Investigation of EBC of seventeen healthy non-smoking donors between 20 and 36 years of age revealed that the major proteins are cell keratins, whose spectrum, however, is polymorphous for different people. Pairs of cytoskeletal keratins 1/10 and 2/9 are invariant for mostly probes. No mutations in the sequences of these proteins in healthy donors have been detected. At the same time, other keratins are substantially different for individual sample.
Apart from keratins, dermcidin (known as a protein antibiotic originating in the sweat glands), prostaglandin H2 D-isomerase (PGDS2), alpha-1-microglobulin/bikunin precursor (AMBP), ubiquitin and cystatin A occurred also frequently ( 30 % of donors). In the same time, some proteins appeared only in single instance. There were immunoglobulin light chain region, human basement membrane heparan sulfate proteoglycan core protein (HSPG2), leukocyte-associated immunoglobulin-like receptor 1 isoform a precursor (LAIR1), lysosomal membrane glycoprotein-2 (LAMP2), cerebroside sulfate activator (CSA), kininogen 1, serum albumin.
We found keratins in most of the samples of patients. These keratins were identified as “normal”, because they were detected in EBC of healthy donors. Additionally, specific peptides of keratins 3, 4, 8 were identified in COPD samples. These keratins were not found in healthy samples; therefore, they were named “abnormal”. It is worth noting that the keratin set identified in samples from patients with acute pneumonia was more varied. Keratins 4/13, 7/19, 8/18 and 15 were also identified in those samples. Peptides of certain other proteins uncharacteristic of healthy EBC samples were discovered in COPD and pneumonia EBC samples: namely, Junction plakoglobin, Desmoplakin, Dermokine, alpha-2-glycoprotein 1, Alpha-1-acid glycoprotein 2, Filaggrin-2, Dynein, Lysozyme, Collagen alpha-1(XVIII), Hornerin.
Результаты анализа белкового состава конденсата выдыхаемого воздуха согласуются с результатами клинического наблюдения пациента, перенесшего трансплантацию легких, и, таким образом, характеризуют его состояние.
Evgeny nikolaev proteomics of body liquids as a source for potential methods for medical diagnostics and mass spectrometry
Proteomics of body liquids as a source for potential methods for medical diagnostics and mass spectrometry Prof. Dr. Evgeny Nikolaev Institute for Energy Problems of Chemical Physics and Institute for biochemical physics Rus. Acad. Sci., Moscow, Russia.
Modern biological mass spectrometers are mainly ESI- TOF Measuring time of ion flights in vacuum MALDI-TOF Orbitraps Measuring frequencies of ion oscillations Ion traps Measuring ion motion stability parameters FT ICR Measuring frequencies of ion oscillations in magnetic field
API Ion source Linear Ion Trap C-Trap Orbitrap differential pumping differential pumping The Thermo Scientific* LTQ Orbitrap XL* hybrid FTMS Alexander Makarov Electrostatic axially harmonic orbital trapping: a high-performance technique of mass analysis. Anal. Chem. 2000; 72: 1156.
The main goal of our research is to connect the level of protein expression with diseases or to find disease biomarkers. Our Project:
Protein enzym Mass analyses fragmentation Isolated peptide Masses of peptide fragments Search in database scoring Protein and DND sequence database Mass analyses High throughput proteome analyses by tandem mass spectrometry methods Bottom-up method
Protein энзим анализ масс 1 Массы фрагментов пептидов Поиск в базе T ор- down method - direct mass spectrometry of proteins and peptides Ion transportation Mass analyses Masses of peptide fragments Search in database scoring Protein and DND sequence database fragmentation
KETAAAKFERQYL K ETAAAKFERQYL KE TAAAKFERQYL KET AAAKFERQYL KETA AAKFERQYL KETAA AKFERQYL KETAAA KFERQYL KETAAAK FERQYL KETAAAKF ERQYL KETAAAKFE RQYL KETAAAKFER QYL KETAAAKFERQ YL KETAAAKFERQY L Sequencing by MS/MS For unambiguous sequencing all peptide bonds should be broken
… -CHR – C(O) – NH – CHR’-… Polypeptide backbone fragmentation b y c z a x Collisionally Activated Dissociation (CAD) Electron Capture Dissociation (ECD) 1960s, 1990s 1998 Electron Detachment Dissociation (EDD) Electron Transfer Dissociation (ETD) 2004 2004 Infrared Multiphoton Dissociation (IRMPD) 1960s, 1995 157 nm UV Photodissociation Metastable-atom Induced Dissociation (MAID) 2004 2005
ECD spectrum of 11+ ions from bovine ubiquitin
Problem of methods based on MS/MS identification <ul><li>Sensitivity lost – informative are only MS/MS spectra, whose intensity is at least ~ 10 -fold lower than intensity of MS spectra </li></ul><ul><li>There is no possibility to detect all peptides in one run </li></ul><ul><li>Extra time for fragment spectra measurements causes longer chromatography time (application of UPLC is questionable for some types of MS instruments) </li></ul>
The other possibility in proteomics – usage of high mass measurement accuracy mass spectrometry
Ion cyclotron resonance mass spectrometer can measure masses with sub ppm accuracy Linear ion trap IR laser Electron gun Magnet
Other mass spectrometers with high accuracy of mass measurements are available now Orbitraps Q-TOFs …… . Mass accuracy 1-2 ppm ( intern. calib .), 5 ppm ( extern . calib. ) Resolution 2 0 000 -60 000 FWHM Rate of mass spectra measurements >20 Hz BRUKER micrOTOF-QII
At accuracy level of 1 ppm elementary composition of peptide with mass up to 600 Da and amino acid composition of peptide with mass up to 5 00 Da could be determined almost unambiguously It is not enough for peptide identification!
. If we are using liquid chromatography (LC) or Capillary electrophoreses (CE) we have another tag - LC retention time or CE retention time Accurate mass tag together with retention time Can identify peptide practically unambiguously! Accurate mass tag retention time Dick Smith group (PNNL)
Thus, there is a possibility in bottom-up approach to proteomics is to create using MS/MS a database for accurate mass tags and retention times as a reference base for fast quantitative measurements of proteins and peptides concentration in a sample
VGLQR YVQLR SLR Validated accurate mass tag ( SLTLGIEPVSPTSLR ) ... T GLYCESQTPR SLTLGIEPVSPTSLR VGLQRYVQLRSLR ... … T GLYCESQTPR SLTLGIEPVSPTSLR trypsinolyses Fragment (463-477) from Vasorin identification validation Vasorin (Homo Sapiens protein) 450 500 550 600 650 m/z 522.5 525.0 m/z LC- FTICR Accurate measured mass: 1568.8768 Putative mass tag from Homo Sapiens : SLTLGIEPVSPTSLR Calculated mass (1568.8773) And measured retention time 200 600 1,000 1,400 1,800 m/z y9 y8 b10 y7 b9 b8 y6 y12 y10 y11 b12 b6 y5 b7 b11 y13 b14 b13 y4 LC-MS/MS (e.g. with ion trap)
FT ICR I.Boldin, E.Nikolaev ASMS May 2010 Dynamicaly harmonized FT ICR cell Pressure limited (practically unlimited mass resolution)
Reserpine, Resolving Power 22,000,000 without apodization, 180 s transient
BSA, 0.3mg/ml, 100scans accumulated, accumulation time in collision cell 50ms (7 Tesla) M Hn+
22s R = 1.3*10 6 BSA (65 kD) high resolution mode on 7 Tesla magnet R = 0.9*10 6
Nb 3 Sn Coils NbTi Coils 21 Tesla FT-ICR Magnet Field Center to Flange 600 - 1100 mm 110 mm Bore Current Leads, Cryocooler, and Quench relief for Zero-Loss 2.2 ° K Cryostat D. Markiewicz, NHMFL T. Painter, NHMFL J. Miller, NHMFL Y. S. Choi, KBSI Slide from Alan Marshall
FT MS ESI Q-TOF ESI TOF Lab Lab Clinic Accurate mass tag retention time approach
The most attractive is human plasma, which contains practically all proteins (around 20000 non modified forms) Human Proteome Detection and Quantitation Project:hPDQ N. Leigh Anderson, Norman G. Anderson, Terry W. Pearson, Christoph H.Borchers, Amanda G. Paulovich, Scott D. Patterson, Michael Gillette, Ruedi Aebersold and Steven A. Carr Mol Cell Proteomics Jan.2009
Proteins in blood N. Leigh Anderson‡ and Norman G. Andersn Protein concentrations are different by 1 1 orders of magnitude!!! There is no method to solve this analytical problem !
The main task is searching for protein biomarker of early stages of diseases
Alzheimer’s disease is a progressive brain disorder of elderly people that gradually destroys a person’s memory and ability to learn, reason, make judgments, communicate and carry out daily activities. Alois Alzheimer (1864-1915) 1906 - 2006 Alzheimer disease
tangles Plaques A β – Amyloid A 1-42, Beta-amyloid peptide The main component of Alzheimer’s plaques (1984) Sequenced in 1987 1 DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA 42 Anomalous accumulation of Beta-amyloid in the form of polymeric aggregates (plaques, tangles) causes Alzheimer
<ul><li>Why normal protein (monomeric Beta-amyloid) aggregates to form pathogenic plaques? </li></ul><ul><li>What molecular event is triggering this process? </li></ul><ul><li>Which part of the molecule is subjected to changing? </li></ul><ul><li>How to detect these changes? </li></ul>Questions to answer
Pro 19 Substitution by proline Abolishes fibril formation Met 35 (O) Oxidation may be Important for toxicity and/or oligomerization Asp 7 Isomerized by 75% In plaques Essential residues for self-association Primary structure elements controlling A β oligomerization
The goal is to develop mass spectrometric methodology to distinguish peptides containing different isomeric forms of individual amino acids and to apply this methodology to fragments of Alzheimer disease Beta-amyloid
f ECD of 1-16 А β z10 z10 z9 z9 Z10 -57 (C α -C β bond destruction) c9 c9 y9 C A β 1-16 (isoAsp 7 ) A β 1-16 (Asp 7 ) Distinguishing aspartate/iso-aspartate in A β – Amyloid by ECD
z10-57 z10 c10 z9 c9 c8 ECD mass spectra of six Аβ1-16(α- Asp ) and Аβ1-16(β- Asp ) peptide mixtures with different relative concentrations
Our recent results on detection of A β 1-16, extracted from human blood
Prototype of molecular diagnostics method <ul><li>Extraction of A β fraction (~ 500 ng) from 5 ml of human plasma </li></ul><ul><li>Preparation and extraction of A β 1-16 probe from A β pool </li></ul><ul><li>MS analysis of isoD7- to D7- A β 1-16 ratio in the probe </li></ul><ul><li>The next goal is to see this peptide in urine! </li></ul>
Body liquids available noninvasively Exhaled breath condensate Urine Saliva Tear Sweat
Noninvasive diagnostic of human breath system by mass spectrometry monitoring of exhaled breath condensate
Breath condenser ECoScreen Jaeger from VIASYSHealthcare (Germany)
KERATINS in individual EBC of young healthy nonsmoking donors
Other proteins in individual EBC of young healthy nonsmoking donors
Proteins overexpressed in samples of patients with COPD (Chronic obstructive pulmonary disease) and pneumonia COPD ( n = 17) pneumonia ( n = 13) <ul><li>Proteins of cytosceleton </li></ul><ul><li>Keratins 3, 4, 8 Keratins 4/13, 7/19, 8/18 and 15 </li></ul><ul><li>Junction plakoglobin Hornerin </li></ul><ul><li>Desmoplakin </li></ul>2. Proteases inhibitors Cystatin А Cystatin А Kininogen - 1 Kininogen -1 Cystatins В, М Alpha -1- antitrypsin 3. Other Osteopontin Cytoplasmic actin
Monitoring of exhaled protein composition after human lung transplantation Before surgery ( artificial lung ventilation ) 1 st month after surgery 15 months after surgery Pure protein spectrum because of disturbance of breath Dermcidin , Keratin 9 , Lysozyme , Ubiquitin Allograft adoptation and medical treatment Annexin 1 , Proteinases inhibitor, Bleomicine - hydrolase, keratin 8 Damaged epithelium removal Desmosomal proteins ( desmoglein , desmoplakin ) Epithelium healing Hornerin , filaggrin “ Normal” proteins Dermcidin , “normal” keratins , Cystatin A , Ubiquitin
Analyses of urine proteom Sick Healthy Urine is available in large quantities – ideal analyte for noninvasive diagnostic . Possibility of biomarker discovery is attracting big attention . 1500 proteins (from Mann’s group Adachi et al. Genome Biology 2006, V7, 9, R80) ; 2,362 proteins (Kentsis , A. et al. Proteomics Clinical Applications 2009, 3, (9), 1052-1061).
<ul><li>Three fractions of voided urine is under investigation </li></ul><ul><li>Proteome (masses of 3-60 kDa) </li></ul><ul><li>Peptidome (masses of 0.8-17 kDa) </li></ul><ul><li>Exosoms (50-90 nm vesicles secreted by a wide </li></ul><ul><li>range of cell types) </li></ul>
Before use some proteins as biomarker we need to know its temporal variability and polymorphism (how different is its concentration in body liquids of different individuals) To clarify this we need to investigate proteomes of hundreds of healthy individuals
Two kinds of sample donors People “from street” (blood donation center) and people in “special conditions”.
For “people from street” Decision to include a person to the study group Current control for urogenital and other pathology including kidney pathology, prostatitis, arterial hypertension, diabetes Analysis of archival information from medical records General blood analysis Examination of internist Blood pressure measurement Control for treatment with diuretics and excessive consumption of fluids
For “healthy people data base” subset we need urine samples from persons under well controlled diet and having healthy lifestyle? In this case we can test urine temporal variability and polymorphism
Those are people p articipating In long term isolation experiments in the frame of space research programs. April- July 2009. March 2010 + 500 days. (The Institute for medical & biological problems RAS)
What is in the DB ( Structured Query Language database) <ul><li>Run, in which this peptide was identified </li></ul><ul><li>Peptide sequence </li></ul><ul><li>What protein does this sequence belong to </li></ul><ul><li>Mascot score </li></ul><ul><li>Modifications </li></ul><ul><li>Measured mass </li></ul><ul><li>Theoretical mass </li></ul><ul><li>Measured charge </li></ul><ul><li>RT, when the peptide began to elute from the column </li></ul><ul><li>RT, when the peptide finished elution </li></ul>
Our statistics of the collected AMT tags in the long term isolation experiment 447 LC-MS (liquid chromatography coupled with mass spectrometry) runs totally: among them 25 samples from each of 6 volunteers have been collected during105 days of isolation experiment. The number of peptides in the database 3468 The number of urine proteins in the database 1055 443 core proteins (all patients have them in their urine)
Current statistics of urinary proteome database for ordinary healthy people Smokers (41 sample) and non-smokers(46 samples) Peptides Proteins Total 2758 840
Current statistics of urinary proteome database 233 LC-MS (liquid chromatography coupled with mass spectrometry) runs totally: 102 with samples from smokers, 131 with samples from non-smokers. Using all peptides Peptides Proteins Non-smokers 2527 762 Smokers 1893 627 Total 2758 840
Influence of life stile on urine proteome Smokers vs. non-smokers urine proteome
40% 35% Using all peptides Peptides Proteins Non-smokers 2527 762 Smokers 1893 627 Total 2758 840 Peptides Proteins 78 549 213 231 1662 865
20% 21% Using all peptides Peptides Proteins Odd 2232 445 Even 2306 467 Non-smokers 2535 506 Peptides Proteins 61 406 49 303 2003 229
A List of Candidate Cancer Biomarkers for Targeted Proteomics Malu Polanski and N. Leigh Anderson Biomark Insights. 2006; 1: 1–48. The Plasma Proteome Institute list of 1261 proteins believed to be differentially expressed in human cancer As an initial approach, we have selected a subset of the candidates based on a set of criteria including number of total citations, number of recent citations, proportion of recent citations, known plasma concentration (implying existence of an assay) and clinical use in any context. This subset of 260 candidates 88 are detected in urine (Mann’s database) 75 (our database)
Molecular & Cellular Proteomics 9:2424–2437, 2010. Prof. Harald Mischak Mosaiques Diagnostics GmbH, Mellendorfer Strasse 7–9, 30625 Hannover, Germany.
ROC curves for classification of patient cohorts with “CKD pattern.” ROC analysis for CKD diagnosis of the training set and the test set after unblinding is shown. 85.5% sensitivity and 100% specificity Peptide (800 to 17,000 Da) patterns distinguishing patients with CKD from HC 230 patients 379 healthy
Samples from 3,600 individuals analyzed by capillary electrophoresis coupled to MS. All processed data were deposited in an Structured Query Language (SQL) database. This database currently contains 5,010 relevant unique urinary peptides that serve as a pool of potential classifiers for diagnosis and monitoring of various diseases.
HPLC-MS run duration is about 1.5-2 hours UPLC-MS duration is about 10-15 minutes We need faster technology!!
<ul><li>Efficient ion accumulation prior to IMS </li></ul><ul><li>High mass accuracy, high dynamic range data acquisition system </li></ul><ul><li>High IMS-TOF </li></ul><ul><li>sensitivity due to ion trapping and multiplexing </li></ul>ION MOBILITY SEPARATIONS IN HIGH THROUGHPUT ROTEOMICS: A NOVEL APROACH TO PROTEIN DETECTION AND IDENTIFICATION PERIMENTAL PLATFORM Mikhail Belov Biological Sciences Division Pacific Northwest National Laboratory
Mobility Drift time Thermal diffusion-limited maximum resolution Temporal spread ION MOBILITY SPECTROMETRY (IMS) T k density N Ze K b av _ 2 16 3 K E L t drift 2 ln 16 T k LEZe R b d
ADDITIONAL ANALYTICAL PEAK CAPACITY DUE TO IMS Only 3 features discerned without drift time dimension ( * )
<ul><li>Conclusions </li></ul><ul><li>Accurate mass tag/retention time databases for human urine proteome and peptidome were created </li></ul><ul><li>Subset of the database contains data from healthy people leaving in long term isolation conditions under the same diet </li></ul><ul><li>The new approach is developed for rapid analyses of urine proteome using AMT/RT database </li></ul><ul><li>Significant difference in protein and peptide content of urine of people with kidney diseases has been found inside some groups of proteins and peptides responsible for particular pathways </li></ul>