Areas of Application for Proteomics in Toxicology

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1 Proteomics

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3 Areas of Application for Proteomics in Toxicology Diagnostics: detection of antigens and antibodies in blood samples profiling of sera to discover new disease markers environment and food monitoring Protein expression profiling: organ- and disease-specific arrays mode of action analysis Protein functional analysis: ligand-binding properties of receptors enzyme activities protein-protein interactions

4 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 Jul 13.

5 Antibody Arrays Screening protein-protein t i interactions ti Studying protein posttranslational modifications Examining gprotein expression patterns

6 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

7 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 o physical supports s include glass slides, silicon, microwells, nitrocellulose or PVDF membranes, microbeads Protein immobilization should be: reproducible e 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

8 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 i detection ti are included. d Source: Panomics, Inc.

9 Protein Activity Arrays Gel ShiftAssay Protein Array Source: Panomics, Inc.

10 2D Gel Electrophoresis + Mass Spectrometry Meyer et al Amino Acids Jul 13.

11 2D Gel Electrophoresis Protein Resolution Bandara & Kennedy (2002)

12 2D Gel Electrophoresis Protein Resolution Courtesy of Bio-Rad Courtesy of Bio-Rad Courtesy of Fermentas

13 2D Gel Electrophoresis Image Analysis Courtesy of Decodon Courtesy of Alphainnotech

14 2D Gel Electrophoresis Mass Spectrometry Source: UNC Proteomics Core Facility

15 Imag izona Proteomi University of Ari e courtesy of U ics 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.

16 Bandara & Kennedy (2002)

17 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 p ij of observing each peptide j from protein i. APEX involves training a classifier to estimate O i, the prior estimate of the number of unique peptides expected from a given protein during such an experiment. By correcting for O i, the number of peptides observed per protein thereby provides an estimate of the protein's abundance. HPLC, high performance liquid chromatography. Nature Biotechnology 25, (2007)

18 Isotope Coded Affinity Tag (ICAT) Analysis Bandara & Kennedy (2002)

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20 SELDI Analysis Perticoin et al., Toxicologic Pathology, 32(Suppl. 1): , 2004

21 Representative raw spectra and gel-view (grey-scale) of serum from a normal donor, and from patients with either BPH (benign prostate hyperplasia) or prostate cancer (PCA) using the IMAC3-Cu Cu chip chemistry From:

22 Courtesy CIPHERGEN The upregulated 11.9 kda biomarker from the TMPD-treated rats was searched via Tagldent (SWISS-PROT), yielding a tentative identity as parvalbumin-alpha. This candidate was subsequently purified, peptide mapped and searched to confirm the identity. Parvalbumin is involved in muscle homeostasis.

23 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)

24 Schneider LV, Hall MP. Drug Discov Today :

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26 T di i l l t h ti l i f t li t t l t i Th t i t d H 3 10 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)

27 Two dimensional electrophoretic analysis of rat liver cytosolic proteins The proteins were separated on a ph Two-dimensional electrophoretic analysis of rat liver cytosolic proteins. The proteins were separated on a ph 3 10 nonlinear IPG strip (left), or ph 5 6 IPG strip (right), followed by a 10% SDS polyacrylamide gel. The spots were analyzed by MALDI-MS. The proteins identified are designated with the accession numbers of the corresponding database. From Fountoulakis & Suter (2002)

28 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)

29 Summary of the 2-D gel electrophoresis data From Fountoulakis & Suter (2002)

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31 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)

32 Gene Expression Profiling Liver samples, total RNA (50 g/array y 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 ifi 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)

33 Glutathione metabolism: Oxidative stress: From: Heijne et al. (2003)

34 Acute phase response: From: Heijne et al. (2003)

35 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)

36 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 g 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 LOC 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.

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38 Proteome Res., 5 (7), , 2006 Systems Toxicology: Integrated Genomic, Proteomic and Metabonomic Analysis of Methapyrilene Induced Hepatotoxicity in the Rat Andrew Craig, James Sidaway, Elaine Holmes, Terry Orton, David Jackson, Rachel Rowlinson, Janice Nickson, Robert Tonge, Ian Wilson, and Jeremy Nicholson Abstract: Administration of high doses of the histamine antagonist methapyrilene to rats causes periportal liver necrosis. The mechanism of toxicity is ill-defined and here we have utilized an integrated systems approach to understanding the toxic mechanisms by combining proteomics, metabonomics by 1H NMR spectroscopy and genomics by microarray gene expression profiling. Male rats were dosed with methapyrilene for 3 days at 150 mg/kg/day, which was sufficient to induce liver necrosis, or a subtoxic dose of 50 mg/kg/day. Urine was collected over 24 h each day, while blood and liver tissues were obtained at 2 h after the final dose. The resulting data further define the changes that occur in signal transduction and metabolic pathways during methapyrilene hepatotoxicity, revealing modification of expression levels of genes and proteins associated with oxidative stress and a change in energy usage that is reflected in both gene/protein expression patterns and metabolites. The difficulties of combining and interpreting multi-omic data are considered.

39 Methapyrilene-induced liver injury in the rat Vehicle 10 mg/kg, 7 days 100 mg/kg, 7 days 100 mg/kg, 7 days Hamadeh et al 2002 Tox Path

40 Systems Toxicology: Integrated Genomic, Proteomic and Metabonomic Analysis of Methapyrilene Induced Hepatotoxicity in the Rat Proteins altered and identified between control and methapyrilene dosed groups. Proteins are numbered Ex where elevated and Rx where reduced. Average standard 1H NMR spectra of liver from each treatment group. This figure shows clearly dose related elevations and composition changes in fatty acid species

41 Systems Toxicology: Integrated Genomic, Proteomic and Metabonomic Analysis of Methapyrilene Induced Hepatotoxicity in the Rat

42 Systems Toxicology: Integrated Genomic, Proteomic and Metabonomic Analysis of Methapyrilene Induced Hepatotoxicity in the Rat Our aim was to determine the impact of drug toxicity on hepatic metabolic pathways and also ascertain whether a multiomic i systems biology approach would result in improved understanding di of the mechanism of hepatotoxicity t it of the drug The combination of information from gene, protein and metabolite levels provides an integrated picture of the response to methapyrilene-induced hepatotoxicity with mutually supporting and mutually validating evidence arising from each biomolecular level. As expected there were several instances where genes and proteins, either encoded by the same gene or by other genes within the same pathway, were both co regulated by methapyrilene toxicity, and sometimes this was in concert with an associated metabolic product However: Strategy of parallel omic data sets: It should be noted that alterations in expression of genes or enzyme levels and modification of protein forms, while suggesting a potential target of toxic effects, do not imply that function or activity must be altered Alterations to metabolic profiles reflect function and so may serve to aid interpretation of corresponding gene expression and proteomic analyses Furthermore, as metabolites unlike genes do not suffer the problem of orthology, observed metabolic effects are likely to be highly conserved between species and integrated systems approaches applied to two species may be one framework within which to reconcile and understand the similarities and differences in genetic wiring of common biological processes between different species. Issue of experimental design: looking at time points where toxicity is already well developed mitigates against obtaining a clear understanding of the temporal dynamics of the mechanism, especially as changes at the gene, protein and metabolite level may proceed at different rates and on different time scales. As such we might expect highly non linear relationships between the concentrations of various species at the different levels of biomolecular organization Issue of molecular resolution: we detected 100s of gene expression changes compared to the relatively small number of changes detected by the other two technologies. It may thus be likely that insufficient detail was obtained at each biomolecular level to elaborate fully on mechanism of methapyrilene toxicity Statistical difficulties: Since each data type usually requires tailored preprocessing (normalization, transformation, scaling, etc.) combining multiple data sets presents a significant analytical challenge. Here, we have performed a separate analysis at the gene, protein, and metabolite level and integrated the knowledge gained from each data set to uncover pathways which responded to the methapyrilene-induced toxicity.