PGE2 519 239 75 45 7 LTB4 503 323 60 35
Page | 26
4 – RESULTS
4.1 – LC-MS/MS ANALYSIS OF PLASMA DERIVATIZED FATTY ACIDS
Figure 1.6 – Representative chromatograms of various fatty acids and eicosanoids in buffer (2.5μM concentration).
The figure above shows chromatograms of selected fatty acids and eicosanoids from SRM scan of the precursors and product-ions.
From these results we can observe that most of the peaks are eluting at 9 minutes except PGE2 and LTB4 that were detected at 7.15 and 8.04 respectively. DHA, LA and AA have very similar a retention times. All the peak intensities are approximately 7.00x107 a part from linoleic acid with 8.66x107.
Similar results were obtained from human plasma and mice plasma.
Observation of the calibration line of ALA (see figure 1.7), a plot of response over the concentration calculated by TargetLynxTM shows a correlation coefficient r2 of 0.989529 using a weighting of 1/x^2 for the curve. Similarly, all the calibration lines used in this project to calculate the relative concentration had a correlation coefficient between 1 and 0.95
Page | 27 in r2. The predominant weighting used was 1/x^2 and none of the standard curves had less than 6 points per calibration curve.
Figure 1.7 – Calibration line of ALA results processed by TargetLynx program.
Figure 1.8 – Representative peak of ALA (2.5μM).
Compound name: a-Linolenic acid
Correlation coefficient: r = 0.994750, r^2 = 0.989529 Calibration curve: 384709 * x + 20970.9
Response type: External Std, Area
Curve type: Linear, Origin: Exclude, Weighting: 1/x^2, Axis trans: None
Conc 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 R e sp o n se -0 1000000 2000000 3000000 min 2.0 4.0 6.0 8.0 10.0 12.0 14.0 % 0 100 MRM of 12 channels,ES+ 445 > 337 Smooth(SG,2x1) AMPP+MixFA_2.5uM_0.25nM 2.744e+007 a-Linolenic acid 9.42*
Page | 28 4.2 – OPIMIZATION OF FATTY ACIDS PLASMA EXTRACTION
Figure 1.9 – LC-MS/MS results of FA and eicosanoids plasma optimization extraction.
The above figure is showing the retention time, peak area and concentration obtained from plasma sample with different extraction methods after analyses on LC-MS/MS.
Comparison of concentrations, shows that plasma extracted with acetonitrile is the most efficient. The concentration differences between the extraction with ethanol and acetonitrile are very small, showing an average difference of 0.4068 µM where LA with a difference of 1.2680µM is the most disparate value. Plasma extraction after derivatization is much less efficient, showing less than half of the concentration values compared with the others two extraction methods (e.g. Linoleic acid).
ALA LA AA EPA DHA PGE2 LTB4
Plasma extract in Acetonitrile 2,4343 13,8193 2,4637 0,6856 1,4820 0,0045 1,9973 Plasma extract in Ethanol 1,9442 12,5513 2,1442 0,5567 1,2332 0,0093 1,6096 Plasma extract after
derivatization 1,9022 7,9542 0,4601 0,1456 0,3370 0,0056 0,3234 0,0000 2,0000 4,0000 6,0000 8,0000 10,0000 12,0000 14,0000 16,0000 Co n cen tr ati on (μ M)
Page | 29 4.3 – PLASMA FATTY ACIDS STABILITY ASSAY
Figure 1.10 – Illustrative graphic of plasma stability assay of fatty acids.
The graph shows the comparison of values of each selected FA concentrations in plasma after freeze/thaw cycles.
We can observe that all the represented lines are almost overlapped and the biggest difference shown is for EPA with 0.4793 µM of difference between fresh plasma and plasma thawed twice and a standard deviation of 27.69%. Except EPA, all the fatty acids in this study have less than 25.00% of deviation. More details are shown in the Table below.
Table 1.4 – Plasma stability assay and fatty acids levels.
0,0000 1,0000 2,0000 3,0000 4,0000 5,0000 6,0000 7,0000 8,0000 9,0000
ALA LA AA EPA DHA PGE2 LTB4
Co n ce n tr ation (µ M ) Fresh Plasma Defrost Once Defrost Twice
Fresh Plasma Plasma Defrost
Once
Plasma Defrost Twice
Name Trace RT Area Conc. RT Area Conc. RT Area Conc. %Std.Dev
1 ALA 445>337 9.42 346563.13 2.5796 9.40 408856.03 2.1871 9.42 418701.31 2.2394 21.31% 2 LA 447>239 9.76 5894711.50 7.7427 9.73 4989795.94 7.3469 9.74 5281799.38 7.7847 24.15% 3 AA 471>239 9.79 732005.58 5.6086 9.76 945300.28 5.6693 9.78 978620.95 5.8694 13.64% 4 EPA 469>239 9.46 268134.00 2.8882 9.44 446262.15 2.8876 9.46 520360.98 3.3675 27.69% 5 DHA 495>239 9.78 166237.97 3.2597 9.75 225053.40 3.6107 9.76 232443.80 3.7292 24.42% 6 PGE2 519>239 7.15 154.61 0.0007 7.15 27.13 0.0056 7.15 65.21 0.0057 0.28% 7 LTB4 503>323 8.00 36.12 0.0030 8.01 59.51 0.0031 8.02 52.83 0.0027 0.02%
Page | 30 4.4 – PLASMA DILUTION ASSAY
Figure 1.11 – Plasma dilution assay and fatty acids levels.
From the results obtained with this experiment and that are shown in figure 1.11, we can observe that the concentration has decreased in each dilution for all the FA with the exception of PGE2.
The purpose of this assay is to determine the variation of FA concentration in different dilution stages. EPA and DHA decrease their concentration in proportion of the dilution factor in comparison with ALA, LA and LTB4 that their concentration values are just in accordance with the expected values at x5 and x10 time dilution. AA shows good linearity in the results when the concentration of normal blood is not included.
ALA LA AA EPA DHA PGE2 LTB4
x10 Dilution 0,1537 1,7026 0,4453 0,0569 0,0703 0,0090 0,0007 x5 Dilution 0,3094 3,3203 0,8391 0,1304 0,1509 0,0091 0,0013 x2 Dilution 0,6218 5,0749 1,4099 0,2465 0,2696 0,0093 0,0019 Normal Plasma 1,0543 7,0037 1,8224 0,3624 0,5019 0,0092 0,0032 0,0000 1,0000 2,0000 3,0000 4,0000 5,0000 6,0000 7,0000 8,0000
Page | 31 4.5 – GENDER AND FATTY ACIDS PLASMA LEVELS
Figure 1.12 – Human plasma fatty acids levels in male and female gender.
In figure 1.12 we are able to compare the levels of FA and eicosanoids from the plasma of two volunteer. The FA levels are slightly similar between both the genders with a difference over 1µM in most of FA a part from ALA that shows the biggest difference. As we can see in the figure, FA levels in female plasma are a little higher when compared with male plasma.
More interpretation of the values is described in table 1.5 where are shown the total plasma average of each FA and is compared with the literature values taken from the website of Humam Metabolome Database30.
Table 1.5 – Comparison between FA and eicosanoids values (µM) from literature with the experimental results. The literature values are from the website human metabolome database30
ALA LA AA EPA DHA PGE2 LTB4
Female 0,9369 3,0412 2,1353 0,8373 1,10135 0,0002 0,02565 Male 0,20285 2,6728 1,63345 0,6315 0,9055 0,0002 0,0249 0 0,5 1 1,5 2 2,5 3 3,5
Conc
en
tr
at
ion
(
μ
M)
ALA LA AA EPA DHA PGE2 LTB4
Total plasma average 2,8494 14,2850 9,4219 3,6720 5,0171 0,0010 0,1264 Literature values 0.11 - 26.4 10.4 – 265 2.88 - 53.3 0.33 - 11.3 0.6 - 4.66 0.000025 - 0.0967 0.000037 - 0.0968
Page | 32 4.6 – HUMAN AND MOUSE PLASMA COMPARISON OF FATTY ACIDS
Figure 1.13 – Comparison between human plasma and mouse plasma fatty acids levels.
From what is shown in figure 1.13, the FA levels in mouse plasma are in higher concentration compared with human plasma. In human plasma the top concentration is LA instead of AA in mouse plasma.
Observing the table 1.6 below we can perceive that the biggest difference between the two plasmas is in AA and DHA. The difference calculation was made by subtracting the mouse plasma concentrations to human plasma concentration.
Human Plasma Mouse Plasma
Name Trace RT Area Conc. RT Area Conc. Difference
1 ALA 445>337 9.42 179536.77 0.9369 9.42 208565,66 1.0894 0.1525 2 LA 447>239 9.75 3484909.25 3.0412 9.75 5012173.75 4.3923 1.3511 3 AA 471>239 9.78 462649.25 2.1353 9.78 1269107.69 5.8635 3.7282 4 EPA 469>239 9.46 155901.78 0.8373 9.46 267879.53 1.4404 0.6031 5 DHA 495>239 9.77 92136.43 1.1014 9.76 298595.59 3.5899 2.4886 6 PGE2 519>239 7.15 62.86 0.0002 7.12 428.61 0.0014 0.0012 7 LTB4 503>323 8.03 27.22 0.0257 8.36 34.63 0.0261 0.0004
Table 1.6 – Comparison between human plasma and mouse plasma fatty acids levels.
ALA LA AA EPA DHA PGE2 LTB4
Female 0,9369 3,0412 2,1353 0,8373 1,10135 0,0002 0,02565 Female Mouse 1,0894 4,3923 5,86345 1,44035 3,5899 0,00135 0,02605 0 1 2 3 4 5 6 7
Conc
en
tr
at
ion
(
μ
M)
Page | 33
5 – DISCUSSION AND CONCLUSION OF RESULTS
When this project started the aim was investigate whether the krebs cycle acids would derivatize with AMPP to increase their sensitivity and detection on LC-MS/MS and then measure them in human plasma. We were unable to achieve this aim consistently so we concluded that it would be better for the project to continue with experiments using FA and the eicosanoids instead, as detailed by Bollinger et al.27,28
The FA related to cancer were researched to make the association between cancer research and lipodomics. Since all the work done so far by James G. Bollinger is related to AMPP derivatization realized and accomplished in mouse serum, we wanted to investigate using it to derivatize endogenous fatty acids and eicosanoids in human plasma.
The project with FA and eicosanoids started with the derivatization in buffer and LC-MS/MS method development (Data not shown). The results on LC-LC-MS/MS analysis of plasma derivatized fatty acids, showed good chromatography and concentrations were in the linear calibration range. We were able to derivatize FA and separate them by LC with relatively good intensity (see Figure 1.6 in results). Standard curves were made to prove that the derivatization was achieved in a linear fashion. (see Figure 1.7 and 1.8 in results).
We have also been successful derivatizing FA and eicosanoids in human plasma through AMPP derivatization and we have been able to measure them using LC-MS/MS.
Comparison of two extraction solvents acetonitrile and absolute ethanol and also derivatization before and after extraction showed that the highest concentrations were obtained with extraction with acetonitrile before derivatization except PGE2 (Figure 1.9). This way, all the followed experiments were made using acetonitrile to extract before derivatization.
Since the normal blood concentrations for PGE2 are very low (2.5x10-5µM to 0.00967µM) the MS detection is challenging; but because it is fundamental to relate PGE2 to cancer, we have reported the data. More information about PGE2, LTB4 and others FA and eicosanoids are shown in APPENDIX B in this report.
The plasma stability assay was carried out to ensure that no modifications are found in fatty acids plasma levels after submitted to freeze/thaw conditions. With the values we conclude that no significant changes were found for FA and eicosanoids concentrations in plasma after freeze/thaw once or twice before experimental utilization (Figure 1.10 and in
Page | 34 Table 1.4). The only FA that had shown a standard percentage deviation bigger than 25% is EPA with 27,69%.
After confirming that freeze/thawing does not compromise our samples, we performed a plasma dilution assay to verify that the values obtained are only from the plasma samples and identify of which dilution is more accurate to work with plasma for quantify them without supressing the detector. Observation of the data collected from this assay and shown in figure 1.11 we conclude that only PGE2 has not changed the concentration between dilutions and it is possibly due to is small concentration as explain above in the conclusion. We could that way prove that the FA analized were from plasma samples and not from reagents or others impurities. In most of the values the concentrations show to be proportional between x5 and x10 in all the FA. That possible means that on samples non-diluted or x2 non-diluted, their concentration in plasma still high and is supressing the MS detector. That way, in our future work (genders comparison and human/mouse comparison) we diluted the plasma in 5 folds dilution prior extraction and derivatization.
With all the experimental evolution done before and all the data obtained, we get more confidence in our results to move on and quantify the endogenous FA of a small population of 2 different samples with different gender (male and female plasmas). From the results obtained in this experiment and shown in Figure 1.12 we can deduce that the FA and eicosanoids selected for this project were successfully derivatized with AMPP, measured and analysed by LC-MS/MS. It is shown that values are very close between genders with less than 1µM of difference, except for ALA. But because it was used a small number of population for this assay, comparison between genders was inconclusive, giving just a representation of what possible is. Comparing our results with the one shown in literature (see Table 1.5) all the FA apart from DHA and LTB4 are in agreement with the values shown in literature.
Because almost of the FA and eicosanoids here mentioned are mainly obtained from the diet or from metabolites, it is very complicated to stipulate a unique concentration for each, as they depend on the individual diet, metabolism and life style. With that and with the short number of representative population used in the assay, we can explain the slightly difference of the low concentration obtained for LA. This explains also the differences found between genders and individuals in all the FA concentrations.
In pre-clinical research, animals like mice and rats play an important role in predicting effects in the human body. Due to that, is also important monitoring the FA and eicosanoids
Page | 35 levels in plasma. Figure 1.13 and Table 1.6 shows the results of the comparison between human plasma and mouse plasma fatty acids levels. The levels of all the FA are higher in mouse plasma than in human. In some of the cases such as AA, DHA and PGE2, the concentration appears to double in mouse when compared to human plasma. To be more confident about the results, comparison of human plasma/mouse plasma and other assays would need to be repeated with a major number of populations.
This project stills in an early stage and much more work will be done in the future. It will be interesting if we use a deuterated FA as an internal standard and verify ion suppression events. Is also important to amplify the number of samples used for compare genders and human/mouse plasma and validate our results. The signal comparison between derivatized/underivatized and +ESI/-ESI is also important to prove that the AMPP derivatization is crucial to improve the signal when plasma is quantified in LC-MS/MS. Comparison of FA levels between healthy/disease plasma needs to be done for better understand about the relation between cancer and FA levels in plasma.
In conclusion we have shown that derivatization of plasma with AMPP is a valid strategy to quantify FA and Eicosanoids in human and mouse plasma with higher sensitivity through positive ESI in LC-MS/MS. This is an advantage for lipidomics and all the medical research in allowing the access of accurate levels in plasma and also creates a metabolite profile of the patient. Changes in FA and eicosanoids derived of drug response or tumour growth will be now able to be reported with more accurate data.
Page | 36
6 – REFERENCES
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Cancer Risk in the SELECT Trial. Oxford University Press, 10.
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Page | 37
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[27] G. R. M. S. James G. Bollinger, "Liquid Chromatography/Electrospray Mass Spectrometric Detection of Fatty Acid by Charge Reversal Derivatization with More Than 4-Orders of Magnitude Improvement in Sensitivity," Journal of lipid research, p. 19, (2013).
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Page | 38
APPENDIX A
Page | 39
INSTRUMENTS
Mettler Toledo MX5 Analytical balance Mettler Instruments, Switzerland.
TECHNE DB.3 Dri Block TECHNE, Cambridge, U.K.
Kinetex™ C18 2.6u 100A 50x2.1mm column Phenomenex, Macclesfield, U.K.
Aquity H-Class LC system Waters Hertford, U.K. Xevo TQ-S Mass Spectrometer Waters, Manchester, U.K.
Gilson Pipettes Anachem, Luton, U.K.
Whirlimixer Vortex mixer Fisher Scientific, Loughborough, U.K. Titramax 100 Plate Mixer Heidolph Instruments, Germany Greiner and Abgene 96-Well plates and seals Fisher Scientific, Loughborough, U.K. Eppendorf Centrifuge 5810R Eppendorf AG, Hamburg, Germany.
CHEMICALS AND REAGENTS
Acetonitrile Biosolve, Valkenswaard, The Netherlands
Absolut Ethanol VWR Prolabo, Fontenay-sous-Bois, France Water (Ultra Purification System) Elga Ltd., High Wycombe, U.K.
Formic Acid, 99% Fisher Scientific, Loughborough, U.K.
N-(4-aminomethylphenyl) pyridinium (AMPP) Synthesized at ICR, Sutton Surrey, London, U.K. 1-Hydroxy-7-azabenzotriazole (HOAt) Sigma-Aldrich, Gillingham, U.K.
N-hydroxybenzotriazole (HOBt) Sigma-Aldrich, Gillingham, U.K. 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide
(EDC) Sigma-Aldrich, Gillingham, U.K.
N,N-dimethylformamide (DMF) Sigma-Aldrich, Gillingham, U.K.
Volunteer‟s plasma donors Obtained at ICR, Sutton Surrey, London, U.K.
Fatty Acids
- Linoleic acid (LA) - α-Linolenic acid (ALA) - Oleic acid (OA)
- Eicosapentaenoic acid (EPA) - Docosahexaenoic acid (DHA) - Prostaglandin E2 (PGE2) - Leukotriene B4 (LTB4)
Page | 40
APPENDIX B
Page | 41
FATTY ACIDS AND EICOSANOIDS INFORMATION29
Name
Structure
Chemical
structure
Molecular
weight
Normal blood
concentration
Linoleic acid (LA)
(18:2 Δ9,12 )
