Genome Sequencing Technologies

High-throughput gene sequencing began when Sanger and colleagues introduced dideoxynucleotide sequencing in 1977 (Sanger et al. 1977). Technical innovations over the next 18 years led to the development of current generation automated instruments, which use fluorescent tagging and capillary electrophoresis to sequence up to 1.6 million base pairs (bp) per day (Chan 2005). These instruments have become the principal engines of genome sequencing projects. Despite the impressive advances in technology, the cost of genome sequencing remains a barrier to the application of whole-genome sequencing to study individual variation in toxic responses and susceptibility. Current costs for wholegenome sequencing in a modern genome sequencing center using Sanger sequencing technology were estimated at approximately $0.004 per bp, which translates to approximately $12M for an entire human genome (Chan 2005).

Fundamental technology limitations of automated Sanger sequencing have spawned the development of new technologies that promise significant cost and throughput improvements. A cycle extension approach using a highly parallel picoliter reactor system and a pyrosequencing protocol sequenced 25 million bp at 99% accuracy in a 4-h analysis (Margulies et al. 2005).

This represents a 100-fold increase in throughput over the best available Sanger sequencing instrumentation and is now marketed commercially (454 Life Sciences 2006). Church and colleagues (Shendure et al. 2005) recently reported the use of a microscopy-based sequencing approach that employs “off-the-shelf” reagents and equipment and is approximately one-ninth as costly as high-speed Sanger sequencing (Shendure et al. 2005). Nanotechnology approaches may yield even more efficient DNA sequencing instrumentation, but these approaches have not yet been implemented for high-throughput sequencing in a research laboratory setting (Chan et al. 2004; Chan 2005).

The dominant genome sequencing strategy to emerge from the human genome project is the whole-genome “shotgun” sequencing approach (Venter et al. 1996), which entails breaking up chromosomal DNA into fragments of 500-1,000 DNA bases, which then are subjected to automated, repetitive sequencing and subsequent data analyses to reassemble the fragment sequences into their original chromosomal context. With this technique, most DNA bases on a chromosome are sequenced three to seven times, resulting in cost- and time-efficient generation of high-fidelity sequence information.

Genotype Analysis

Analysis of genetic variation between individuals involves first the discovery of SNPs and then the analysis of these variations in populations. SNPs occur on approximately every 2 kilobases in the human genome and have been commonly detected by automated Sanger sequencing as discussed above. The Human DNA Polymorphism Discovery Program of the National Institute of Environmental Health Sciences Environmental Genome Project (EGP) is one example of the application of automated DNA sequencing technologies to identify SNPs in human genes (Livingston et al. 2004). The EGP selected 293 “candidate genes” for sequencing based on their known or anticipated roles in the metabolism of xenobiotics. The major limitation of this candidate gene approach is that it enables discovery of polymorphisms only in those genes targeted for analysis.

This analysis at the population level requires high-throughput, cost-effective tools to analyze specific SNPs. The analysis generally involves PCR-based amplification of a target gene and allele-specific detection. The dominant approaches in current use are homogeneous systems using TaqMan (Livak 1999) and molecular beacons (Marras et al. 1999), which offer high throughput in a multiwell format. However, these systems are not multiplexed (multiple SNPs cannot be analyzed simultaneously in the same sample), and this ultimately limits throughput. Other approaches may provide the ability to analyze multiple SNPs simultaneously. For example, matrix-assisted laser desorption ionization MS (MALDI-MS) has been used to simultaneously analyze multiple products of primer-extension reactions (Gut 2004). A more highly multiplexed approach is the combination of tag-based array technology with primer extension for genotyping (Phillips et al. 2003; Zhang et al. 2004).

The ultimate approach for SNP discovery is individual whole-genome sequencing. Although not yet feasible, rapidly advancing technology makes this approach the logical objective for SNP discovery in the future. In the meantime, rapid advances in microarray-based SNP analysis technology have redefined the scope of SNP discovery, mapping, and genotyping. New microarray-based genotyping technology enables “whole-genome association” analyses of SNPs between individuals or between strains of laboratory animals (Syvanen 2005). In contrast to the candidate gene approach described above, whole-genome association identifies hundreds to thousands of SNPs in multiple genes. Arrays used for these analyses can represent a million or more polymorphic sequences mapped across a genome (Gunderson et al. 2005; Hinds et al. 2005; Klein et al. 2005). This approach makes it possible to identify SNPs associated with disease and susceptibility to toxic insult. As more SNPs are identified and incorporated into microarrays, the approach samples a greater fraction of the genome and becomes increasingly powerful. The strength of this technology is that it puts a massive amount of easily measurable genetic variation in the hands of researchers in a cost-effective manner ($500-$1,000 per chip). Criteria for including the selected SNPs on the arrays are a critical consideration, as these affect inferences that can be drawn from the use of these platforms.

Epigenetics

Analysis of genes and genomes is not strictly confined to sequences, as modifications of DNA also influence the expression of genes. Epigenetics refers to the study of reversible heritable changes in gene function that occur without a change in the sequence of nuclear DNA. These changes are major regulators of gene expression. Differences in gene expression due to epigenetic factors are increasingly recognized as an important basis for individual variation in susceptibility and disease (Scarano et al 2005), as discussed in Chapter 6.

Epigenetic phenomena include DNA methylation, imprinting, and histone modifications. Of all the different types of epigenetic modifications, DNA methylation is the most easily measured and amenable to the efficient analysis characteristic of toxicogenomic technologies. DNA methylation refers to addition of a methyl group to the 5-carbon of cytosine in an area of DNA where there are many cytosines and guanines (a CpG island) by the enzyme DNA methyltransferase. Many different methods for analyzing DNA methylation have been developed and fall into two main types—global and gene-specific. Global methylation analysis methods measure the overall level of methyl cytosines in the genome using chromatographic methods (Ramsahoye 2002) or methyl accepting capacity assays. Gene-specific methylation analysis methods originally used methylation-sensitive restriction enzymes to digest DNA before it was analyzed by Southern blot analysis or PCR amplification. Sites that were methy lated were identified by their resistance to the enzymes. Recently, methylationsensitive primers or bisulfite conversion of unmethylated cytosine to other bases have been used in methods such as methylation specific PCR and bisulfite genomic sequencing. These methods give a precise map of the pattern of DNA methylation in a particular genomic region or gene and are fast and cost effective (e.g., Yang et al. 2006).

To identify unknown methylation hot-spots within a larger genomic context, techniques such as Restriction Landmark Genomic Scanning for Methylation (Ando and Hayashizaki 2006) and CpG island microarrays (e.g., Y. Wang et al. 2005a; Bibikova et al. 2006; Hayashi et al. 2007) have also been developed. Restriction landmark genomic scanning (RLGS) is a method to detect large numbers of methylated cytosines sites in a single experiment using direct end-labeling of the genomic DNA digested with a restriction enzyme and separated by high-resolution two-dimensional electrophoresis.

Several array-based methods have also been developed recently. Bibikova et al. (2006) developed a rapid method for analyzing the methylation status of hundreds of preselected genes simultaneously through an adaptation of a genotyping assay. For example, the methylation state of 1536 specific CpG sites in 371 genes was measured in a single reaction by multiplexed genotyping of bisulfite-treated genomic DNA. The efficient nature of this quantitative assay could be useful for DNA methylation studies in large epidemiologic samples. Hayashi et al. (2007) describe a method for analysis of DNA methylation using oligonucleotide microarrays that involves separating methylated DNA immunoprecipitated with anti-methylcytosine antibodies.

Most of these gene-specific methods work consistently at various genomic locations (that is, they have minimal bias by genomic location) and will be useful for high-resolution analysis of the epigenetic modifications to the genome in toxicogenomic studies.

Technologic Approaches

Technologies for assaying gene, protein, and metabolic expression profiles are not new inventions. Measurements of gene expression have evolved from the single measures of steady-state mRNA using Northern blot analysis to the more global analysis of thousands of genes using DNA microarrays and serial analysis of gene expression (SAGE), the two dominant technologies (Figure 2-2). The advantage of global approaches is the ability of a single investigation to query the behavior of hundreds, thousands, or tens of thousands of biologic molecules in a single assay. For example, in profiling gene expression, one might use technologies such as Northern blot analysis to look at expression of a single gene. Quantitative real-time reverse transcriptase PCR (qRT-PCR), often used with subtractive cloning or differential display, can easily be used to study the expression of 10 or more genes.

FIGURE 2-2

Overview of commonly used methods and technologies for gene expression analysis.

Techniques such as SAGE allow the entire collection of transcripts to be catalogued without assumptions about what is actually expressed (unlike microarrays, where one needs to select probes from a catalogue of genes). SAGE is a technology based on sequencing strings of short expressed sequence tags representing both the identity and the frequency of occurrence of specific sequences within the transcriptome. SAGE is costly and relatively low throughput, because each sample to be analyzed requires a SAGE Tag library to be constructed and sequenced. Massively parallel signature sequencing speeds up the SAGE process with a bead-based approach that simultaneously sequences multiple tags, but it is costly.

DNA microarray technology can be used to generate large amounts of data at moderate cost but is limited to surveys of genes that are included in the microarray. In this technology (see Box 2-1 and Figure 2-3), a solid matrix surface supports thousands of different, surface-bound DNAs, which are hybridized against a pool of RNA to measure gene expression. A systematic comparison indicates that gene expression measured by oligonucleotide microarrays correlates well with SAGE in transcriptional profiling, particularly for genes expressed at high levels (Kim 2003).

BOX 2-1

Experimental Details of Transcriptome Profiling with Microarrays. TmRNA extracted from cell or tissue samples is prepared for microarray analysis by PCR-based amplification (Hardiman 2004). A fluorescent dye (or biotin for Affymetrix microarrays) is incorporated (more...)

FIGURE 2-3

An overview of DNA microarray analysis. (A) In two-color analysis approaches, RNA samples from patient and control samples are individually labeled with distinguishable fluorescent dyes and cohybridized to a single DNA microarray consisting of individual (more...)

Although toxicogenomic studies typically rely on technologies that generate large amounts of data, results are often confirmed and replicated with lower throughput assays. For example, differential gene expression detected with more global approaches is often verified by qRT-PCR analysis. The utility of these lower throughput approaches goes beyond validation. A subset of genes analyzed by qRT-PCR may exhibit sensitivity and specificity comparable to global transcriptomic analyses with microarrays. Relatively small sets of marker genes that represent more complex gene expression patterns may be of considerable value in toxicogenomics.

DNA Microarray Technology

Microarray technology (Figure 2-3) fundamentally advanced biology by enabling the simultaneous analysis of all transcripts in a system. This capability for simultaneous, global analysis is emblematic of the new biology in the genomic era and has become the standard against which other global analysis technologies are judged. DNA microarrays contain collections of oligonucleotide sequences located in precise locations in a high-density format. Two complementary DNA (cDNA) microarray formats have come to dominate the field. Spotted microarrays are prepared from synthesized cDNAs or oligonucleotide probes that are printed on a treated glass slide surface in a high-density format. These spotted arrays were the first widely used DNA microarrays (Schena et al. 1995 , 1996) and were originally printed in individual investigators’ laboratories from collections of clones. Complications in characterizing, managing, and standardizing these collections led to substantial variability in performance. Commercially produced oligonucleotide microarrays, in which oligonucleotides are synthesized in situ using inkjet printing, have largely replaced cDNA microarrays (Hughes et al. 2001). Whole-genome microarrays for human and mouse genomes contain 40,000-45,000 features corresponding to unique genes and transcripts. The probes range from 20 to 60 bp and individual microarrays typically contain between 5,000 and 50,000 features. The longer probes provide improved sensitivity and tolerance of polymorphic sequence mismatches. Several commercial vendors provide spotted arrays or variants of this technology and development in this area continues (Hardiman 2004).

The alternative technology uses photolithographic synthesis of oligonucleotide probes on a quartz surface and was developed by Affymetrix (Fodor et al. 1993; Pease et al. 1994; Lipshutz et al. 1999). These GeneChip arrays are characterized by very high probe densities (up to 1.3 million probes per chip) and typically consist of up to 25-mer probes (probes with 25-base residues). Each “gene” may be represented by as many as 20 overlapping probe sequences and paired mismatch probes, which contain sequence substitutions that enable quantitative evaluation of nonspecific hybridization. Elaboration of this mismatch strategy also allows analysis of SNPs by microarray analysis (see SNP discussion in section above). Considerable research into probe design has contributed to the improvement of microarray performance and has facilitated the standardization of transcriptome analysis.

Other array formats have been developed. Nylon membranes and plastic microarrays have been used with varying degrees of success (Qian et al. 2005). Nylon membranes produce low- to medium-density cDNA microarrays, whereas plastic retains the advantages of glass for producing high-density microarrays that are somewhat cheaper than glass slide arrays. The probes for nylon arrays are typically labeled with radioactive phosphorus isotopes (³²P or ³³P) to afford increases in sensitivity, but this approach is not favored because of problems associated with the use of radioactivity and efficiency of analysis.

Affymetrix and other major commercial vendors (Agilent, GE Healthcare [formerly Amersham], and Applied Biosystems) currently offer several different microarrays corresponding to essentially all known genes and transcripts for human as well as similar microarray products for model organisms used in toxicity studies. In addition, Affymetrix also offers whole-genome microarrays for application to SNP mapping and detection (see above).

Gel-Based Proteomics

Two major approaches used are gel-based proteomics and “shotgun” proteomics (see Figure 2-4). In the gel-based approach, proteins are resolved by electrophoresis or another separation method and protein features of interest are selected for analysis. This approach is best represented by the use of two-dimensional sodium dodecylsulfate polyacrylamide gel electrophoresis (2D-SDS-PAGE) to separate protein mixtures, followed by selection of spots, and identification of the proteins by digestion to peptides, MS analysis, and database searching. Gel-based analyses generate an observable “map” of the proteome analyzed, although liquid separations and software can be coupled to achieve analogous results. Reproducibility of 2D gel separations has dramatically improved with the introduction of commercially available immobilized pH gradient strips and precast gel systems (Righetti and Bossi 1997). Comparative 2D-SDS-PAGE with differential fluorescent labeling (for example, differential gel electrophoresis, DIGE) offers powerful quantitative comparisons of proteomes (Tonge et al. 2001; Von Eggeling et al. 2001). Moreover, modified protein forms are often resolved from unmodified forms, which enable separate characterization and quantitative analysis of each. Although 2D gels have been applied most commonly to global analyses of complex proteomes, they have great potential for comparative analyses of smaller subproteomes (for example, multiprotein complexes). Problems with gel-based analyses stem from the poor separation characteristics of proteins with extreme physical characteristics, such as hydrophobic membrane proteins. A major problem is the limited dynamic range for protein detection by staining (200- to 500-fold), whereas protein abundances vary more than a million fold (Gygi et al. 2000). This means that abundant proteins tend to preclude the detection of less abundant proteins in complex mixtures. This problem is not unique to gel-based approaches.

FIGURE 2-4

Schematic representation of 2D gel-based proteome analysis and shotgun proteome analysis. LC, liquid chromatography; TOF, time of flight; MS-MS, tandem mass spectrometry.

Shotgun Proteomics

Shotgun proteomic analysis is somewhat analogous to the genome sequencing strategy of the same name. Shotgun analyses begin with direct digestion of protein mixtures to complex mixtures of peptides, which then are analyzed by liquid-chromatography-coupled mass spectrometry (LC-MS) (Yates 1998). The resulting collection of peptide tandem mass spectrometry (MS-MS) spectra is searched against databases to identify corresponding peptide sequences and then the collection of sequences is reassembled using computer software to provide an inventory of the proteins in the original sample mixture. A key advantage of shotgun proteomics is its unsurpassed performance in the analysis of complex peptide mixtures (Wolters et al. 2001; Washburn et al. 2002). Peptide MS-MS spectra are acquired by automated LC-MS-MS analyses in which ions corresponding to intact peptides are automatically selected for fragmentation to produce MS-MS spectra that encode peptide sequences (Stahl et al. 1996). This approach enables automated analyses of complex mixtures without user intervention. However, selection of peptide ions for MS-MS fragmentation is based on the intensity of the peptide ion signals, which favors acquisition of MS-MS spectra from the most abundant peptides in a mixture. Thus, detection of low-abundance peptides in complex mixtures is somewhat random. Application of multidimensional chromatographic separations (for example, ion exchange and then reverse-phase high-performance liquid chromatography) “spreads out” the peptide mixture and greatly increases the number of peptides for which MS-MS spectra are acquired (Link et al. 1999; Washburn et al. 2001; Wolters et al. 2001). This increases the detection of less abundant proteins and modified protein forms (MacCoss et al. 2002) (see below). New hybrid linear ion trap-tandem MS instruments offer more rapid acquisition of MS-MS spectra and more accurate identification of peptide ion mass-to-charge ratio values, which provides more identifications with greater reliability. Nevertheless, a continuing challenge of shotgun proteome analyses is the identification of less abundant proteins and modified protein forms.

Quantitative Proteomics

The application of quantitative analyses has become a critical element of proteome analyses. Quantitative methods have been developed for application to both gel-based and shotgun proteomic analyses. The most effective quantitative approach for gel-based analyses is DIGE (see above), which involves using amine- or thiol-reactive fluorescent dyes that tag protein samples with different fluorophores for analysis on the same gel. This approach eliminates gel-to-gel variations inherent in comparing spots from individual samples run on different gels. The use of a separate dye and mixed internal standards allows gel-to-gel comparisons of DIGE analyses for larger studies and enables reliable statistical comparisons (Alban et al. 2003). Quantitative shotgun proteome analyses have been done with stable isotope tags, which are used to derivatize functional groups on proteins (Julka and Regnier 2004). Stable isotope tagging is usually used in paired experimental designs, in which the relative amounts of a protein or protein form are measured rather than the absolute amount in a sample. The first of these to be introduced were the thiol-reactive isotope-coded affinity tag reagents (Gygi et al. 1999), which have been further developed to incorporate solid-phase capture and labeling (Zhou et al. 2002). These reagents are available in “heavy” (for example, ²H- or ¹³C-labeled) and “light” (for example, ¹H- or ¹²C-labeled) forms. Analysis of paired samples labeled with the light and heavy tags allows relative quantitation by comparing the signals for the corresponding light and heavy ions. Other tag chemistries that target peptide N and C termini have been developed and have been widely applied (Julka and Regnier 2004). An alternative approach to tagging proteins and peptides is to incorporate stable isotope labels through metabolic labeling of proteins during synthesis in cell culture (Ong et al. 2002). Quantitative proteomic approaches are applicable not only to comparing amounts of proteins in samples but also to kinetic studies of protein modifications and abundance changes as well as to identification of protein components of multiprotein complexes as a function of specific experimental variables (Ranish et al. 2003).

Major limitations of the isotope-tagging approaches described above include the requirement for chemical induction of changes in the samples (derivatization) or metabolic incorporation of isotope label and the need to perform quantitative analyses by pairwise comparison. Recent work has demonstrated that quantitative data from LC-MS full-scan analyses of intact peptides (W. Wang et al. 2003) and data from MS-MS spectra acquired from peptides are proportional to the peptide concentration in mixtures (Gao et al. 2003; Liu et al. 2004). This suggests that survey-level quantitative comparisons between any samples analyzed under similar conditions may be possible.

Finally, the use of stable isotope dilution LC-MS-MS analysis provides a method for absolute quantification of individual proteins in complex samples (Gerber et al. 2003). Use of stable-isotope-labeled standard peptides that uniquely correspond to proteins or protein forms of interest are spiked into proteolytic digests from complex mixtures, and the levels of the target protein are measured relative to the labeled standard. This approach holds great potential for targeted quantitative analysis of candidate biomarkers in biologic fluids (Anderson et al. 2004).

Bioinformatic Tools for Proteomics

A hierarchy of proteomic data is rooted in MS and MS-MS spectra (Figure 2-5) and includes identification and quantitation of proteins and peptides and their modified forms, including comparisons across multiple experiments, analyses, and datasets. A key element of MS-based proteomic platforms is the identification of peptide and protein sequences from MS data. This task is accom plished with a variety of algorithms and software (“bioinformatics” tools) that search protein and nucleotide sequence databases (Fenyo 2000; Sadygov et al. 2004; MacCoss 2005). Measured peptide masses from MALDI-MS spectra of tryptic peptide digests can be searched against databases to identify the corresponding proteins (Perkins et al. 1999). This peptide mass fingerprinting approach works best with relatively pure protein samples. The most widely used and most effective approach is to search uninterpreted MS-MS spectra against database sequences with algorithms and software, such as Sequest, Mascot, and X!Tandem (Eng et al. 1994; Perkins et al. 1999; Craig and Beavis 2004). These algorithms match all spectra to some sequence and provide scores or probability assessments of the quality of the matches. Nevertheless, the balance between sensitivity and specificity in these analyses amounts to a trade-off between missed identifications (low sensitivity) and false-positive identifications (poor specificity) (Nesvizhskii and Aebersold 2004). A second tier of bioinformatic tools evaluates outputs from database search algorithms and estimates probabilities of correct protein identifications (Keller et al. 2002; Nesvizhskii et al. 2003). Other software applications have been developed to enable the identification of modified peptide forms from MS-MS data, even when the chemical nature and amino acid specificity of the modification cannot be predicted (Hansen et al. 2001 , 2005; Liebler et al. 2002 , 2003).

FIGURE 2-5

A hierarchy of proteomic data is rooted in MS and MS-MS spectra (level 1) and includes outputs of database search analyses and related data reduction (level 2), integrated information about single proteins (level 3), and information about groups of proteins (more...)

A key issue in proteomics is the standardization of data analysis methods and data representation and reporting formats. A fundamental problem is the variety of MS instruments, data analysis algorithms, and software used in proteomics. These generate a variety of different data types that describe proteins and peptides and their modifications. Proposals for common representations of MS and proteomic data have been published recently (Taylor et al. 2003; Craig et al. 2004; Pedrioli et al. 2004). In addition, draft criteria for data reporting standards for publication in proteomic journals are under consideration (e.g., Bradshaw 2005a). Another useful development is the emerging collection of databases of matched peptide and protein sequences and corresponding spectral data that define them (Craig et al. 2004; Desiere et al. 2005 , 2006).

Another important, but unresolved, issue concerns the differences in protein and peptide identifications attributable to the use of different database search algorithms. Because different database search software packages are sold with different MS instruments (for example, Sequest is licensed with Thermo ion trap MS instruments), differences in performance of the algorithms are difficult to separate from differences in characteristics of the instruments. Another issue in comparing the performance of different database searching software is wide variation in identifications due to variation in criteria used to filter the search results (Peng et al. 2003; Elias et al. 2005). This situation will be improved somewhat by adopting standards for reporting false-positive identification rates (Bradshaw 2005b).

Although the efforts described above represent useful steps in information sharing and management, the diversity of instrumentation, analytical approaches, and available data analysis tools will make standardization of informatics an ongoing challenge.

Proteome Profiling

Another type of proteome analysis that has attracted widespread interest is proteome profiling, in which MALDI time-of-flight (MALDI-TOF) MS is used to acquire a spectral profile of a tissue or biofluid sample (for example, serum) (Chaurand et al. 1999; Petricoin et al. 2002a,b; Villanueva et al. 2004). The signals in these spectra represent intact proteins or protein fragments and collectively reflect the biologic state of the system but, in profiling (compared with approaches described above), the overall pattern rather than identification of specific proteins or protein fragments is the focus. Analyses with high-performance MALDI-TOF instruments can generate spectral profiles containing hundreds to thousands of signals. These typically correspond to the most abundant, lower molecular weight (<25 kilodaltons) components of proteomes. Machine learning approaches have been used to identify sets of spectral features that can classify samples based on spectral patterns (Baggerly et al. 2004 , 2005; Conrads et al. 2004). This approach has attracted considerable interest as a potential means of biomarker discovery for early detection of diseases, particularly cancers, as well as drug toxicity. Despite intense interest, proteome profiling studies have created considerable controversy due to problems with lab-to-lab reproducibility of the marker sets identified, a lack of identification of the proteins corresponding to the marker signals, and artifacts in data generation and analysis (Diamandis 2004; Baggerly et al. 2005). In addition, studies that have identified some of the marker species have shown that they typically are proteolysis products of abundant blood proteins (Marshall et al. 2003), which raises questions about the biologic relationship of the markers to the disease processes under study. The general utility of biofluid proteome profiling for biomarker discovery remains an attractive, if unproven, approach. Nevertheless, methods of instrumental and data analysis are rapidly evolving in this field, and the applicability of this approach should be better substantiated within the next 2-3 years.

New MS Instrumentation and Related Technology for Proteomics

Despite impressive advances over the past 15 years, MS instrumentation for proteomics is limited in the numbers of peptides or proteins that can be identified and in the quality of the data generated. New hybrid tandem MS instruments that couple rapid-scanning linear ion tray analyzers with Fourier transform ion cyclotron resonance (FTICR), high-resolution ion trap, and TOF mass analyzers offer both high mass accuracy measurements of peptide ions and rapid scanning acquisition of MS-MS spectra (Syka et al. 2004a; Hu et al. 2005). This improves the fidelity of identification and the mapping of modifications (Wu et al. 2005). New methods for generating peptide sequence data, such as electron transfer dissociation (Syka et al. 2004b), can improve the mapping of posttranslational modifications and chemical adducts. An important emerging application of FTICR instrumentation is the tandem MS analysis of intact proteins, which is referred to as “top-down” MS analysis (Ge et al. 2002; Kelleher 2004). This approach can generate near-comprehensive sequence analysis of individual protein molecular forms, thus enabling sequence-specific annotation of individual modification variants (Pesavento et al. 2004; Coon et al. 2005). A limitation of the approach is the requirement for relatively purified proteins and larger amounts of samples than are used in shotgun analyses. However, rapid technology development will make top-down methods increasingly useful for targeted analyses of individual proteins and their modified forms.

Non-MS-Based Proteomic Approaches

Non-MS-based technologies have been applied to proteome analyses, but they have not proven to be as robust and versatile as MS-based methods. Microarray technology approaches include antibody microarrays, in which immobilized antibodies recognize proteins in complex mixtures (de Wildt et al. 2000; Miller et al. 2003; Olle et al. 2005). Although straightforward in principle, this approach has not proven robust and reliable for several reasons. Monospecific antibodies with high affinity for their targets are difficult to obtain and they often lose activity when immobilized. Because arrays must be probed under native conditions, antibodies may capture multiprotein complexes as well as individual proteins, which complicates interpretation. Short strands of chemically synthe sized nucleic acid (aptamers) have been studied as potential monospecific recognition molecules for microarrays, and this technology may eventually overcome some of the problems with antibody arrays (Smith et al. 2003; Kirby et al. 2004). “Reversed-phase” microarrays, which consist of multiple samples of protein mixtures (for example, tissues, cell lysates), are probed with individual antibodies (Paweletz et al. 2001; Janzi et al. 2005). This establishes the presence of the target protein in multiple samples rather than the presence of multiple proteins in any sample. As with antibody microarrays, the main limitations of this approach stem from the quality and availability of antibodies for the targets of interest. Microarrays of expressed proteins or protein domain substructures have been probed with tagged proteins or small molecules to identify protein binding partners or small molecule ligand sites or to conduct surveys of substrates for enzymes (for example, kinases) (Zhu et al. 2001; Ramachandran et al. 2004). This approach is directed to functional analysis of known proteins as opposed to identification and analysis of the components of complex mixtures. A related technique directed at the study of protein-protein interactions is surface plasmon resonance (SPR) (Liedberg et al. 1995; Homola 2003; Yuk and Ha 2005). This technology allows real-time measurements of protein binding affinities and interactions. In common usage, SPR is used to study single pairs of interacting species. However, recent adaptations of SPR allow direct analysis of protein-protein interactions in microarray format (Yuk et al. 2004).

NMR-Based Metabolomics

The principal technology platforms for metabolomics are NMR spectroscopy and gas chromatography MS (GC-MS) or LC-MS. NMR has been the dominant technology for metabolomic studies of biofluids ex vivo. High-field (600 mHz) ¹H-NMR spectra of urine contain thousands of signals representing hundreds to thousands of metabolites (Nicholson et al. 2002). Hundreds of analytes have been identified in such spectra and collectively represent a plurality of metabolic processes and networks from multiple organs and tissues. Although NMR has been most commonly applied to urine samples, similar analyses of intact solid tissues were accomplished with the use of magic angle spinning ¹H-NMR (Waters et al. 2000; Nicholson et al. 2002; Y. Wang et al. 2003).

Although it is possible to establish the identity of many, but not all, of the peaks in NMR spectra of urine and biofluids, the value of the data has been in the analyses of collections of spectral signals. These pattern recognition approaches have been used to identify distinguishing characteristics of samples or sample sets. Unsupervised² analyses of the data, such as principal components analysis (PCA), have proven useful for grouping samples based on sets of similar features (Beckwith-Hall et al. 1998; Holmes et al. 1998). These similar features frequently reflect chemical similarity in metabolite composition and thus similar courses of response to toxicants. Supervised analyses allow the use of data from biochemically or toxicologically defined samples to establish models capable of classifying samples based on multiple features in the spectra (Stoyanova et al. 2004).

NMR-based metabolomics of urine measure global metabolic changes that have occurred throughout an organism. However, metabolite profiles in urine can also indicate tissue-specific toxicities. PCA of urinary NMR data have shown that the development and resolution of chemically induced tissue injury can be followed by plotting trajectories of PCA-derived parameters (Azmi et al. 2002). Although the patterns themselves provide a basis for analyses, some specific metabolites have also been identified (based on their resonances in the NMR spectra). Mapping these metabolites onto known metabolic pathways makes it possible to draw inferences about the biochemical and cellular consequences and mechanisms of injury (Griffin et al. 2004). An interesting and important consequence was the identification of endogenous bacterial metabolites as key elements of diagnostic metabonomic profiles (Nicholls et al. 2003; Wilson and Nicholson 2003; Robosky et al. 2005). Although the interplay of gut bacteria with drug and chemical metabolism had been known previously, recent NMR metabolomic studies indicate that interactions between host tissues and gut microbes have a much more pronounced effect on susceptibility to injury than had been appreciated previously (Nicholson et al. 2005).

A critical issue in the application of metabolomics is the standardization of methods, data analysis, and reporting across laboratories. A recent cooperative study by the Consortium for Metabonomic Toxicology indicated that NMR-based technology is robust and reproducible in laboratories that follow similar analytical protocols (Lindon et al. 2003). Investigators in the field recently have agreed on consensus standards for analytical standardization and data representation in metabonomic analyses (Lindon et al. 2005a).