Developments on Metallomic and Proteomic strategies for early diagnosis of Alzheimer's Disease

TESE DE DOUTORAMENTO DEVELOPMENTS ON METALLOMIC AND PROTEOMIC STRATEGIES FOR EARLY DIAGNOSIS OF ALZHEIRMER’S DISEASE María del Pilar Chantada Vázquez ESCOLA DE DOUTORAMENTO INTERNACIONAL PROGRAMA DE DOUTORAMENTO EN CIENCIA E TECNOLOXÍA QUÍMICA SANTIAGO DE COMPOSTELA 2019 DECLARACIÓN DO AUTOR/A DA TESE DEVELOPMENTS ON METALLOMIC AND PROTEOMIC STRATEGIES FOR EARLY DIAGNOSIS OF ALZHEIRMER’S DISEASE Dna. María del Pilar Chantada Vázquez Presento a miña tese, seguindo o procedemento axeitado ao Regulamento, e declaro que: 1) A tese abarca os resultados da elaboración do meu traballo. 2) De selo caso, na tese faise referencia ás colaboracións que tivo este traballo. 3) A tese é a versión definitiva presentada para a súa defensa e coincide coa versión enviada en formato electrónico. 4) Confirmo que a tese non incorre en ningún tipo de plaxio doutros autores nin de traballos presentados por min para a obtención doutros títulos. En Santiago de Compostela, 24 de maio de 2019 Asdo. María del Pilar Chantada Vázquez AUTORIZACIÓN DO DIRECTOR / TITOR DA TESE DEVELOPMENTS ON METALLOMIC AND PROTEOMIC STRATEGIES FOR EARLY DIAGNOSIS OF ALZHEIRMER’S DISEASE Dr. Antonio Moreda Piñeiro Dra. Pilar Bermejo Barrera INFORMAN: Que a presente tese, correspóndese co traballo realizado por Dna. María del Pilar Chantada Vázquez, baixo a miña dirección, e a utorizo a súa presentación , considerando que reúne os r equisitos esixidos no R egulamento de Estudos de Doutoramento da USC, e que como director desta non incorre nas causas de abstención establecidas na Lei 40/2015. En Santiago de Compostela, 24 de maio de 2019 Asdo. Dr. Antonio Moreda Piñeiro Asdo. Dra. Pilar Bermejo Barrera Mar en calma nunca fixo a un mariñeiro experto ACKNOWLEDGEMENTS Abbreviations………………………………………………………………………………………………………………….......i Abstract…………………………………………………………………………………………………………………...................vii I. Introduction………………………………………..……………………………………………………………………...........1 1. DEMENTIA, PROTEOMICS AND METALLOMICS………………………….....……………3 1.1 DEMENTIA…………………………………………………………………………………………………………………...5 1.1.1 Epidemiology of Dementia……………………………………………………………………….......6 1.1.2 Classification of Dementias……………………………………………………………………….....7 1.1.3 Diagnosis of Dementias………………………………………………………………………..................8 1.2 ALZHEIMER………………………………………………………………………..............................................................9 1.2.1 Etiology of AD………………………………………………………………………...........................................14 1.2.1.1 Neuropathological characteristics………………………………………………………....14 1.2.1.1.1 Aβ extracellular plaques or deposits………………………………………………..15 1.2.1.1.2 Neurofibrillary tangles……………………………………………………………………….......16 1.2.1.2 Complementary hypotheses…………………………………………………………………..…17 1.2.1.2.1 Cholinergic hypothesis……………………………………………………………………….......18 1.2.1.2.2 Inflammation………………………………………………………………………......................................18 1.2.1.2.3 Oxidative stress………………………………………………………………………..............................19 1.2.1.2.4 Metal homeostasis………………………………………………………………………......................19 1.2.2 Therapeutic approaches………………………………………………………………………..........20 1.3 BIOMARKERS………………………………………………………………..............................................………........22 1.3.1 AD biomarkers………………………………………………………………………........................................25 1.4 PROTEOMICS……………………………………………………………………….......................................................31 1.4.1 Preparation and prefractionate of the sample………………………….........33 1.4.2 Analysis by mass spectrometry…………………………………………………………….…34 1.4.3 Protein identification……………………………………………………………………….....................40 1.5 METALLOMICS……………………………………………………………………….................................................41 2. SPECTROMETRIC BASED TECHNIQUES FOR METAL-BINDING PROTEIN ASSESSMENT IN CLINICAL, ENVIRONMENTAL AND FOOD SAMPLES………………………………………………………………………................................................................................61 2.1 INTRODUCTION………………………………………………………………………...............................................62 2.2. DIRECT SPECTROMETRIC TECHNIQUES. ASSESSMENT OF METALPROTEIN INTERACTIONS………...........................…………………………………………………………….......63 2.2.1 Nuclear magnetic resonance spectrometry………………………………….......63 2.2.2 X-ray spectrometry based techniques…………………………………………….......67 2.2.2.1 X.-ray diffraction spectrometry………….....…………………………………………….......67 2.2.2.2 High-throughput techniques based on X-ray absorption spectrometry…………………………………………………………..............................................................…………….......69 2.2.3 Electron paramagnetic resonance………………………………………………....….......70 2.2.4 Circular dichroism………………………………………………………………….....................…….......71 2.2.5. Other spectrometric techniques………………….................................................…….......73 2.2.5.1. Fourier-transform infrared spectrometry…………………………………….........73 2.2.5.2. Single molecule force spectroscopy…………………………….………………….......74 2.2.5.3. Fluorescence spectrometry……………………………….....…………………………………..76 2.2.5.4. Atomic spectrometry……………………………………………………………...........………….......78 2.3. LC/ELECTROPHORESIS HYPHENATED TECHNIQUES………………………..78 2.3.1 Flame atomic absorption spectrometry (FAAS) and electrothermal atomic absorption spectrometry (ETAAS) ..…………....82 2.3.2 Inductively coupled plasma-optical emission spectrometry (ICP-OES) ………………………………………………………………………........................................................................84 2.3.3 Inductively coupled plasma-mass spectrometry (ICP-MS) .…85 2.3.4 Laser ablation-inductively coupled plasma-mass spectrometry……………...………………………………………………………..................................................................89 2.3.5 Isotope dilution ICP-MS………………………………………………………………………..........90 2.3.6 Mass Spectrometry………………………………………………………………………...........................92 2.4. CHARACTERIZATION OF NANOPARTICLE-PROTEIN ASSEMBLIES..................................................................................................................................................................................94 2.5. CONCLUSIONS……………………………………………………………………….................................................97 II. Objectives………………………………………………………………………...............................................................117 III. Results and discussion……………………………………………………………………….......................119 1. DEVELOPMENT OF DRIED SERUM SPOT SAMPLING TECHNIQUES FOR THE ASSESSMENT OF TRACE ELEMENTS IN SERUM SAMPLES BY LA-ICP-MS…………………......………………………………………..............................................................................123 1.1. INTRODUCTION………………………………………………………………………..........................................124 1.2. MATERIALS AND METHODS………………………………………………………………………....127 1.2.1 Instrumentation……………………………………………………………………….................................127 1.2.2 Reagents and materials………………………………………………………………………..........127 1.2.3 Serum collection and storage…………………………………………………………….......128 1.2.4 Dried serum spot sample preparation……………………………………………...128 1.2.5 Procedure for simultaneous multi-elemental analysis of DMS…………………………………………………………………..................................................................................…….......129 1.3. RESULTS AND DISCUSSION………………………………………………………………………........130 1.3.1 Influence of the paper card nature………………………………..……………………130 1.3.2 Influence of the ablated area and serum volume spotted on paper card ……………………………………..............................................................................................…………131 1.3.3 Use of oxidizers………………………………………………………………………....................................132 1.3.4 Optimization of laser ablation conditions……………………………...………134 1.3.4.1. Laser fluency………………………………………………………………………......................................134 1.3.4.2. Repetition rate………………………………………………………………………..................................135 1.3.4.3 Scan speed………………………………………………………………………................................................135 1.3.4.4 Spot size……………………………………………………………................…………......................................135 1.3.5 Calibration methods………………………………………………………………………...................136 1.3.6 Internal standards and accuracy…………………………….…......................................138 1.3.7 Limits of detection and quantification, repeatability and reproducibility……………………………………………………….................………………......................................139 1.3.8 Applications………………………………………………………………………..............................................140 1.4. CONCLUSIONS………………………………………………………….......……………......................................141 2. DISCRETE SAMPLING BASED-FLOW INJECTION AS AN INTRODUCTION SYSTEM IN ICP-MS FOR THE DIRECT ANALYSIS OF LOW VOLUME HUMAN SERUM SAMPLES…………………………………....................................149 2.1. INTRODUCTION…………………………………………………………………...…….....................................150 2.2. MATERIALS AND METHODS………………………………...………………......................................152 2.2.1 Instrumentation…………………………………………...………………………......................................152 2.2.2 Reagents and materials…………………………………………...………………………...............152 2.2.3 Serum samples…………………………………………...………………………...........................................153 2.2.4 SERUM ANALYSIS BY ICP-MS…………………………………………...…………................153 2.3. RESULTS AND DISCUSSION………………………………......………………......................................155 2.3.1. Selection of the loop volume and loading conditions.......................155 2.3.2. Selection of eluting conditions…………………………………..........................................156 2.3.3. Calibration methods and limit of detection and quantification…………………………………………...………………………...............................................................159 2.3.4. Precision and accuracy…………………………….......…………………......................................160 2.3.5. Application…………………………..............………………...………………………......................................164 2.4. CONCLUSIONS…………………………………………...………………………..................................................165 3. SERUM PROTEIN-BASED BIOMARKERS IN MILD COGNITIVE IMPAIRMENT AND ALZHEIMER’S DISEASE……………....…………......................................175 3.1 INTRODUCTION…………………………………………...…………...........……………......................................176 3.2 MATERIAL AND METHODS…………………………………………......................................................178 3.2.1 Clinical samples…………………………………………...…………….…………......................................178 3.2.2 Preparation of serum proteins……………………………….....…......................................178 3.2.3 2-DE…………………………………………...………………………............................................................................179 3.2.4 Image acquisition and software analysis of 2-DE gel........................180 3.2.5 Tryptic digestion…………..……………………………...………………………......................................180 3.2.6 Protein identification through MALDI-TOF…….......................................181 3.3 RESULTS…………………………………………...……………................................…………......................................181 3.3.1 High abundance protein depletion…………………………………………..................182 3.3.2 2-DE and gel image analysis………………………….....……………......................................184 3.3.3 MALDI-TOF identification of increased and decreased proteins in AD patients…………………………………………...……...……………......................................187 3.4 DISCUSSION…………………………………………...………………………............................................................195 3.4.1 Increased low abundant proteins in AD patients………………............196 3.4.1.1 Pigment epithelium-derived factor (PEDF) ……………………......................196 3.4.1.2 Ficolin-3 (FCN3) ……………………......................................................................................................196 3.4.1.3 Serotransferrin (TRFE) ……………………....................................................................................196 3.4.1.4 Haptoglobin (HP) ……………...............................................................................………......................197 3.4.2 Decreased serum proteins in AD patients……………………............................197 3.4.3 FunRich and String analysis…………...............................................…………......................198 3.5 CONCLUSIONS……………………...........................................................................................................................201 4. EXPLORING BIOMARKERS FOR ALZHEIMER´S DISEASE BY A QUALITATIVE AND QUANTITATIVE PROTEOMIC APPROACH…......................211 4.1 INTRODUCTION…………….................................................................................................………......................212 4.2 MATERIALS AND METHODS…..............................................................................................................217 4.2.1 Clinical samples….........................................................................................................................................217 4.2.2 Depletion of multiple high abundant proteins…......................................217 4.2.3 One dimensional SDS-PAGE (1-DE).….....................................................................218 4.2.4 Tryptic digestion….......................................................................................................................................218 4.2.5 Protein identification by mass spectrometry (LC-MS/MS) and data analysis….......................................................................................................................................................219 4.2.6 Protein quantification by SWATH (Sequential Window Acquisition of all Theoretical Mass Spectra) ...............................................................220 4.2.6.1 Creation of the spectral library…...........................................................................................220 4.2.6.2 Relative quantification by SWATH acquisition…......................................221 4.2.6.3 Data analysis…................................................................................................................................................221 4.3 RESULTS AND DISCUSSION…..................................................................................................................223 4.3.1 Proteins identified in serum samples by shotgun proteomic techniques…..............................................................................................................................................................................223 4.3.2 Protein quantification by SWATH analysis…................................................238 4.3.3 Differentially expressed serum proteins…............................................................239 4.3.3.1 Apoliprotein A-II…...................................................................................................................................248 4.3.3.2 Other significant proteins…..........................................................................................................249 4.4 CONCLUSIONS….......................................................................................................................................................254 5. SCREENING OF METAL-PROTEIN COMPLEXES TWO-DIMENSIONAL POLYACRYLAMIDE GEL ELECTROPHORESIS AND LASER ABLATIONINDUCTIVELY COUPLED-PLASMA MASS SPECTROMETRY.................................263 5.1 INTRODUCTION…...................................................................................................................................................264 5.2 MATERIALS AND METHODS…...............................................................................................................266 5.2.1 Instrumentation….........................................................................................................................................266 5.2.2 Reagents and materials…..................................................................................................................267 5.2.3 Serum collection and storage…...............................................................................................267 5.2.4 Depletion of multiple high abundant proteins….........................................268 5.2.5 2-DE…...............................................................................................................................................................................268 5.2.6 Procedure for simultaneous multi-elemental analysis of protein spots in the 2-DE gels…..............................................................................................................269 5.3 RESULTS AND DISCUSSION…..................................................................................................................270 5.3.1 Optimization of laser ablation conditions….......................................................270 5.3.1.1 Spot size and ablation depth…...................................................................................................271 5.3.1.2 Laser fluency, repetition rate and scan speed…...............................................272 5.3.2 Screening of protein spots in two-dimensional gels by LA-ICPMS…...........................................................................................................................................................................274 5.3.3 Application….........................................................................................................................................................275 5.4 CONCLUSIONS….......................................................................................................................................................280 IV. Conclusions…..............................................................................................................................................................287 V.Annex I….................................................................................................................................................................................293 VI. Annex II….........................................................................................................................................................................305 ABREVATIONS i 1D-PAGE: one-dimensional gel electrophoresis 2D-NMR: two-dimensional NMR 2D-PAGE: two-dimensional polyacrilamide gel electrophoresis 3D-NMR: three-dimensional NMR A1AT: alpha-1-antritrypsin A2M: alpha-2-macroglobulin AChEI: acetylcholinesterase inhibitors ACTB: actin cytoplasmatic 1 AD: Alzheimer´s disease ADH: alcohol dehydrogenase AEC: anion exchange chromatography AFM: atomic force microscopy AHSG: alpha-2-HS-glycoprotein ALBU: serum albumin ANS: amino-2-naphthol-4-sulfonic APDC: ammonium pyrrolidinedithiocarbamate Apo A-II: apoliprotein A-II APP: amyloid precursor protein Asp: aspartic acid ATH: avian thymic hormone Aβ: β amyloid protein BMIMBr: 1-butyl-3-methylimidazoliumm bromide BMIMCI: 1-butyl-3-methylimidazoliumm chloride BSA: bovine serum albumin C9JKR2: CRA_k isoform CA: carbonic anhydrase CaM: calmodulin Cap: capillary CCDs: charge-coupled devices CD: circular dichroism CD5L: CD5 antigen-like CE: collision energy ix Abstract Alzheimer's disease (AD) is considered the new epidemic of the 21st century because of the increase on the year’s life expectancy which generates a remarkable increase of patients suffering this pathology. Etiology of AD is still unknown, but it is well-known that there are several factors related to AD. Regarding treatments, there is no medication that recovers patients, although there are available several drugs that diminish patient deterioration which are applied at advanced stages of the disease. Therefore, the knowledge of the behavior of this disease in the early stages is a goal in biomedical research. There are several studies that compare healthy people with patients who suffer AD but currently these studies are focused on knowing the evolution of the disease, that is, in studying the differences between an early stage such as mild cognitive impairment (MCI) and AD. The objective of this thesis has been focused on optimizing several techniques (multi-element analysis and proteomics) for searching potential biomarkers for an early AD diagnosis. The two first chapters have been devoted to the optimization of methods for the determination of several elements, which led to successful results (good precision and accuracy). The developed techniques differ in the sample introduction mode in ICP-MS, but both share short analysis time for assessing simultaneously several elements, and the use of very small sample volume. The first technique consists of using paper as a support for serum samples (dried blood spot, DBS) followed to introduction in ICPMS after laser ablation (LA-ICP-MS). The second technique is based on discrete sampling for ICP-MS. Both techniques require very small sample volumes, short analysis times, as well as minimum sample handling and sample conservation. In the second part of this thesis regarding proteomics, we have been able to find several proteins for differentiating three groups of population (healthy people, AD patients, and MCI patients). The study x has been addressed qualitatively and quantitatively, and these preliminary results are first step for designing a panel of biomarkers. In a final chapter, LA conditions have been optimized for the determination of metals in the isoforms of two proteins [Serotransferrin (TRFE) and Keratin type II cytoskeletal 1 (K2C1)] after 2D electrophoresis. The current research is therefore a first step to study the differences between groups of patients suffering AD using elements and proteins as discriminating features, but also the levels of metals bound to proteins. I. INTRODUCTION 1. DEMENTIA, PROTEOMICS AND METALLOMICS I. Introduction 5 1.1 DEMENTIA Dementia is defined as the acquired and sustained impairment of the cognitive capacities in a patient, which hinders the satisfactory completion of daily activities. In this sense, this illness must be distinguished from mental retardation and delirium. Dementias can be so severe that they significantly affect patients social sphere, family and work [1, 2]. A differential diagnosis must be made with mild cognitive impairment (MCI), a state which could increase the risk of dementia and can manifest itself through normal memory disorders and cortical function disorders, without meeting the diagnostic criteria for dementia. In some cases, MCI is associated with 12-15% increased risk of dementia per year, in comparison with 2% community risk [3]. A major problem with dementias is that they have multiple diagnosis. Thus, the establishment of clinical and paraclinical criteria is needed. Dementias could be degenerative, as Alzheimer’s Disease (AD), Pick’s Disease (PD), Parkinson’s Disease (PDi), Huntington’s Disease (HD) and the Progressive Supranuclear Palsy (PSP), among many others; acquired, such as Vascular Dementia (VascD), Multiple Sclerosis (MS), intracranial neoplasia, traumatism, hydrocephalus, and prions, among others; and potentially reversible, such as toxic/metabolic disorders (hypothyroidism, kidney dialysis, vitamin B12 deficiency, alcoholism, malnutrition, etc.), infectious diseases (HIV/AIDS, neurosyphilis, tuberculosis, cryptococcosis, viral encephalitis, etc.) and major depression [2]. The main cause of degenerative dementia (60%) is AD, followed by VascD, which accounts for 12.5-27% of cases. Other frequent degenerative dementias are frontotemporal dementia, which is present in young individuals, and Lewy Body Dementia, present in individuals over 65 [4]. It is crucial to understand that, currently, very few people are familiar with the care and treatment of a person with dementia. As yet there is no cure for this disease and therefore this generates severe MARÍA DEL PILAR CHANTADA VÁZQUEZ 6 disabled patients with a high dependence on their caregivers, whose quality of life is also affected [5]. The worldwide cost of dementia has increased from 604 billion dollars in 2010 to 800 billion dollars at present, which means an increase of 35.4%. This amount represents about 1.09% of global gross domestic product (GDP). Even these expenses may seem unbearable, it is calculated that the total costs will exceed a trillion dollars in 2030. In Western Europe, 50.8 million dollars are invested in direct health care (about 19.6% of total health expenditure), 113 millions in direct costs of care (about 43% of the total), and 98.9 millions in costs of informal care (about 37.6% of the total) [6]. Diagnostic services are still insufficient and become a barrier for the adequate provision of care to dementia patients. Although currently there are no treatments that modify the disease, the correct and timely diagnosis is a precondition to have access to supporting services (e.g., subsidized home upgrades) and to symptomatic treatment. Only 20 to 50% of dementia patients are estimated to have a documented diagnosis at primary care, this ratio being substantially lower in developing countries [7]. 1.1.1 Epidemiology of Dementia In 2011 there were 36.5 million people with dementia, figure which is increasing. The prevalence is 2-3% between the ages of 70 to 75, and it dramatically increases to 20-25% when people are 85 or older [8]. It is estimated that there will be 42 million patients with dementia in 2020, and 81 million patients in 2040 [9, 10]. Several epidemiologic studies mention that about 2 billion people worldwide will be over 60 years old in 2050 [5]; that is why a drastic increase in the number of patients with dementia is expected (Figure 1). I. Introduction 7 Figure 1. Estimated number of people living with dementia worldwide. The data is shown in millions (Figure taken from reference 9). 1.1.2 Classification of Dementias Dementias must be regarded as a progressive multifactorial syndrome, produced by several diseases rather than just one. The traditional concept of dementia has to main categories: a) neurodegenerative dementias considered irreversible and b) nonneurodegenerative or potentially reversible dementias (Table 1) [11]. 10 20 30 40 50 60 70 80 90 100 1995 2005 2015 2025 2035 2045 MARÍA DEL PILAR CHANTADA VÁZQUEZ 14 appear at the age of 30 to 60 or 65, whereas in the AD of late onset, which is the most common form of AD, the symptoms appear at the age of 60 or 65 at the earliest. Both can happen in people whose families are affected by Alzheimer. In spite of the fact that first-degree relatives of AD of late onset patients double the risk of developing the disease, the transmission pattern is rarely consistent with the Mendelian inheritance. In contrast, approximately 60% of cases of AD of early onset have multiple cases of AD in their families, and 13% of these cases are inherited in an autosomal dominant manner, with at least three generations affected [27]. 1.2.1 Etiology of AD 1.2.1.1 Neuropathological characteristics Many neurodegenerative disorders are characterized by the formation of insoluble deposits and oligomers composed by individual amyloidogenic proteins as diverse as β amyloid protein (Aβ), tau, prion protein (PrP), α-synuclein and huntingtin [28]. At a neuropathological level, Alzheimer’s disease is characterized by the appearance of two characteristic structures: I) Aβ extracellular plaques or deposits and II) neurofibrillary tangles (Figure 2) [29]. The brain tissue shows “neurofibrillary tangles” (twisted fragments of protein within neurons that clog them up), “neuritic plaques” (abnormal clusters of dead and dying neurons, other brain cells and proteins) and “senile plaques” (areas where products of dying nerve cells have accumulated around proteins). Although these changes happen to a certain extent in all brains as a result of ageing, they are much more common in the brains of people with Alzheimer’s disease [30]. I. Introduction 15 Figure 2. Cortical sections of the brain from a patient affected by AD. (a) Senile plaques are labeled with a specific antibody for Aβ and (b) neurofibrillary tangles were stained with a specific antibody for phosphorylated tau (Figure taken from reference 31). 1.2.1.1.1 Aβ extracellular plaques or deposits Aβ extracellular plaques or deposits are complex structures consisting of accumulated Aβ peptide, specifically Aβ 40 and Aβ 42, which is characterized by the presence of 39 and 43 amino acids and molecular weight of 4 kDa in its central part [32]. Aβ peptide originates from an abnormal proteolytic rupture of the amyloid precursor protein (APP), a transmembrane glycoprotein with a membrane domain and a short cytosolic domain [33] (Figure 3). The amyloid cascade hypothesis considers that the processing of APP can occur by two metabolic pathways: a non-amyloidogenic pathway, or an amyloidogenic pathway. The former is the most frequent and is produced by the action of α-secretase, which cleaves the APP in the amino acid 83 from the C-terminal, generating two fragments: Nterminus and C-terminus. The former is secreted in the extracellular medium (sAPPα), and the latter, a C-terminal fragment of 83 amino acids (C83), is retained in the membrane and subsequently cleaved by a γ-secretase, producing a small soluble peptide (p3). However, if the processing takes place by an amyloidogenic pathway, the first proteolytic cleavage is mediated by β-secretase and located 99 amino acids away from the C-terminus. Thus, there is a release of sAPPβ and a membrane-bound fragment containing 99 amino acids (C99) is MARÍA DEL PILAR CHANTADA VÁZQUEZ 16 generated. Then, C99 is processed by γ-secretase. This process may lead to a proteolytic cleavage in different positions. As a result, there is a release of Aβ peptide, which may contain between 37 to 49 amino acids (Aβ37Aβ49), being Aβ40 the most frequent fragment. In any case, these aggregated Aβ peptides in small oligomeric structures give rise to the typical senile plaques observed in AD [34-36]. Different mutations related to the disease were found in APP proteolytic cleavage areas. They were identified as α-, βand γ-secretases [37]. Figure 3. Scheme of the processing mechanisms of amyloid precursor protein (APP): non-amyloidogenic pathway and amyloidogenic pathway. The latter allows the generation of β-amyloid peptide (Aβ) and the formation of senile plaques (Figure taken from reference 38). 1.2.1.1.2 Neurofibrillary tangles Neurofibrillary tangles are intracellular deposits formed by residues of hyperphosphorylated tau protein. In AD neurofibrillary tangles are more common in the areas of higher neuronal destruction, such as the hippocampus and the area of the temporal lobe [39]. Tau is a cytosolic protein which is part of the structure of microtubules present in the neurons, and it is widely present in the central nervous system. A characteristic of this protein is that it presents many areas of Cytosol Non-amylodogenic pathway Amylodogenic pathway α-secretase γ-secretase APP sAPPαβ-secretase γ-secretase APP sAPPβ Aβ C83 C59 C99 C59 p3 Extracellular space I. Introduction 17 phosphorylation, some of which can modify the union of the protein to the microtubules. Tau hyperphosphorylation produces the accumulation of fibrils forming tangles, which do not allow microtubule stabilization. This creates disruptions in the nutrient transport, and nerve signals, axon degeneration, neurotoxicity and, finally, cognitive impairment, typical of this pathology [39, 40] (Figure 4). Figure 4. Neuronal death due to the destabilization of microtubules by tau hyperphosphorylation (Figure taken from reference 41). In this context, currently three cerebral spinal fluid (CSF) markers of AD are included in research guides, and their use as inclusion criteria and/or outcome measures in clinical trials is increasing. These are Aβ - 42, total Tau (t-tau) and Tau phosphorylated at threonine 181 (p-tau) [4245] 1.2.1.2 Complementary hypotheses Even so, the etiology of AD is unknown. There is growing evidence of the neuropathological processes explained above being the main cause of this disease, although it is well-known that genetic, Microtubules Tau Neurofibrillary tangles Tau binds and stabilizes microtubules Loss of tau bindings with microtubule dissociation Defective microtubules Healthy neuron Diseased neuron MARÍA DEL PILAR CHANTADA VÁZQUEZ 18 environmental and ageing factors may have an influence on this pathology. Traditionally, it has been considered that AD specifically affects the central nervous system, but recent studies are starting to consider that it may also affect the peripheral system. So, in the last years there has been a growing concern and interest in studies using biological fluids, such as serum or blood plasma, CSF, peripheral cells (e.g. lymphocytes, skin fibroblasts) and non-brain tissue [46]. There are many studies that relate the progression of AD, not only with pathologies such as plaques or extracellular Aβ deposits or neurofibrillary tangles, but also with other mechanisms [47]. Thus, other complementary hypotheses that try to explain the complex etiology of AD have been proposed. Some of these hypotheses are the cholinergic hypothesis, oxidative stress, inflammation, or the involvement of the metabolism of different metals. These hypotheses give a global view of the multiple pathological mechanisms behind the disease (Figure 5). 1.2.1.2.1 Cholinergic hypothesis This is one of the most ancient pathologies, and it is related to the decrease of the neurotransmitter acetylcholine, as a result of the loss of cholinergic neurons in the hippocampus and brain cortex. Acetylcholine plays an important role in the functioning of memory and learning, which decreases in AD patients. This is one of the main hypotheses, as it has been a basis for the development of the most widely used drugs in palliative care of AD [48]. 1.2.1.2.2 Inflammation Neuroinflammatory processes were first related to AD many years ago, but there is great controversy over whether these processes cause the neuronal damage observed in this disease or they are just a natural protection response against other pathological processes (e.g. deposition of Aβ, formation of neurofibrillary tangles). These inflammatory mechanisms seem to be caused by overactive glial cells, I. Introduction 19 which produce a huge amount of pro-inflammatory molecules such as cytokines, the complement system and eicosanoids. Thus, the levels of interleukins, tumor necrosis factor and other cytokines increase in brain tissue of patients with AD. However, these disruptions have not only been found in the central nervous system, but also in the peripheral one [49,50]. 1.2.1.2.3 Oxidative stress It is a molecular mechanism present in many diseases. Oxidative stress which is consider an imbalance between free radicals and antioxidants mainly, reactive oxygen species (ROS) and reactive nitrogen species (RNS), caused a default in organisms protection. It is present in many diseases including AD. Thus, the brain has proved to be highly susceptible to oxidative stress, due to its high metabolic capacity, high concentration of easily oxidizable substrate and scarcity of antioxidant compounds, in comparison with other tissues. So, numerous oxidative stress markers have been proposed as potential evidence for AD diagnosis. These markers can be simultaneously identified in both the central and peripheral nervous systems. These include the production of isoprostanes, protean carbonyls, 3-nitrotyrosine, thiobarbituric acid reactive substances, oxidized bases and decreased levels of antioxidants, among others [51-53]. 1.2.1.2.4 Metal homeostasis Metal elements play a crucial role in the development of the AD [54], involving both essential elements and toxic species. The involvement of the homeostasis of iron, copper and zinc in the pathogenesis of AD has been widely documented, as these essential metals take part in the proteinopathies which characterize this neurodegenerative disorder. These metals develped in the amyloid deposits in the brain, causing Aβ peptide aggregation and senile plaque deposition [55]. MARÍA DEL PILAR CHANTADA VÁZQUEZ 20 Moreover, have also been established relations between AD and different metals with a protective function. In this sense, it has been shown that there exists a negative correlation between cognitive impairment and levels of selenium and the activity of different selenoproteins in AD patients [56]. Furthermore, zinc has been reported to play a neuroprotective role –due to its antioxidant capacityagainst Aβ-induced cytotoxicity in spite of its neurotoxic properties described above. This paradox proves the high complexity of the pathological mechanisms associated to AD [57, 58]. Figure 5. Pathological mechanisms implicated in AD. 1.2.2 Therapeutic approaches Despite the scientific and clinical advances in the study of AD in the last 30 years, the available treatments are symptomatic; that is, they just alleviate the symptoms of the disease [59]. During the last decade, from 1998 to 2011, about 100 compounds, tested with the aim of altering the progression of the disease, have failed when they were in phase of clinical development [60, 61]. The reason for their failure could be explained by the complexity of the disease, due to its multifactorial etiology and its fisiopathological complexity. Finding a suitable drug, effective in all the trial population, is a challenging task. I. Introduction 21 Although certain key aspects of the pathogenesis of AD remain unsolved, the scientific advances in the last 25 years have made it possible to establish several strategies for the development of treatments with potential to alter the progression of AD [62]. Thus, among all the different therapeutic approaches in progress, those aimed to decrease the formation of Aβ42, and tau protein phosphorylation are the most promising [45]. These two types of disruptions are the best studied in this field, and may be the key for AD treatment in the near future. Currently, there are only four drugs approved for the AD treatment. These belong to two groups: acetylcholinesterase inhibitors (AChEI) and N-methyl-D-aspartate receptor (NMDAR) antagonists [61, 63]. However, new treatments and therapeutic approaches are being investigated with the aim of stopping the disease progression, focusing on different targets and the drug administration in the early stages of AD. To enable effective treatments to be developed, new diagnostic techniques need to emerge. They must allow early diagnosis of AD in a preclinic phase (before symptoms begin to manifest) or even predict AD development. AD prevention is a realistic challenge for researchers. However, to make it possible, it is necessary a better understanding of its etiology and of the extent to which environmental influences and life style have an influence on the risk of developing this disease [64]. Nowadays, in addition to those mentioned above there exist many therapeutic approaches under consideration in order to address AD (Figure 6). There are as many approaches as hypothesis that try to explain this complex disease because, so far, researches have neither been able to elucidate the mechanisms involved in this pathology nor its origin. MARÍA DEL PILAR CHANTADA VÁZQUEZ 22 Figure 6. Current therapeutic strategies in AD. 1.3 BIOMARKERS In the field of human health, the development, validation and use of biomarkers as information tools for evaluating risk factors associated to environmental exposures increase every day, due to the need of knowing the adverse effects generated by the different work environments and life styles. Nowadays it is known that many diseases related to life styles; that is, in many occasions environmental exposure, food, physical activity or even geographic location are key factors in the manifestation and development of certain diseases such as several carcinogenesis, teratogenesis, genotoxicity, nephrotoxicity, neurotoxicity or I. Introduction 23 immunotoxicity, among others [65]. The risk of suffering health deterioration can be evaluated thanks to the use of biomarkers and it is expressed as the probability of an undesirable effect happening as a result of an exposure [66]. There are many and very diverse definitions of “biomarker”. A wide and inclusive definition can be: “characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic pathways, or a biological response to a therapeutic intervention”, which takes place at a cell or molecular level, and it is also associated with the probability of developing a disease [67]. Biomarkers are used for the understanding of different diseases in various aspects such as their treatment, prevention, diagnosis and progression, responses to therapy, experimental toxicological assessment of drugs and pesticides, measurement of environmental and epidemiologic risk, and assessment of therapeutic intervention, among others [68]. It is essential for a biological marker to be validated before its use in human health studies. Thus, its selection and validation require careful consideration. Its specificity, sensitivity and reliability as a risk measure need to be considered, establishing the correctness, accuracy and quality assurance of the analytical process and the interpretation of the measured data [69-73]. The factors that need to be considered in the selection and validation of a biomarker are [70]: i. Identification and definition of the relevant biological process; ii. Previous studies regarding the exposure agent, the biomarker and the effect to be assessed (in vitro studies in humans and other organisms); iii. Identification of the variable to be quantified, to assess marker sensitivity and specificity in relation to the exposure; Table 3. Summary of selected candidate AD fluid biomarkers (Table taken from reference 48) (Continued). Biomarker Stage of clinical validation Levels in AD vs. healthy controls Stage of assay development CSF Plasma/ Serum VILIP-1 Several studies on CSF - Inconsistent results in CSF but most studies showed an increase. - Increased in plasma but data are limited. Commercially available assays Commercially available assays Inconsistent results NF-L Several studies on CSF - Consistently increased in CSF. - Increased in plasma but data are limited. Commercially available assays (IVDs in Europe) Commercially available assays Consistent results Few studies on plasma or serum Aβ38 amyloid beta 38, Aβ40 amyloid beta 40, Aβ42 amyloid beta 42, AD Alzheimer’s disease, BACE1 β-site amyloid precursor protein cleaving enzyme 1, CSF cerebrospinal fluid, hFABP heart-type fatty acid-binding protein, IP-10 interferon-γ-induced protein 10, IVD in vitro diagnostic, IWG-2 International Working Group 2, NF-L neurofilament light, P-tauphosphorylated tau, SNAP-25 synaptosome-associated protein 25, TDP-43 transactive response DNA-binding protein 43, TREM2 triggering receptor expressed on myeloid cells 2, T-tau total tau, VILIP-1 visinin-like protein 1. 30 MARÍA DEL PILAR CHANTADA VÁZQUEZ 31 1.4 PROTEOMICS The Human Genome Project revealed that the human genome is composed by approximately 3x109 base pairs or nucleotides far, they have sequenced 25,000 genes that codify proteins, which represents 1.5% of the genome, whereas the rest are repeated DNA sequences which do not codify or codify regulatory sequences and introns for noncoding RNA [93, 94]. The completion of this project led to the current post-genomic era, when new research fields such as functional genomics, comparative genomics, transcriptomics, metallomics and proteomics, started to grow by leaps and bounds. In 1994, Wilkins introduced the term proteome as a linguistic equivalent of genome [95]. The proteome defines the set of proteins that can be expressed by a genome. Unlike the genome, which is always the same in each cell throughout life, the proteome is an extremely dynamic element. It undergoes variations within a single organism, tissue, cell or subcellular compartment, in response to environmental and physiological factors such as age, stress conditions, toxic agents, drugs or hormones [96, 97]. The analysis of the proteome is a difficult task, due to the high number of existing proteins. As a single gen may codify multiple proteins, the proteome is estimated to have a more complex order of magnitude than the genome [98, 99]. Thus, a typical proteome may contain tens of thousands of unique proteins, which are present in a wide range of concentrations [100]. Once the proteins are synthesized, they may undergo modifications in their structure or in their basic sequence by proteolytic processes, as well as post-translational modifications, including methylations, phosphorylations and acetylations. All the above helps us to understand the high complexity of the proteome in a cell [92]. I. Introduction MARÍA DEL PILAR CHANTADA VÁZQUEZ 32 Proteomics is a tool endowed with a set of techniques and instruments with a high resolving power. For a robust proteomic analysis, it is necessary to optimize the current techniques. Moreover, their development and assessment are also needed in order to apply them in routine analysis, especially in the identification of biomarkers [101]. The strategies used or applied to the biological samples are vital, due to their great complexity. Thus, the analysis and characterization of the proteome will also be influenced by the sensitivity of the equipment used. In most cases, it is necessary or even obligatory the prefractionate of the sample, due to its complexity and the need for high resolution, as well as good results [102]. The application of proteomic techniques in the medicine field is called clinic proteomics, and its main aim is the identification of new biomarkers [103]. Proteomics provide information on the changes in the specific protein expression profiles and on the mechanisms associated to them in different cell stages (healthy and diseased), with the long term aim of developing better diagnosis methods and treatment of diseases [104]. This is not an easy task as the whole sequence of the complete human proteome is not still available (we only know a small part of the proteins present in the human body). Currently, the “Human Proteome Project” (HPP), which is focused in the characterization of the known 21,000 gens of the human genome, is being developed. The target of this project is to generate protein maps based on the molecular structure of the human body. The HPP will allow us to explain the biological function of proteins and how their modifications are affected under different cellular conditions [105]. The study areas of proteomics are wide and diverse. Among them, protein function, protein-protein interaction analysis, and their role in the metabolic and signaling pathways are worth-mentioning. All these have a vital importance to understand the role of proteins in biological processes [105-108]. I. Introduction 33 There is a wide range of strategies or workflows to carry out a proteomic analysis. Exists such robust proteomic techniques that they comprise most of the range in protein concentration in a complex biofluid, from ultrasensitive (~0.05 pg / mL) to extremely abundant (~ 50 mg / mL). Conducting an experimental selection and a careful design has great importance in order to maximize the probability of quantifying precisely an object of interest (Figure 7). Figure 7. Chart of the main proteomics and molecular biology techniques according to their suitability to detect different concentration ranges in biofluids analytes (Figure taken from reference 94). Usually, the experimental design of a proteomic analysis has the following stages: preparation and splitting of the sample, an analysis by mass spectrometry (MS) and computer analysis of the data (identification of proteins) [106]. 1.4.1 Preparation and prefractionate of the sample Sample preparation before its analysis is one of the most critical points of the work in a proteomic laboratory. Pre-analytical factors are those which take place before the sample analysis, including collection methods and materials, hemolytic contamination of the samples, sample MARÍA DEL PILAR CHANTADA VÁZQUEZ 34 handling, storage temperature, thaw conditions and sample stability before processing. All these factors may affect accuracy and precision of the measured analytes [110-111]. The proteins integrity varies a lot with the frost/thaw cycle, which is specific of each proteomic platform, depending on the detection sensitivity. Ideally, sample collection methods and timing have to be strictly controlled to minimize diurnal effects, as well as taking into account the differences in the protein concentration between fasting and non-fasting, which may affect hormone, triglyceride and other marker levels. Certain protein levels may vary a lot from day to day, for this reason, it is important to examine the bitemporal stability of proteins before validating the results [113]. Most of the biological samples are very complex protein mixtures which cannot be directly analysed. Consequently, prefractionation is a crucial step in proteomics. The main problem of this type of samples is the masking of low-abundance proteins by high-abundance ones when, with the aim of finding a biomarker, the best expected results are found in the minority protein fraction. There are a number of techniques applied for this purpose, of which the recent use of nanotechnology (use of bare or functionalized nanoparticles), the one/two-dimensional gel electrophoresis and the high-performance liquid chromatography (HPLC) must be highlighted [114]. 1.4.2 Analysis by mass spectrometry MS is an analytic technique that emerges in the 60’s, but it is widely known in the 70’s. In the field of biological sciences, this technique has no relevance until the 90’s since it is in this decade when the ionization techniques allow for the protein and peptide analysis. It is a widely used technique in many and different scientific areas, such as biomedicine, environment, pharmaceutical companies, food sector, toxicological analysis and even in the world of cosmetics [115]. The main feature of MS is that it not only provides the accurate molecular weight of the compound, but also its structural data, and in I. Introduction 35 many occasions, it is used for the structural analysis of certain substances or species [116]. That is, it is an analytic technique that provides quantitative and qualitative information of the analysed molecules. A mass spectrometer is composed by three basic functional units: the ionization source, the mass analyser and the detector (Figure 8). Figure 8. Parts of the mass spectrometry equipment. The ionization source is the part that turns molecules into ions in the gaseous phase by means of gain or loss of charge (for example, loss of electrons, desprotonation or protonation). There are multiple ionization methods. They depend on the nature of the sample and the molecules to be detected in the analysis. In the case of biomolecular analysis in liquid and solid samples the matrix-assisted laser desorption/ionization (MALDI) and the electrospray ionization (ESI) are the most commonly used [117]. - MALDI. It was developed by Karas and Hillenkamp in the late 80’s when observing the alanine co-adsorption. It was only expected to Ionization Source MassAnalyzer Detector Computer System Ion generation Ion separation Ion detection Data processing Mass spectrum MARÍA DEL PILAR CHANTADA VÁZQUEZ 36 visualize tryptophan at a wavelength of 266 nm, but it gave rise to soft ionization [118]. This technique consists on mixing analyte with an organic compound -such as sinapinic acid or α-cyano-4-hydroxycinnamic acid called matrix in the presence of an organic solvent, and the deposit of the mixture is a metal plate. When the organic solvent evaporates, the matrix co-crystallizes with the analyte. The plate is introduced in the ionization chamber of the mass spectrometer, which is under hardvacuum conditions, and an ultraviolet (UV) laser is applied in it. The crystalized mixture absorbs the laser energy, producing analyte ions in the gaseous phase, most of them having just one positive charge. This results in an ion beam which is oriented and redirected to the ion analyzer (Figure 9) [117]. Figure 9. Major proposed models for MALDI ionization; [I] Gas-phase protonation, showing the charge transfer from the ionized matrix (mH+) to the analyte (A). [IIa] Direct desorption of the preformed singly charged analyte (AH+). [IIb] The multiply charged analyte (AHn+), where incomplete neutralization by the counterions (X−) or electrons occurs in the gas phase, producing the singly charged analyte ions (AH+). (A = analyte, m= matrix, x− = counterion). Desorption Gas phase AH+ AH+AH+AH+ AH+ A AAH+ AH+ AmH+ Ahn+ n+ XXXXXXXXmH+ mH+ mH+ mH+ mH+ mH+ A IIIa IIb Mass Analycer Sample Plate I. Introduction 37 - ESI. This technique emerged in the late 60’s, with the experiments made by Dole and his collaborators [119]. However, it is not until 2002 when John Fenn publishes an interesting article on the personal and historical memories of the pros and cons of ESI [120], being awarded the Nobel Prize in Chemistry together with Koichi Tanaka. It is an ionization technique in which analytes are in dissolution. This dissolution is drawn through a thin stainless-steel capillary to which a high electric potential -around 5.000 wattsis applied. The result is the nebulization of the dissolution. Thus, small charged drops are formed in the form of spray and subjected to a high-temperature gas stream, which removes the solvent and leaves protoned analytes in gaseous phase. The generated ions are later accelerated in an electric field towards the mass analyzer (Figure 10) [117]. Figure 10. Schematic diagram of an ESI source. ESI ionization allows direct connections with the mass spectrometer such as the capillary electrophoresis and the liquid chromatography, which facilitate and make it possible the massive identification of analytes in very complex samples. Nowadays, the MALDI technique is still being used, but the number of proteins which is able to identify is much lower. Thus, sample simplification by prefractionation techniques such as 2-DE or liquid chromatography in this case is needed [121]. Nebulizing gas Nebulizing gas SAMPLE Atmospheric pressure Intermediate vacuum High vacuum Drying gas Mass Analycer Curtain plate Orifice Skimmer High voltage Curtain gas MARÍA DEL PILAR CHANTADA VÁZQUEZ 38 In the mass analyzer, ions are separated according to their massto-charge-ratio (m/z) by means of magnetic and/or electric fields. This part is considered the heart of the equipment and it is currently available in a wide range of analyzers, each of them with advantages and disadvantages according to the required results. Among them, the use of the cuadrupole, the ion trap and the time-of-flight (TOF) must be highlighted [122]. - CUADRUPOLE. This type of analyzers contains four equidistant metallic cylinders which are applied a direct current (DC) potential, and another of radio frequency (RF). When applying variable RF voltage, ions of a determined m/z ratio go through the cylinders, keeping the trajectory, while others are diverted. In this way, ions of different m/z ratio can be sent to the detector successively, whereas the rest are discarded (Figure 11). This type of analyzers is the most widely used in tandem mass spectrometry. Among its advantages, it has to be emphasized the fact that they are extremely fast, the narrower the scanning range, the higher the speed [123, 124]. Figure 11. Scheme of a triple quadrupole mass analyzer. - IONS TRAP. It is an analyzer able to isolate ions within a circular electrode, which has two pierced hemispherical electrodes on top and below. The ions go in and out through these pierced electrodes. Thus, the ions of a certain m/z ratio are trapped according to the electrode voltage setting. After changing this setting, ions go towards the detector N2 Firts selection Collision Second selection DETECTION Q1 Q2 Q3 I. Introduction 39 (Figure 12). The analyzers may be one of these two types: Orbitrap, if the applied field is electric, or ion cyclone resonance using Fourier (FT-IRC), if the field is magnetic [124]. Figure 12. Scheme of an ion trap mass analyzer. - TOF. It is one of the simplest and most widely used analyzers in laboratories. Ions are accelerated with a determined voltage towards the flight tube. 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Gross: Mass spectrometry: a textbook. Analytical and Bioanalytical Chemistry (2018), 410(8), 20512052. [123] Henchman, M., & Steel, C. Understanding the quadrupole mass filter through computer simulation. Journal of Chemical Education (1998), 75(8), 1049. II. Objectives 117 Objectives Current limitations in early diagnosis of the Alzheimer's disease (AD) have led to an increasing research on discovering sensitive and specific biomarkers. In addition, the potential applicability of biomarkers should be also used for diagnosing AD at different stages as well as people suffering mild cognitive impairment (MCI). Therefore, highly sensitive and robust metallomic and proteomic techniques have been developed/applied for charactering biochemical disturbances associated to the evolution of AD in the current research. Recent data have confirmed the influence of essential metals (Fe, Cu, Zn, etc.) in neurodegeneration processes, and levels of these metals in blood serum and cerebrospinal fluid have been found to be altered in AD patients. The main objective of this Doctoral Thesis has been the discovery of serum biomarkers (metals, minor proteins and metalloproteins) for allowing an accurate discrimination among healthy people (controls), MCI patients and AD patients. The specific objectives of the Doctoral Thesis are therefore as follows: 1. Study of trace metals as potential biomarkers in serum microsamples. 1.1 Development and optimization of a method based on serum dried spots (20 µL of serum sample dried on paper) and further laser ablation (LA) and inductively couple plasma – mass spectrometry (ICPMS) as determination technique (Chapter 1 Experimental Part). 1.2 Development of discrete sampling based-flow injection procedures that allow low sample consumption by exploring the MARÍA DEL PILAR CHANTADA VÁZQUEZ 118 possibilities of new and advanced sample introduction systems such as SeaFast for ICP-MS measurement (Chapter 2 Experimental Part). 2. Proteomic study for the identification of minor serum proteins (qualitatively and quantitatively) altered in healthy people and MIC and AD patients. 2.1 Developments of sample pretreatments for major/minor proteins depletion before applying proteomic platforms. 2.2 Development and application of two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) methods for minor proteins separation before identification by mass spectrometry (MS) (Chapter 3 Experimental Part). 2.3 Development and application of advanced Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH) methods for minor proteins determination/identification by MS techniques (Chapter 4 Experimental Part). 3. Study of the levels of trace metals associated with proteins in serum samples. 3.1. Development of LA-ICP-MS methods for assessing trace metals in selected proteins spots after 2D-PAGE (Chapter 5 Experimental Part). 3.2 Study of the possibilities of the levels of metals in metal-protein complexes as potential biomarkers of AD diagnosis (Chapter 5 Experimental Part). III. RESULTS AND DISCUSSION CHAPTER 1 DEVELOPMENT OF DRIED SERUM SPOT SAMPLING TECHNIQUES FOR THE ASSESSMENT OF TRACE ELEMENTS IN SERUM SAMPLES BY LA-ICP-MS MARÍA PILAR CHANTADA-VÁZQUEZ, JORGE MOREDA–PIÑEIRO, ALICIA CANTARERO–ROLDÁN, PILAR BERMEJO-BARRERA AND ANTONIO MOREDA-PIÑEIRO TALANTA (2018), 186: 169-175 DOI: 10.1016/J.TALANTA.2018.04.049 https://doi.org/10.1016/j.talanta.2018.04.049 CHAPTER 2 DISCRETE SAMPLING BASED-FLOW INJECTION AS AN INTRODUCTION SYSTEM IN ICP-MS FOR THE DIRECT ANALYSIS OF LOW VOLUME HUMAN SERUM SAMPLES MARÍA PILAR CHANTADA-VÁZQUEZ, PALOMA HERBELLO-HERMELO, PILAR BERMEJO-BARRERA, ANTONIO MOREDA-PIÑEIRO TALANTA (2019), 199: 220-227 DOI: 10.1016/J.TALANTA.2019.02.050 https://doi.org/10.1016/j.talanta.2019.02.050 CHAPTER 3 SERUM PROTEIN-BASED BIOMARKERS IN MILD COGNITIVE IMPAIRMENT AND ALZHEIMER´S DISEASE MARÍA PILAR CHANTADA-VÁZQUEZ, MARÍA GARCÍA-VENCE, SUSANA B. BRAVO, PILAR BERMEJO-BARRERA AND ANTONIO MOREDA-PIÑEIRO MARÍA DEL PILAR CHANTADA VÁZQUEZ 180 Sypro Ruby (Lonza, Basel, Switzerland) following manufacturer’s instructions. 3.2.4 Image acquisition and software analysis of 2-DE gel The Sypro Ruby stained 2-DE gels were scanned using a Typhoon fluorescence scanner (GE Healthcare). The scanned images were processed using the ProgenesisSameSpots software v.4.5 (Nonlinear Dynamics, Durham, NC, USA). Both manual and automatic alignments were used to align the images. All gels were compared, and the foldchanges (FC) and p-values of all spots were calculated using the SameSpots software with 1-way ANOVA analysis. The differential protein expression was considered significant when the FC was at least 1.8, and the p-value was lower than 0.05. 3.2.5 Tryptic digestion Digestion of the spots from 2-DE was manually performed according to the protocol established by Shevchenko et al. [13], with minor modifications. The spots selected from representative 2-DE gels were excised and washed with a solution containing 50 mM NH4HCO3 and 50% MeOH HPLC grade (Scharlau, Barcelona, Spain). The proteins were reduced with 10 mM DTT in 50mM NH4HCO3 and alkylated with 55mM iodoacetamide in 50mM NH4HCO3. Subsequently, the proteins were rinsed with 50 mM NH4HCO3 in 50% MeOH, dehydrated through the addition of acetonitrile (ACN) (HPLC grade, Scharlau) and dried in a Speed Vac (Thermo Scientific, Waltham, MA, USA). Modified porcine trypsin (Promega, Madison, WI, USA) prepared in 20mM NH4HCO3 (concentration of 20 g/μL) was added to the dried gel slices, followed by incubation at 37°C for 16 h. The peptides were extracted three times by incubation in 40 μL of a solution containing 60% ACN and 0.5% formic acid (HCOOH) for 20 min. The resulting peptide extracts were pooled, concentrated in a Speed Vac and stored at −20°C. 181 3.2.6 Protein identification through MALDI-TOF MALDI-TOF analysis of the peptides digested from spots was performed by mixing equal volumes (0.5 μL) of peptides and matrix solution (3 mg of α-cyano-4-hydroxycinnamic acid (CHCA) dissolved in 1 mL of 50% ACN in 0.1% trifluoroacetic acid (TFA)). The mixture was deposited onto a 384 Opti-TOF MALDI plate (Applied Biosystems, Foster City, CA, USA) using the thin layer method. Mass spectrometric data were obtained in an automated analysis loop using 4800 MALDI-TOF/TOF analyser (Applied Biosystems). MS spectra were acquired in reflector positive-ion mode with a Nd:YAG, 355 nm wavelength laser, averaging 1000 laser shots, and at least three trypsin autolysis peaks were used as internal calibration. All MS/MS spectra were performed by selecting the precursors with a relative resolution of 300 (FWHM) and metastable suppression. Automated analysis of mass data was achieved using the 4000 Series Explorer Software V3.5. MS and MSMS spectra data were combined with the Protein Pilot Explorer Software v4.5 using Mascot software search engine v2.1 (Matrix Science, Boston, MA, USA). Searches were performed against a non-redundant database (release version 2016-05; February, 551193 entries) with 100 ppm precursor tolerance, 0.35Da MS/MS fragment tolerance, and allowing only missed cleavage. All spectra and database results were manually inspected in detail using the previously mentioned software. Protein scores greater than 56 were accepted as significant (p<0.05), considering the identification positive when the protein score (CI%, Confidence Interval) was above 98. In the case of MS/MS spectra, the total ion score CI% was above 95. 3.3 RESULTS Currently, AD diagnosis is based on several valuations, such as medical history, physical examination, neurophychological tests, and brain scans, the latter being the most important diagnosis tool. However, a brain scan is only useful (brain imaging appears abnormal III. Results and discussion. Chapter 3 MARÍA DEL PILAR CHANTADA VÁZQUEZ 182 compared to a typical healthy brain) when AD is in middle or late stages. There is therefore no single test that can diagnose early AD with 100% accuracy [1]. Current techniques predict the disease when it is already advanced. The development of early biomarkers is therefore necessary. Serum is one of the most representative biofluids, and AD could cause characteristic changes in the concentrations of some biomolecules such as serum proteins. Post transcriptional modifications of signaling proteins could generate a detectable disease-specific protein partner [14]. 3.3.1 High abundance protein depletion The presence of high abundant proteins in most biofluids decreases the capacity of the analytical methods by a factor from 5 to 10 to detect low-abundance proteins. High-abundance proteins removal, therefore, allows improvements when assessing low-abundance proteins. Several approaches have been developed to deplete high abundant proteins [9], and affinity chromatography is one of the most appealing strategies. Affinity columns are based on dye ligands or antibodies for albumin removal, and on protein A or G for removing immunoglobulins (IgGs). New multiple affinity chromatographic columns allow several high-abundance proteins (albumin, IgG, IgA, transferrin, haptoglobin, and α1-antitrypsin) depletion at the same time, and have been found to be the most effective protein depletion systems, providing more reproducible results (retention times and peak areas) during LC-MS analysis than previously proposed methods [8]. Serum samples (200 µL) have been diluted five times with buffer A, and high abundant proteins were depleted using a Multiple Affinity Removal System Hu6 chromatographic column following the manufacturer's instructions. Figure 1 shows a chromatogram with two high chromatographic signals corresponding to low abundance proteins (retention times between 2 and 5 min) and high abundance proteins (retention times between 12 and 13.5 min). The presence of low abundance proteins was, however, checked along the whole 183 chromatographic run by dividing the chromatogram into nine fractions at different retention times as shown in Figure 1a (fractions 2 and 4 encompass the two high chromatographic signals related to low abundant and high abundant proteins, respectively). Each fraction was further loaded/run in a 10 % SDS-gel, and results show that low abundant proteins are also present in fraction 3 (retention times between 5 and 12 min). Therefore, the low abundance proteins fraction was fixed between 2 and 12 minutes (fraction 2 and 3 in Figure 1b); whereas, high abundance proteins were fixed between 12 and 13.5 min (fraction 4 in Figure 1b). Figure 1. a) Serum chromatogram. b) SDS-gel of 9 fractions. III. Results and discussion. Chapter 3 MARÍA DEL PILAR CHANTADA VÁZQUEZ 184 3.3.2 2-DE and gel image analysis Proteins (low and high abundance proteins) from seven control (CTR) and seven patient samples (three AD and four MCI patients) were separated according to their isoelectric point and molecular weight by 2DE (17 cm strips pH 4-7, 10 % SDS gel), and images of the gels were compared with the SameSpots software (Figure 2). Most studies regarding low abundant proteins involved in AD compare only control (CTR) and AD patients [1, 12, 15-18]. However, MCI patients have been included in our study, and we have performed CTR/MCI, and CTR/AD (data not given), and MCI/AD comparisons (more novel and reliable results for early AD diagnosis). Gel images from AD and MCI patients were obtained for low abundance and high abundance protein fractions. Results show differences between AD and MCI patients when performing analysis of high and low abundance proteins. Regarding the high abundance proteins fraction, sixteen statistically significant spots (p-value <0.05 and a Fold Change >1.3) have been obtained. Seven spots showed to be increased in AD patients; whereas, nine spots were found to be decreased in AD patients. There were more statistically significant differences in spots of the low abundance proteins fraction. Twentyfour spots showed p-values lower than 0.05 and Fold Change ratios higher than 1.7. Seven of these spots were increased (Figure 3a) and 17 spots were decreased in advanced AD patients (Figure 3b). The value of log normalized volume, expressed as the mean value and standard deviation of the same spot in gels of different patients (also given by the SameSpots software), has further been used to know the extension of the increase/decrease of certain spots. Results after comparing two sets of gels (AD and MCI patients) classify the spots into increased (Figure 4a) or decreased in AD patients (Figure 4b). 185 Figure 2. Typical image of a Sypro stained 2-DE map of serum. a) Image of CTR; b) Image of MCI patient; c) Image of AD patient. 25 37 50 75 100 150 pI4 7 pI4 7 Low Abundant High Abundant a 25 37 50 75 100 150 b 25 37 50 75 100 150 c CTR MCI AD III. Results and discussion. Chapter 3 MARÍA DEL PILAR CHANTADA VÁZQUEZ 186 Figure 3. a) Protein spots increases in AD patients versus MCI patients. b) Protein spots decreased in AD versus MCI patients. Analysis performed with SameSpots software. The spots were considerated significative when P < 0.05 and Fold Change ≥ 1.3. 187 Figure 4. log normalized volume of spots (a) increased and (b) decreased in AD patients. 3.3.3 MALDI-TOF identification of increased and decreased proteins in AD patients Statistically significant low abundant protein spots (gel images analysis) have been cut, digested, and identified by MALDI-TOF. Results (score values, percentage of coverage of sequence, size and theoretical isoelectric point) are shown in Table 1. 0 1 2 3 4 5 6 7 8 Spot 901 (PEDF) Spot 1047 (HP) Spot 1228 (FCN3) Spot 1102 (C9JKR2) Spot 320 (TRFE) Spot 493 (TRFE) Spot 494 (TRFE) LOG NORMALISED VOLUME INCREASED IN AD PATIENTS AD MCI a 0 1 2 3 4 5 6 7 8 LOG NORMALISED VOLUME DECREASED IN AD PATIENTS AD MCI b III. Results and discussion. Chapter 3 Table 1. MALDI-TOF/TOF identification of serum proteins from AD and MCI patients. Protein Spot No. Uniprot ID Score Sequence coverage (%) Theorical MW(Da)/pI Increased low abundant proteins in AD Pigment epithelium-derived factor 901 PEDF 69 16 46283.30/5.97 Haptoglobin 1047 HP 215 51 31387.87/8.48 Ficolin-3 1228 FCN3 132 18 32881.99/6.20 Albumin, isoform CRA_k 1102 C9JKR2 304 37 47256.70/5.97 Serotransferrin 320, 493, 494 TRFE 191 18 77013.63/6.81 Decreased low abundant proteins in AD Ceruloplasmin 191 E9PFZ2 60 5 108751.41/5.49 Complement factor I 1211 G3XAM2 82 19 65016.27/7.72 Keratin, type II cytoskeletal 1 1211, 712 K2C1 75 25 65999.0/8.15 Charged multivesicular body protein 1ª 1211 F5H875 62 38 14304.15/4.69 Serum amyloid P-component 1447, 1452 SAMP 366 30 25371.13/6.10 Kininogen-1 725, 752, 799 KNG1 183 18 71912.15/6.34 Vitronectin 725, 752 VTNC 138 16 54271.17/5.55 DNA (cytosine-5)-methyltransferase 1 725 DNMT1 60 12 183049.81/7.99 Ig alpha-2 chain C region 799, 806 IGHA2 183 15 14743.35/4.93 Ig alpha-1 chain C region 799 IGHA1 182 15 37731.56/5.63 Dynamin-1 799 DYN1 62 17 97408.25/6.73 Haptoglobin 752 H3BS21 68 30 24780.65/6.08 Centrosomal protein C10 or F90 752 CJ090 64 13 73349.21/8.88 CD5 antigen-like 992 CD5L 99 22 38062.94/5.28 Antithrombin-III 806 ANT3 208 36 52568.86/6.32 MARÍA DEL PILAR CHANTADA VÁZQUEZ 188 Table 1. MALDI-TOF/TOF identification of serum proteins from AD and MCI patients (Continued). Protein Spot No. Uniprot ID Score Sequence coverage (%) Theorical MW(Da)/pI Decreased low abundant proteins in AD Alpha-1-antitrypsin 915 A1AT 275 45 46707.02/5.37 Nuclear mitotic apparatus protein 1 915 H0YFY6 60 14 107345.79/9.16 Serum albumin 984 ALBU 174 22 69321.49/5.92 Albumin, isoform CRA-k 984 C9JKR2 117 23 47256.70/5.97 Alpha-2-HS-glycoprotein 1981 FETUA 92 21 39299.71/5.43 Actin, cytoplasmic 1 1013 ACTB 231 32 41709.73/5.29 Microtubule-actin cross-linking factor 1, isoforms 1/2/3/5 221 E9PNZ4 61 10 230947.41/5.20 Intermediate filament family orphan 1 1995 F8W8H2 67 29 29399.77/4.41 Alpha-2-macroglobulin 95 A2MG 63 7 163187.89/6.03 III. Results and discussion. Chapter 3 189 MARÍA DEL PILAR CHANTADA VÁZQUEZ 196 3.4.1 Increased low abundant proteins in AD patients 3.4.1.1 Pigment epithelium-derived factor (PEDF) PEDF is a non-inhibitory member of the serpin class of proteins with various biological functions including anti-angiogenesis, antivasopermeability, anti-tumor, and neurotrophic activities [49]. High PEDF levels have been recently proposed as a CSF biomarker for AD. However, the presence of PEDF in CSF (whether derived from the brain or from the systemic circulation) and the specificity of this finding remains unclear. As PEDF has neuroprotective and anti-inflammatory functions, the increase of PEDF in AD patients may be a response to brain injury [25]. The exact role of PEDF in AD physiopathology remains unknown, but recent work highlights the implication of PEDF in the regulation of proliferation in hippocampal progenitor cells and thus in memory consolidation [2]. PEDF has also been found to be increased in serum from AD patients. Therefore, and as PEDF is also increased in CSF in AD patients [50], PEDF could be a potential biomarker for AD diagnosis. 3.4.1.2 Ficolin-3 (FCN3) FCN3 contributes to the complement independent inflammatory processes of traumatic brain injury. Lower serum FCN3 levels have been demonstrated to be highly associated with unfavorable outcome after ischemic stroke [27]. Therefore, this protein is commonly associated with degenerative diseases such as multiple sclerosis (MScl), and FCN3 has been proposed as a possible MScl biomarker [26]. The presence of high levels of FCN3 in serum from AD patients suggests that increased FCN3 is related to neurodegenerative processes. 3.4.1.3 Serotransferrin (TRFE) TRFE has been reported to be increased in plasma samples from AD patients [33, 49]. These studies have suggested that iron oxidation promotes the accumulation of this protein, which is an iron-transport 197 protein [48]. Our findings also confirm that serum TRFE is increased in AD patients compared to MCI patients (early AD stage). As previous reports have shown that plasma TRFE is increased in AD patients compared to healthy people, further research is needed with healthy (control) / MCI patient comparison. 3.4.1.4 Haptoglobin (HP) Since HP in CSF is a useful marker of AD progress, a reliable analytical method of measuring HP concentrations in AD patients is needed. HP is an acute-phase protein produced by the liver that functions to scavenge cell-free hemoglobin and its by-product. HP exhibits several functional properties, including antioxidant and antiinflammatory activities, and the ability to participate in immune system regulation. Furthermore, serum HP levels have been shown to be increased in humans with sepsis, and the pathogenesis of neurodegenerative disorders such as AD, and it has been reported to involve inflammation and oxidative stress. In addition, significantly higher serum HP levels in the patients with AD and Parkinson’s disease (PD) compared to those observed in control patients have also been reported for serum HP, and no significant differences have been observed between AD and PD groups. Our findings also show that serum HP is increased in AD compared to MCI patients, a fact that is in good agreement with those shown by Song et al. when comparing healthy people and AD patients [16]. 3.4.2 Decreased serum proteins in AD patients There are several different (nature and function) low abundant proteins that have been found to be decreased in AD patients compared to MCI patients. Most of them have been reported as related with AD, but they were described as increased or decreased in many other diseases such as diabetes, eye diseases, and even MScl. Some serum proteins found to be decreased in AD patients compared to MCI patients (Table 1) have been related to neurodegenerative processes when comparing healthy people and AD patients. These proteins are III. Results and discussion. Chapter 3 MARÍA DEL PILAR CHANTADA VÁZQUEZ 198 ceruloplasmin (CP), complement factor 1 (CF1), charged multivesicular body protein 1A (CHMP1A), kininogen-1 (KNG1), DNA (cytosine-5)-methyltransferase 1 (DNMT1), CD5 antigen-like (CD5L), alpha-1-antitrypsin (SERPINA1), antithrombin-3 (SEPINC1), alpha-2-HS-glycoprotein (AHSG), actin cytoplasmatic 1 (ACTB), and alpha-2-macroglobulin (A2M). 3.4.3 FunRich and String analysis Characterization of identified decreased low abundant proteins was performed by FunRich software (functional analysis). In this case, the images of the gels of patients with MCI versus patients with AD were compared. As shown in Figure 5, two groups, one related to brain central nervous systems diseases (Figure 5a), and another to peripheral organ disease (Figure 5b), have been obtained. Proteins involved in memory loss, progressive dementia, and neurological disease have been found as proteins related to the central nervous system. In addition, proteins involved in diabetes, retinal degeneration and achalasia have been found to belong to the peripheral organ disease group (Figure 5b). Finally, there have also been identified proteins related to certain diseases that cause alterations in serum metal levels, such as hemosiderosis, anemia, and aceruloplasminemia. Several studies regarding trace metal levels have pointed out that metal ions play an important role in the promotion of these diseases [51]. Metals such as iron, copper, zinc, manganese and aluminum have therefore been found to be related to this disease [52]. Because metal ions are essential cofactors for many proteins, and can compete with each other for binding to proteins, they help preserve neuronal function. Some heavy metals may worsen the progression of the disease due to their high neurotoxicity and their ability to induce epigenetic changes [53]. 199 Figure 5. Functional analysis using FunRich software. a) Diseases associated with the brain and central nervous system. b) Other diseases. III. Results and discussion. Chapter 3 MARÍA DEL PILAR CHANTADA VÁZQUEZ 200 Identified proteins are related not only to neurodegenerative diseases but also to other diseases that are often associated with diseases such as AD. String software was used to investigate possible interactions between all the proteins identified in order to highlight predominant networks, pathways and connections to pathophysiological processes. Findings show that thirteen proteins are connected in a network related to mediated transport, seven proteins are involved in negative regulation of endopeptidase activity, eight proteins are related to a negative regulation of hydrolase activity, eight proteins are related to a regulation of response to wounding, and seven proteins affect the regulation of the inflammatory response network. From the results shown in Figure 6, three increased proteins (HP, TRFE and FCN3) in patients with AD are related among each other, and are also transport proteins. Figure 5. STRING interaction network analysis. In grey, general interaction network of all proteins identified; in red, protein network related to mediated transport; in blue, protein network related to negative regulation of endopeptidase activity; in green, regulation of response to wounding; in violet, protein network related to regulation of inflammatory response. 201 3.5 CONCLUSIONS CSF has typically been proposed as a potential biofluid for detecting protein-based biomarkers. In addition, most studies follow a strategy based on comparing healthy (control) people and advanced AD patients. However, more available clinical samples such as serum/plasma and oral fluid are preferred for performing proteomic studies. Moreover, a direct comparison between MCI and AD patients is also preferable for early AD diagnosis. Our results confirm some previous findings regarding certain serum proteins such as TRFE, which has been reported to be increased in AD patients compared to healthy people (serum TRFE is also increased when comparing with TRFE values in MCI patients). Some potential biomarkers in CSF such as PEDF have also been found to be increased in serum from AD patients compared to MCI patients. In addition, increased serum HP has also been observed in AD patients compared to MCI patients. High levels of serum FCN3 in AD patients compared to MCI patients suggest this protein could be related to neurodegenerative illnesses because it is also increased in serum from MScl patients. Finally, several serum proteins have been found to be decreased in AD patients compared to MCI patients. These proteins (serum and CSF) have also been found to be decreased in AD patients compared to healthy people; however, they have also been reported to be related to other illnesses. As conclusion, serum PEDF and HP could be potential biomarkers of early AD diagnosis. These two proteins have also been reported at high levels in CSF (PEDF) and serum (HP) in AD patients compared to healthy people. III. Results and discussion. Chapter 3 MARÍA DEL PILAR CHANTADA VÁZQUEZ 202 Acknowledgments The authors wish to thank the Dirección Xeral de I+D – Xunta de Galicia (Project 6RC2014/2016), and the European Regional Development Funds (2007-2013, Infrastructure Program UNST10-1E1195) for financial support. References [1] Yang, M. H., Yang, Y. H., Lu, C. Y., Jong, S. B., Chen, L. J., Lin, Y. F., & Tyan, Y. C. Activity-dependent neuroprotector homeobox protein: A candidate protein identified in serum as diagnostic biomarker for Alzheimer's disease. Journal of Proteomics (2012), 75(12), 36173629. [2] Abraham, J. D., Calvayrac-Pawlowski, S., Cobo, S., Salvetat, N., Vicat, G., Molina, L., & Fareh, J. 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Proteomic profiling from human samples: the body fluid alternative. Toxicology Letters (2001), 120(1-3), 379-384. [12] Kitamura, Y., Usami, R., Ichihara, S., Kida, H., Satoh, M., Tomimoto, H., & Oikawa, S. Plasma protein profiling for potential biomarkers in the early diagnosis of Alzheimer’s disease. Neurological Research (2017), 39(3), 231-238. [13] Shevchenko, A., Tomas, H., Havli, J., Olsen, J. V., & Mann, M. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nature Protocols (2006), 1(6), 2856-2860. [14] Ray, S., Britschgi, M., Herbert, C., Takeda-Uchimura, Y., Boxer, A., Blennow, K., & Kaye, J. A. Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins. Nature Medicine (2007), 13(11), 1359. [15] Crespo, Â. C., Silva, B., Marques, L., Marcelino, E., Maruta, C., Costa, S., & Correia, A. P. Genetic and biochemical markers in patients with Alzheimer's disease support a concerted systemic iron homeostasis dysregulation. Neurobiology of Aging (2014), 35(4), 777-785. III. Results and discussion. Chapter 3 MARÍA DEL PILAR CHANTADA VÁZQUEZ 204 [16] Song, I. U., Kim, Y. D., Chung, S. W., & Cho, H. J. Association between serum haptoglobin and the pathogenesis of Alzheimer's disease. Internal Medicine (2015), 54(5), 453-457. [17] Tannorella, P., Stoccoro, A., Tognoni, G., Petrozzi, L., Salluzzo, M. G., Ragalmuto, A., & Migliore, L. Methylation analysis of multiple genes in blood DNA of Alzheimer’s disease and healthy individuals. Neuroscience Letters (2015), 600, 143-147. [18] Olsson, B., Lautner, R., Andreasson, U., Öhrfelt, A., Portelius, E., Bjerke, M., & Wu, E. CSF and blood biomarkers for the diagnosis of Alzheimer's disease: a systematic review and meta-analysis. The Lancet Neurology (2016), 15(7), 673-684. [19] Bah, A., & Forman-Kay, J. D. Modulation of intrinsically disordered protein function by post-translational modifications. Journal of Biological Chemistry (2016), jbc-R115. [20] Perluigi, M., Barone, E., Di Domenico, F., & Butterfield, D. A. Aberrant protein phosphorylation in Alzheimer disease brain disturbs pro-survival and cell death pathways. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease (2016), 1862(10), 1871-1882. [21] Soares, H. D., Chen, Y., Sabbagh, M., Rohrer, A., Schrijvers, E., & Breteler, M. Identifying early markers of Alzheimer's disease using quantitative multiplex proteomic immunoassay panels. Annals of the New York Academy of Sciences (2009), 1180(1), 56-67. [22] Song, F., Poljak, A., Smythe, G. A., & Sachdev, P. Plasma biomarkers for mild cognitive impairment and Alzheimer's disease. Brain Research Reviews (2009), 61(2), 69-80. [23] Ravetti, M. G., & Moscato, P. Identification of a 5-protein biomarker molecular signature for predicting Alzheimer's disease. PloS One (2008), 3(9), e3111. [24] Hu, Y., Hosseini, A., Kauwe, J. S., Gross, J., Cairns, N. J., Goate, A. M., & Holtzman, D. M. Identification and validation of novel CSF biomarkers for early stages of Alzheimer's disease. PROTEOMICS– Clinical Applications (2007), 1(11), 1373-1384. 205 [25] Lang, V., Zille, M., Infante-Duarte, C., Jarius, S., Jahn, H., Paul, F., & Pina, A. L. Alzheimer's disease: Elevated pigment epitheliumderived factor in the cerebrospinal fluid is mostly of systemic origin. Journal of the Neurological Sciences (2017), 375, 123-128. [26] Amin, B., Maurer, A., Voelter, W., Melms, A., & Kalbacher, H. New poteintial serum biomarkers in multiple sclerosis identified by proteomic strategies. Current Medicinal Chemistry (2014), 21(13), 1544-1556. [27] Pan, J. W., Gao, X. W., Jiang, H., Li, Y. F., Xiao, F., & Zhan, R. Y. Low serum ficolin-3 levels are associated with severity and poor outcome in traumatic brain injury. Journal of Neuroinflammation (2015), 12(1), 226. [28] Cheung, C. L., Cheung, T. T., Lam, K. S., & Cheung, B. M. High ferritin and low transferrin saturation are associated with pre-diabetes among a national representative sample of US adults. Clinical Nutrition (2013), 32(6), 1055-1060. [29] Lehmann, D. J., Schuur, M., Warden, D. R., Hammond, N., Belbin, O., Kölsch, H., & Morris, C. M. Transferrin and HFE genes interact in Alzheimer's disease risk: the Epistasis Project. Neurobiology of Aging (2012), 33(1), 202-e1-202.e13. [30] Loeffler, D. A., Connor, J. R., Juneau, P. L., Snyder, B. S., Kanaley, L., DeMaggio, A. J., & LeWitt, P. A. Transferrin and iron in normal, Alzheimer's disease, and Parkinson's disease brain regions. Journal of Neurochemistry (1995), 65(2), 710-716. [31] Connor, J. R., Menzies, S. L., St. Martin, S. M., & Mufson, E. J. A histochemical study of iron, transferrin, and ferritin in Alzheimer's diseased brains. Journal of Neuroscience Research (1992), 31(1), 7583. [32] Rhodes, S. L., Buchanan, D. D., Ahmed, I., Taylor, K. D., Loriot, M. A., Sinsheimer, J. S., & Ritz, B. Pooled analysis of iron-related genes in Parkinson's disease: association with transferrin. Neurobiology of Disease (2014), 62, 172-178. III. Results and discussion. Chapter 3 MARÍA DEL PILAR CHANTADA VÁZQUEZ 212 Based on proteins’ functionality, the most of these proteins have been found to be related with a transcriptional activity in both AD and MCI. Quantitative analysis showed that 19 differentially proteins were found after the comparison of patients with MCI and AD; whereas, 31 and 42 proteins were found when comparing controls against patients with AD and MCI, respectively. The expression levels of Apoliprotein A-II (Apo A-II) were found to be significantly decreased in serum from AD patients in comparison with MCI. In addition, the expression levels of this protein were found to be significantly increased in serum samples from MCI patients when comparing to controls. These findings means that Apo A-II may play an important role in the progression of AD, and this protein could thus be a susceptibility biomarker for the early diagnosis of this disease. Finally, serotransferrin (TRFE) immunoglobulin kappa constant (IGKC) could be also considered as protein biomarkers at an early stage of AD neurodegeneration. 4.1 INTRODUCTION Alzheimer’s disease (AD) is an age-dependent neurodegenerative disorder that impairs cognitive and memory function progressively and is the most common form of dementia in the elderly [1]. Pathologically, AD is characterized by neuronal loss and the accumulation of neurofibrillary tangles and amyloid plaques [2]. Currently, effective diagnostics are costly and treatments and preventative strategies are lacking, thus making the validation of AD biomarkers imperative [3]. Biomarkers that have been examined can play a critical role in diagnostics and drug development. The research for AD biomarker has taken many directions, includes measuring of proteins in blood, cerebrospinal fluid (CSF), and urine. Studies involving proteomics to discover biomarkers have been developed for decades; however, these attempts have met less success samples which was used to be a clinical biomarker for AD. Validation of a clinical biomarker is a challenge due to there are many critical steps from the biomarker discovery to the 213 biomarker validation, and also due to the complexity of the body fluids, and the low abundance of the potential biomarkers,. Despite of the challenges exist, there are some “-omics” areas leading us into the future study for AD biomarkers, such as peptidomics, modificationspecific proteomics, and metabolomics. An approach based on combining all these “-omics” techniques could offer new insights to discover biomarkers [4]. CSF is a biological fluid which is in direct contact with the extracellular space of the brain. Therefore, biochemical changes in the brain are reflected in the CSF. Since AD pathology is restricted to the brain, CSF has been the focus in research on diagnostic biomarkers for AD [5]. Due to the difficulty of sampling CSF from a patient with AD, recent studies have been focused on samples such as blood, serum or saliva. Since blood is more easily accessible than CSF, finding reliable blood biomarkers for AD is desirable. Due to the blood–brain barrier, the concentration of brain-derived proteins in the blood is lower than in the CSF, which makes this task a challenge [6]. Furthermore, examined AD serum samples have shown increased levels of interleukin-1 beta (IL-1β), interleukin-8 (IL-8), tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), granulocytemacrophage colony-stimulating factor (GMCSF), soluble CD40 ligand (sCD40L), and vascular endothelial growth factor (VEGF) [7,8]. Recently, chemokines have been found to serve novel roles in the pathophysiology of psychiatric disorders [9], and the most important proteins have been identified as CXCL10 (interferon gamma-induced protein 10, IP10) and CX3CL1 (fractalkine) [10]. Other contributing factors to cognitive impairment include metabolic disorders, such as obesity and hyperglycemia, and cellular dysfunction such as oxidative stress [11-13]. Furthermore, AD patients were found to have higher serum levels of adiponectin and insulin, and significantly lower serum levels of leptin compared to ageand gender-matched control subjects [14]. Thus, cytokines/chemokines and other metabolic factors related to both neuro-inflammatory and peripheral inflammatory processes are of increasing interest when searching for AD biomarkers [15]. III. Results and discussion. Chapter 4 MARÍA DEL PILAR CHANTADA VÁZQUEZ 214 The high complexity and large dynamic range of serum proteins can be a problem when using the proteins profile as a biomarker of a disease because the more useful information (differences between healthy and patients) is currently obtained for minor serum proteins. To circumvent these technological limitations, it is describe here a new two-stage strategy for the mass spectrometry (MS) assisted discovery, verification and validation of disease biomarkers [16]. New advances in MS based proteomics that combine developments in instrumentation, sample preparation and computational analysis, could help to fill the gap in the development of non-invasive and easy-to-implement AD biomarkers [17]. Despite of rapid development in MS based proteomics over last 20 years, up to date the US Food and Drug Administration (FDA) approved only around 109 unique protein markers including over 20 protein-based cancer biomarkers. The majority (88 of 109) are measured by immunoaffinity assays, and the remaining targets (21 of 109) by other assays including MS/MS [18]. Paradoxically, only 20% of the currently approved protein assays were introduced since the application of MS/MS to measure proteins, thus on average 1.5 new protein assays were approved by the FDA per year [19]. These figures suggest that the current protein biomarker discovery pipeline is inefficient and suffering from relatively high false-positive rates, which hampers identification and validation of true biomarkers. In the post-genome era, a wide range MS-based proteome analysis method have been developed, which provide valuable insight about proteins and their expression-levels directly from complex tissue and body fluid samples [20, 21]. Exact identification and quantitation of proteins is essential for a better understanding of biological processes in health and disease [22]. Moreover, precise quantification of specific proteins in tissues and body fluids provides valuable insights, and validation of potential biomarkers for diseases stages [23]. 215 The progress on biomarker discovery and validation will be markedly accelerated by the use of more robust and quantitative protein assays, based on targeted reaction monitoring and possibly other emerging techniques [24, 25]. Selected reaction monitoring (SRM), sometimes also referred to as multiple reaction monitoring (MRM), is currently the method of choice for sensitive protein analyses. SRM is suited to quantify from molar to millimolar amounts of a specific protein in a body fluid, [26] or equivalent to measuring in the range of 50 to > 1 million copies [27]. SRM methods rely on using a triple-quadrupole (QQQ) mass spectrometer as a dual mass filter to allow passage and analyses of only predefined targeted proteotypic peptides, by specifically selecting precursor ions in Q1 and their specific fragment ions in Q3 as predefined mass to charge (m/z) values [28]. The signal intensities of SRM transitions (precursor/fragment ion pairs) of the unique peptide can be monitored over time, and they are efficient as surrogate measures of a specific proteins quantity. Due to its high sensitivity, reproducibility and broad dynamic range, SRM has become a powerful tool used in absolute and relative quantification in several biological samples, especially in the area of biomarkers research. Depending on sample type and instrument methods, only ten to fifty proteins can be quantified within each analytical run of a complex sample. More recently, with the introduction of ultra-fast scanning high-resolution Q-TOF instruments, the Sequential Window Acquisition of all Theoretical spectra (SWATH) has been introduced as a novel SRM-like analysis method but based on a Data Independent Acquisition (DIA) strategy [24]. SWATH-MS presents a new and faster alternative to SRM-MS (Figure 1), and like SRM-MS, SWATH-MS operates by collecting time-resolved data from peptides and their fragments, an allows identification and quantification of specific proteins in complex tissue sample. SWATH-MS can theoretically collect all MS/MS fragment ion spectra for all precursor ions in a complex sample by using stepped m/z III. Results and discussion. Chapter 4 MARÍA DEL PILAR CHANTADA VÁZQUEZ 216 windows, which can be used for biomarker discovery with parallel and consistent detection of 30,000-40,000 peptides from 4000-5000 targeted proteins in large sets of samples [24, 29]. Figure 1. Mass spectrometry acquisition methods. Both SRM and SWATH methods for quantification of human proteins have been developed at an amazing pace over the past decade, and today we have open access to accurate and validated MS methods for every known human protein [30, 31]. But, such MS-based methods are speciesand tissue-specific because parameters as linearity and limit of quantification for a certain protein and/or peptide, depends on the complexity of the biological sample. The development of MS-based protein quantification methods is a challenge because there are not many applications for specific tissue/targets [32]. The objective of the present study is the identification of novel potential serum biomarkers of AD using a combination of nextgeneration proteomics and machine-learning algorithms for feature selection. Shotgun SRM/MRM Swath MS Ion Source m/z MS1 MS2 CID Intensity Intensity Time Scan m/z Time Intensity 0.7 Da filtering 25 Da filtering 0.7 Da filtering 217 4.2 MATERIALS AND METHODS 4.2.1 Clinical samples Serum samples from healthy volunteer adults and patients (Table 1) were supplied by the Servicio de Neurología at the University Clinical Hospital of Santiago de Compostela (Santiago de Compostela, Spain). The developed research has been ascribed to the approved and in force expert opinion from the Comité de ética de la investigación con medicamentos – CEIm-G (Ethics Committee for the Research with medicines) of Galicia (Registration Code: CEIm-G 2018/575). Table 1. Characteristics of healthy subjects and patients with MCI and AD. Demographic variables Control MCI AD Number of subjects (M/F) 8 (2/6) 7 (4/3) 15 (8/7) Age (years) 64.3 ± 8.3 75.4 ± 7.2 79.5 ± 10.2 Total cholesterol (mg/dl) 235 ± 32 184 ± 20 198 ± 42 Triglycerides (mg/dl) 137 ± 73 99 ± 35 96 ± 48 Data are represented as mean ± SD. M/F = male/female. N/A = not applicable. FAST = Functional Assessment Staging. MMSE = Mini Mental State Examination. Venous blood samples (2.0 mL) samples were collected in Vacutainer blood collection tubes with silicone-coated cores (BD Diagnostics, Franklin Lakes, NJ, USA) by a standard venipuncture method. The collected serum samples were stored at room temperature to allow for blood clotting and were centrifuged (1800 g, 4 °C, 10 min). Serum samples were then immediately frozen at –80 °C. 4.2.2 Depletion of multiple high abundant proteins Serum aliquots were filtered with Miller-GP® Filter Unit (Millipore) with a size of 0.22 μm. Each aliquot of human serum (30 μL) was depleted with dithiothreitol (DTT) according to the protocol described by Warder el al. [33, 34]. Fresh DTT 500 mM (3.3 μL) was mixed with 30 μL of human serum and vortex briefly. Samples were III. Results and discussion. Chapter 4 MARÍA DEL PILAR CHANTADA VÁZQUEZ 218 then incubated until a viscous white precipitate persisted (60 min), followed by centrifugation at 14000 rpm for 20 min. Supernatants were transferred to a clean tube and total dry (30 ºC, 45 min). 4.2.3 One dimensional SDS-PAGE (1-DE) Samples were reconstituted in 24 μL of ultrapure water and mixed with 4 μL of SDS-PAGE loading buffer (10% w/v SDS, Tris-Base 40 mM, pH 6.8, 50% v/v glycerol, 0.1% v/v bromophenol blue, 10% v/v β-mercaptoethanol). Then, all samples were denatured by heating at 100 °C for 5 min and loaded into a 10% acrylamide/ bis-acrylamide, stacking gel / 12.5% acrylamide/bis-acrylamide running gel, of 1 mm thickness, and separated at 80 V (constant voltage) and the run was stopped as soon as the front had penetrated 3 mm into the resolving gel. The gels were stained with Coomassie Blue for 2 hours at room temperature under agitation and distained with methanol / acetic acid (45% / 7.5%) for 12 hours, also under continuous agitation. Gels were then washed with ultrapure water and scanned. 4.2.4 Tryptic digestion Protein bands were excised and washed with a solution containing 50 mM NH4HCO3 and 50 % MeOH HPLC grade (Scharlau, Barcelona, Spain). The proteins were reduced with 10 mM DTT in 50 mM NH4HCO3 and alkylated with 55 mM iodoacetamide in aqueous 50 mM NH4HCO3. Subsequently, the proteins were rinsed with 50 mM NH4HCO3 in 50 % MeOH, dehydrated by adding acetonitrile (ACN) (HPLC grade, Scharlau), and dried in a Speed Vac. Modified porcine trypsin (Promega, Madison, WI, USA) prepared in 20 mM NH4HCO3 (concentration of 20 g/μL) was added to the dried gel slices, followed by incubation at 37°C for 16 h. The peptides were extracted three times by incubation in 40 μL of a solution containing 60% ACN and 0.5% formic acid (HCOOH) for 20 min. The resulting peptide extracts were pooled, concentrated in a Speed Vac and stored at −20°C. 219 4.2.5 Protein identification by mass spectrometry (LC-MS/MS) and data analysis Digested peptides of each sample were separated using Reverse Phase Chromatography. Gradient was developed using a micro liquid chromatography system (Eksigent Technologies nanoLC 400, Sciex) coupled to high speed Triple TOF 6600 mass spectrometer (Sciex) with a micro flow source. The analytical column used was a silica-based reversed phase column Chrom XP C18 150 × 0.30 mm, 3 mm particle size and 120 Å pore size (Eksigent, Sciex). The trap column was a YMC-TRIART C18 (YMC Technologies, Teknokroma) with a 3mm particle size and 120 Å pore size, switched on-line with the analytical column. The loading pump delivered a solution of 0.1% formic acid in water at 10 μL/min. The micro-pump provided a flowrate of 5 μL/min and was operated under gradient elution conditions, using 0.1% formic acid in water as mobile phase A, and 0.1% formic acid in acetonitrile as mobile phase B. Peptides were separated using a 25 min gradient ranging from 2% to 90% mobile phase B (mobile phase A: 2% acetonitrile, 0.1% formic acid; mobile phase B: 100% acetonitrile, 0.1% formic acid). Injection volume was 4 μL. Data acquisition was performed in a TripleTOF 6600 System (Sciex, Foster City, CA) using a Data dependent workflow. Source and interface conditions were as follows: ion spray voltage floating (ISVF) 5500 V, curtain gas (CUR) 25, collision energy (CE) 10 and ion source gas 1 (GS1) 25. Instrument was operated with Analyst TF 1.7.1 software (Sciex, USA). Switching criteria was set to ions greater than mass to charge ratio (m/z) 350 and smaller than m/z 1400 with charge state of 2–5, mass tolerance 250 ppm and an abundance threshold of more than 200 counts (cps). Former target ions were excluded for 15 s. Instrument was automatically calibrated every 4 h using as external calibrant tryptic peptides from PepcalMix (Sciex). III. Results and discussion. Chapter 4 MARÍA DEL PILAR CHANTADA VÁZQUEZ 220 4.2.6 Protein quantification by SWATH (Sequential Window Acquisition of all Theoretical Mass Spectra) 4.2.6.1 Creation of the spectral library In order to prepare MS/MS spectral libraries, the peptide solutions were analyzed by a shotgun data-dependent acquisition (DDA) approach by micro-LC-MS/MS. Pooled vials of samples from each group (control, MCI and AD) were prepared using equal mixtures of the original samples to get a good representation of the peptides and proteins present in all samples. A volume of 4 μL (4mg) of each pool was separated into a micro-LC system Ekspert nLC425 (Eksigen, Dublin, CA, USA) using a column Chrom XP C18 150 × 0.30 mm, 3 mm particle size and 120 Å pore size (Eksigent, Sciex) at a flow rate of 5µL/min. Water and ACN, both containing 0.1 % formic acid, were used as solvents A and B, respectively. The gradient run consisted of 5% to 95 % B for 30 min, 5 min at 90 % B, and finally 5 min at 5 % B for column equilibration (total run time of 40 min). Eluted peptides were directly injected into a hybrid quadrupole-TOF mass spectrometer Triple TOF 6600 (Sciex, Redwood City, CA, USA) operated with a data-dependent acquisition system in positive ion mode. A Micro source (Sciex), operated at 2600 V voltage, was used for the interface between microLC and MS. The acquisition mode consisted of a 250 ms survey MS scan from 400 to 1250 m/z followed by an MS/MS scan from 100 to 1500 m/z (25 ms acquisition time) of the top 65 precursor ions from the survey scan (total cycle time of 2.8 s). The fragmented precursors were then added to a dynamic exclusion list for 15 s; any singly charged ions were excluded from the MS/MS analysis. The peptide and protein identifications were performed using Protein Pilot software (version 5.0.1, Sciex) over Human specific Uniprot database (iodoacetamide as Cys alkylation). The false discovery rate (FDR) was set to 1 for both peptides and proteins. The MS/MS spectra of the identified peptides were then used to generate the spectral library for SWATH peak extraction using the add-in for PeakView Software (version 2.2, Sciex) MS/MSALL with SWATH 221 Acquisition MicroApp (version 2.0, Sciex). Peptides with a confidence score above 99% (as obtained from Protein Pilot database search) were included in the spectral library). 4.2.6.2 Relative quantification by SWATH acquisition SWATH–MS acquisition was performed on a TripleTOF® 6600 LC-MS/MS system (Sciex). Samples from control, MCI and AD were analysed using a data-independent acquisition (DIA) method (30 total samples). Each sample 4 μL (from a 1 mg/ml solution) was analysed using the LC-MS equipment and LC gradient described above for building the spectral library but using the SWATH-MS acquisition method. The method consisted of repeating a cycle that consisted of the acquisition of 65 TOF MS/MS scans (400 to 1500 m/z, high sensitivity mode, 50 ms acquisition time) of overlapping sequential precursor isolation windows of variable width (1 m/z overlap) covering the 400 to 1250 m/z mass range with a previous TOF MS scan (400 to 1500 m/z, 50 ms acquisition time) for each cycle. Total cycle time was 6.3 s. For each sample set, the width of the 65 variable windows was optimized according to the ion density found in the DDA runs using a SWATH variable window calculator worksheet from Sciex. 4.2.6.3 Data analysis The targeted data extraction of the fragment ion chromatogram traces from the SWATH runs was performed by PeakView (version 2.2) using the SWATH Acquisition MicroApp (version 2.0). This application processed the data using the spectral library created from the shotgun data. Up to ten peptides per protein and seven fragments per peptide were selected, based on signal intensity; any shared and modified peptides were excluded from the processing. Five-minute windows and 30 ppm widths were used to extract the ion chromatograms; SWATH quantitation was attempted for all proteins in the ion library that were identified by ProteinPilot with an FDR below 1%. III. Results and discussion. Chapter 4