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Application of omics technologies in toxicology: P roteomics and Metabolomics. Most Commonly Used Proteomics Techniques: Antibody arrays Protein activity arrays 2-D gels “Shotgun” proteomics ICAT technology SELDI.
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Application of omics technologies in toxicology:Proteomics and Metabolomics
Most Commonly Used Proteomics Techniques: • Antibody arrays • Protein activity arrays • 2-D gels • “Shotgun” proteomics • ICAT technology • SELDI 100% protein sequence coverage: a modern form of surrealism in proteomics. Meyer et al Amino Acids. 2010 Jul 13.
Antibody Arrays • Screening protein-protein interactions • Studying protein posttranslational modifications • Examining protein expression patterns
Antibody Arrays The layout design of the BD Clontech™ Ab Microarray 380. The BD Clontech™ Ab Microarray 380 (#K1847-1) contains 378 monoclonal antibodies arrayed in a 32 x 24 grid. Each antibody is printed in duplicate. Dark gray dots at the corners represent Cy3/Cy5-labeled bovine serum albumin (BSA) spots, which serve as orientation markers. The open circles correspond to unlabeled BSA spots, which serve as negative controls. For complete descriptions of the proteins profiled by the Ab Microarray 380, visit bdbiosciences.com
Limitations, Challenges and Bottlenecks • Protein production: • ►cell-based expression systems for recombinant proteins • ► purification from natural sources • ► production in vitro by cell-free translation systems • ► synthetic methods for peptides • Immobilization surfaces and array formats: • ► Common physical supports include glass slides, silicon, microwells, nitrocellulose or PVDF membranes, microbeads • Protein immobilization should be: • ► reproducible • ► applicable to proteins of different properties (size, charge, …) • ► amenable to high throughput and automation, and compatible with retention of fully functional protein activity • ► such that maintains correct protein orientation • Array fabrication: • ► robotic contact printing • ► ink-jetting • ► piezoelectric spotting • ► photolithography
Protein Activity Arrays Panomics® Transcription Factor Arrays: A set of biotin-labeled DNA binding oligonucleotides (TranSignal™ probe mix) is preincubated with any nuclear extract of interest to allow the formation of protein/DNA (or TF/DNA) complexes; The protein/DNA complexes are separated from the free probes; The probes in the complexes are then extracted and hybridized to the TranSignal™ Array. Signals can be detected using either x-ray film or chemi-luminescent imaging. All reagents for HRP-based chemiluminescent detection are included. Source: Panomics, Inc.
Protein Activity Arrays Protein Array Gel Shift Assay Source: Panomics, Inc.
2D Gel Electrophoresis + Mass Spectrometry Meyer et al Amino Acids. 2010 Jul 13.
2D Gel Electrophoresis Protein Resolution Bandara & Kennedy (2002)
2D Gel Electrophoresis Image Analysis Courtesy of Decodon Courtesy of Alphainnotech
Acquiring a Mass Spectrum Ionization Mass Sorting (filtering) Detection Ion Source Ion Detector Mass Analyzer • Form ions • (charged molecules) Sort Ions by Mass (m/z) • Detect ions 100 75 Inlet • Solid • Liquid • Vapor 50 25 0 1330 1340 1350 Mass Spectrum
+ + + + + + Ion Sources make ions from sample molecules Electrospray ionization: Partialvacuum Sample Inlet Nozzle (Lower Voltage) Pressure = 1 atmInner tube diam. = 100 um MH+ N2 + + + + + + + + + + + + MH2+ + + + + + + + + + Sample in solution + + + + + + + + + + + + + + N2 gas + + + + + MH3+ High voltage applied to metal sheath (~4 kV) Charged droplets All compounds must be ionized, but ionization efficiency is variable with different compounds
2D Gel ElectrophoresisMass Spectrometry Source: UNC Proteomics Core Facility
Image courtesy of University of Arizona Proteomics Core SEQUEST is a program that uses raw peptide MS/MS data (off TSQ-7000 or LCQ) to identify unknown proteins. It works by searching protein and nucleotide databases (in FASTA format) on the web for peptides that match the molecular weight of the unknown peptides produced by digestion of your protein(s) of interest. Theoretical MS/MS spectra are then generated and a score is given to each one. The top 500 scored theoretical peptides are retained and a cross correlation analysis is then performed between the un-interpreted MS/MS spectra (real MS/MS spectra) of unknown peptides with each of the retained theoretical MS/MS spectra. Highly correlated spectra result in identification of the peptide sequences and multiple peptide identification and thus determine the protein and organism of origin corresponding to the unknown protein sample.
shotgun proteomics Proteins are analyzed by standard shotgun proteomics, beginning with tryptic digest of a protein mixture, liquid chromatographic separation of the mixture (2D HPLC), analysis of peptide masses by mass spectrometry (MS) and fragmentation of peptides and subsequent analysis of the fragmentation spectra (MS/MS). Each step introduces bias into the peptides ultimately interpreted from the analysis, thereby affecting the probability pij of observing each peptide j from protein i. APEX involves training a classifier to estimate Oi, the prior estimate of the number of unique peptides expected from a given protein during such an experiment. By correcting for Oi, the number of peptides observed per protein thereby provides an estimate of the protein's abundance. HPLC, high-performance liquid chromatography. Nature Biotechnology 25, 117 - 124 (2007)
Limitations, Challenges and Bottlenecks • Resolution: • ► number of proteins that can be separated/distinguished (500,000?!?) • ► pI resolution • ► mass resolution (gels and mass spectrometry) • Amount of the protein in the sample: • ► too little to be seen on a 2D gel? • ► too little to be extracted and digested? • Protein solubility • Database searching and peptide identification Bandara & Kennedy (2002)
Two-dimensional electrophoretic analysis of rat liver total proteins. The proteins were separated on a pH 3–10 nonlinear IPG strip (left), or pH 4-7 IPG strip (right), followed by a 10% SDS–polyacrylamide gel. The gel was stained with Coomassie blue. The spots were analyzed by MALDI-MS. The proteins identified are designated with the accession numbers of the corresponding database. From Fountoulakis & Suter (2002)
Summary of the 2-D gel electrophoresis data • In total, 273 different gene products were identified from all gels: • 65 gene products were only detected in the gels carrying total • 52 in the gels carrying cytosolic • remaining proteins were found in both samples • 45 proteins out of the 62 found in the gels carrying total protein samples were detected in the broad pH range 3–10 gel, 11 in the narrow pH range and nine in both types of gels • 52 proteins only detected in the gels carrying the cytosolic fraction, except for 6 which were found in the broad pH range 3–10 gel, were found in one of the narrow pH range gels only (narrow pH range strips helped to detect 46 proteins not found in the broad range gels) • Protein distribution was based on the protein identification by mass spectrometry and may not be complete due to: • spot loss during automatic excision • peptide loss mainly from weak spots • spot overlapping • small protein size • About 5000 spots were excised from 13 2-D gels, 5 carrying total and 8 carrying cytosolic proteins. The analysis resulted in the identification of about 3000 proteins, which were the products of 273 different genes From Fountoulakis & Suter (2002)
Summary of the 2-D gel electrophoresis data From Fountoulakis & Suter (2002)
Animals: Male Wistar rats (10–12 weeks, bw: 225±8 g) Treatment: Bromobenzene (i.p., 5.0 mmol/kg bw) dissolved in corn oil (40% v/v) Duration of treatment: 24 hrs The bromobenzene dose was hepatotoxic, and this was confirmed by the finding of a nearly complete glutathione depletion at 24 hr after bromobenzene administration. The low level of oxidised (GSSG) relative to reduced glutathione (GSH) indicates that the depletion is primarily due to conjugation and to a much lesser extent due to oxidation of glutathione. The bromobenzene administration resulted in on average 7% decrease in body weight after 24 hr. From: Heijne et al. (2003)
Gene Expression Profiling • Liver samples, total RNA (50 mg/array experiment) • cDNA microarrays (3000 genes) • Reference sample: • pooled RNA from liver (~50% w/w), kidneys, lungs, brain, thymus, testes, spleen, heart, and muscle of untreated Wistar rats • Duplicated microarray/sample • 2-Fold cutoff (p<0.01) relative to the vehicle control: • 32 genes were found to be significantly upregulated and 17 were repressed following bromobenzene treatment • 1.5-Fold cutoff (p<0.01) relative to the vehicle control: • 63 genes were found to be significantly upregulated and 35 genes were repressed following bromobenzene treatment • Functional groups: • Drug metabolism • Glutathione metabolism • Oxidative stress • Acute phase response • Protein synthesis • Protein degradation • Others From: Heijne et al. (2003)
Glutathione metabolism: Oxidative stress: From: Heijne et al. (2003)
Protein Expression Profiling • 3 two-dimensional gels were prepared from each sample • A reference protein pattern contained 1124 protein spots • 24 proteins were differentially expressed (BB or Corn oil) From: Heijne et al. (2003)
Liver is unique in its capability to regenerate after an injury. Liver regeneration after a 2/3 partial hepatectomy served as a classical model and is adopted frequently to study the mechanism of liver regeneration. In the present study, semi-quantitative analysis of protein expression in mouse liver regeneration following partial hepatectomy was performed using an iTRAQ technique. Proteins from pre-PHx control livers and livers regenerating for 24, 48 and 72 h were extracted and inspected using 4-plex isotope labeling, followed by liquid chromatography fractionation, mass spectrometry and statistical differential analysis. A total of 827 proteins were identified in this study. There were 270 proteins for which quantitative information was available at all the time points in both biologically duplicate experiments. Among the 270 proteins, Car3, Mif, Adh1, Lactb2, Fabp5, Es31, Acaa1b and LOC100044783 were consistently down-regulated, and Mat1a, Dnpep, Pabpc1, Apoa4, Oat, Hpx, Hp and Mt1 were up-regulated by a factor of at least 1.5 from that of the controls at one time point or more. The regulation of each differential protein was also demonstrated by monitoring its time-dependent expression changes during the regenerating process. We believe this is the first report to profile the protein changes in liver regeneration utilizing the iTRAQ proteomic technique.
Metabolomics is the Most Closely Related to Phenotype Dettmer et al., MS Reviews, 26, 51, 2007
Studying the Whole Metabolome Focused analysis of a single metabolic pathway CH2OP CH2OP 3-phosphoglyceric acid dehydrogenase CHOH CO CH2O- CH2O- Unbiased analysis of the entire metabolome
Typical Size Range of Metabolites Douglas B. Kell, Curr Opin Microbiol. 7, 296, 2004
Range of Tools Required to Cover the Entire Metabolome NMR mM (10-6) nM (10-9) LC/UV GC/MS pM (10-12) LC/MS fM (10-15) Adapted from Sumner, LW, et al., Phytochem, 62, 817,2003
Off-line hyphenation MS LC/MS GC/MS CE/MS NMR Chromatography LC/NMR Main Analytical Approaches to Metabolomics
Comparison of NMR vs MS for Metabonomics Taken from D.G. Robertson, Toxicological Sciences, 85, 809, 2005
Features of GC/MS Metabolomics • Useful for volatiles or compounds that can be derivatized to volatile compounds (derivatization often required) • Ideal for long chain compounds e.g. FFA, acyl carnitines, etc • More stable and reproducible than LC/MS • Most advanced metabolomics libraries • Standards are typically required for positive identification • Inexpensive technology Experiment Library match
Features of LC/MS Metabolomics • Chromatography can be tailored to specific chemical classes • Various MS analyzers can be coupled e.g. triple quad, TOF, ion trap each with it’s own advantages in speed, resolution and sensitivity. • Very high mass accuracy available with TOF instruments (< 2ppm) • Variable ionization efficiencies and matrix suppression leads to poor quantitation w/out standards • Excellent for targetted metabolomics, more challenging for global “unbiased” profiling • Q-TOF can acquire high res data + MS/MS for fragmentation analyses • Libraries are available but suffer from inconsistent retention times in the LC front end.
The NMR Phenomenon (Hydrogen nuclei act like little magnets) Hydrogen nuclei out and about Hydrogen nuclei in a magnetic field
The NMR Experiment RF pulse detector Excited state transverse to the field Aligned with the big magnetic field Precession based on magnetic environment & detection
The Chemical Shift Different hydrogen atoms (gray) are in unique chemical and magnetic environments This results in different precession frequencies and distinct spectral features.
Features of NMR PROS • High structural information content • Very high intra/inter-lab reproducibility • Inherently quantitative (no need for authentic standards) • Minimal sample processing required • Non-destructive • Expensive instrumentation • Relatively low sensitivity (typically >mM concentrations required) • Spectral crowding can hinder interpretation • Long chain aliphatics are challenging (e.g. fatty acids) CONS
Normal Metabolic Profiles (rat urine) Day 5 Day 4 Day 3 Day 2 Day 1 Adapted from D. Robertson, Pfizer Global Research and Development
Functional NMR Spectrum of Rat Urine “Biomarker Windows” Nature Reviews: Drug Discovery Nicholson et al. (2002)
Data Analysis in Metabolomics Supervised classification and calculation of confidence intervals Primary Data Processing Unsupervised mapping of data in 3D space NMR Spectra Nature Reviews: Drug Discovery Nicholson et al. (2002)
z z x x y y z PC2 x PC1 y Adapted from D. Robertson, Pfizer Global Research and Development
25 15 PC2 PC2 20 10 15 ANIT 10 5 5 0 0 ANIT -5 -5 Control -10 Control -15 PAP -10 -20 PC1 PC1 -15 -25 -40 -30 -20 -10 0 10 -30 -20 -10 0 10 p-Aminophenol (PAP) a-naphthylisothiocyanite (ANIT) Control Adapted from D. Robertson, Pfizer Global Research and Development
2D spectra 1H & 13C Set of 1D 1H spectra Metabolite ID KEGG Analysis NMR Database 1H & 13C Prediction Tools to Identify Biomarkers