Completion of a human genome sequence will change biology and medicine in many ways (
1,
2). Most fundamentally, the information in a well-annotated genome sequence represents a catalog of all possible gene products in human cells. Thus, analyses of gene expression provide important information about the states of cells and about their responses to chemicals and other environmental stimuli. Genes ultimately code for proteins, which perform most of the functions of cells. The
proteome is the protein complement of the expressed genes in a cell. Whereas the genome of an organism is essentially invariant in all its cells, proteomes vary among different cell types and also vary with time. This is because not all genes in an organism are expressed in all its cells and levels of gene expression vary with time. Moreover, any protein product of a single gene may exist in multiple forms because of posttranslational modifications, the existence of mutants, and the formation of splice variants. Indeed, it appears that many cellular proteins may exist in at least two forms at any given moment. Thus, the complexity of the proteome presents challenges not seen in gene expression analyses.
Mass spectrometry (MS) is the key technology presently driving proteomics (
3,
4). MS analysis of peptides generated by proteolysis of protein samples provides information sufficient to identify the proteins. I present an overview of analytical proteomics methodology in Figure 1. Protein mixtures (cell extracts, subcellular fractions, protein complexes, etc.) are first subjected to some type of separation to resolve the mixture into several fractions containing fewer components. The prototypical approach employs two-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis (2D SDS-PAGE), but many other protein separation methods can be used. The proteins in the separated fractions then are digested chemically or enzymatically to produce a mixture of peptides. The peptides then are analyzed by MS either with or without some prior chromatographic or electrophoretic separation. The MS data are then evaluated with the aid of database search algorithms, which correlate MS data with protein or nucleotide sequences, thus identifying the proteins represented in the mixture of peptides.
 |
Figure 1. General scheme for proteomic analysis.
|
Table 1 is a summary of the features of the two principal approaches to MS analysis of peptide mixtures. Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) MS employs laser energy to generate peptide ions from co-crystallized mixtures of peptides and ultraviolet-absorbing organic acids. MALDI-TOF analyses yield mass measurements of peptides, which can then be searched against databases with computer-assisted search algorithms to identify the proteins from which the peptides were derived. This technique, termed peptide mass fingerprinting, is a highly robust, useful means of rapidly identifying proteins, particularly in organisms for which completed genome sequences or extensive protein sequence databases are available (4,5). However, this approach becomes increasingly difficult in higher organisms for which reliably annotated genome and protein sequences are unavailable. Moreover, the increasing predominance of gene paralogs (highly homologous genes) in higher organisms (6) makes it more difficult to unambiguously identify proteins by this technique (7).
Electrospray ionization-tandem mass spectrometry (ESI-MS-MS) has emerged as the second major MS technique to analytical proteomics (3). In ESI-MS-MS, peptides are ionized under high voltage at atmospheric pressure and then analyzed with either ion trap, triple quadrupole, or quadrupole-time-of-flight mass analyzers. In these analyses, the peptide ions are subjected to collision-induced dissociation, which generates fragment ions that are detected by the tandem mass analyzer. These fragmentation patterns are recorded in the resulting MS-MS spectra, which provide fingerprints that define the sequence of peptide ions. The development of the Sequest algorithm and software by Yates and Eng made possible the identification of peptides from correlation of their MS-MS spectra with virtual MS-MS data generated from protein and nucleotide sequence databases (8). Automated liquid chromatography-tandem mass spectrometry (LC-MS-MS; "data-dependent") scanning acquires MS-MS spectra for hundreds to thousands of peptides in a single LC analysis. The combination of data-dependent scanning and Sequest automates the identification of proteins from LC-MS-MS analysis of complex peptide mixtures (9). Combination of MS-MS with reverse-phase or tandem LC (e.g., ion exchange-reverse phase) makes LC-MS-MS the most powerful approach to protein identification and characterization (10). Moreover, fragmentation of peptides in MS-MS provides unambiguous confirmation not only of sequence but also of the location and nature of posttranslational modifications, xenobiotic adducts, and sequence variants.
In the 1940s, the Millers first discovered that chemical carcinogens underwent biotransformation to metabolites that covalently bound tissue macromolecules (
11). The subsequent discoveries that binding to DNA led to molecular lesions, mutations, and cancer established a paradigm that has dominated the study of chemical carcinogenesis ever since. Nevertheless, the bulk of reactive intermediates formed in cells bind to cellular proteins. The significance of protein binding went largely unappreciated until the work of Brodie, Gillette, Jollow, Mitchell, and colleagues in the early 1970s established that protein covalent binding of acetaminophen and bromobenzene was closely associated with toxicity and suggested a causative role for protein adduction in cell injury (
12-
15). In the years since, a large body of data has emerged to support this view, yet the relationship between protein covalent binding and toxicity certainly is far from simple. In some cases, nontoxic analogs of toxic chemicals also covalently bind to proteins (
16-
18); in others, toxicity can be modulated without affecting covalent binding (
19). From these data has emerged the view that adduction of some protein targets is critical to injury, whereas adduction of others is not (
20-
22).
Several nucleophilic residues in proteins are potential targets for reactive electrophiles. In general, the most reactive protein nucleophiles are cysteine thiols, lysine 
-amines, histidine imidazoles, and protein N-terminal amines. Somewhat less nucleophilic sites that nonetheless may be targets for highly reactive electrophiles include methionine sulfur, tyrosine phenols, arginine guanidinium, serine and threonine hydroxyls, and possibly even aspartate and glutamate carboxyls (23).
Attempts to identify protein targets of reactive chemicals have been complicated by the same difficulties that have plagued the field of protein analysis. Protein structures are complex, and proteins vary greatly in their molecular sizes and physical properties. Specific detection of adducted species is difficult because unmodified proteins are always present in excess and because of the multiplicity of protein targets for most electrophiles. Lance Pohl and his colleagues first pioneered the use of antibodies in the early 1980s to detect adducts in hepatic proteins from humans and animals treated with the volatile anesthetic halothane (24). Approximately a half-dozen hepatic proteins adducted by the halothane metabolite trifluoroacetyl chloride have been detected and subsequently identified by conventional purification methods and Edman sequencing (20). The identified targets are abundantly expressed proteins of the endoplasmic reticulum, close to the site of formation of the extremely reactive acyl halide metabolite.
Other work by the Hinson and Cohen groups over the past decade has employed antibody-based approaches to identify hepatic protein adducts of acetaminophen. These groups have collectively identified seven protein targets adducted by the N-acetyl-p-benzoquinoneimine metabolite of acetaminophen (20). These include cytosolic, mitochondrial, and microsomal proteins. Similar antibody-based detection approaches have been employed to detect protein adducts of benzoquinone (from bromobenzene) (25), diclofenac (26), S-(1,1,2,2-tetrafluoroethyl)-l-cysteine (27), and dichloroacetyl chloride (from trichloroethylene) (22). Other recent work by Myers et al. employed densitometric analyses of proteins on 2D SDS-PAGE whose apparent levels were altered by either in vivo treatment with acetaminophen or its nonhepatotoxic regioisomer 3-hydroxyacetanilide (28). However, it was not possible to determine the identities of most of the proteins affected.
Each published protein adduct identification has resulted from months or years of labor-intensive work to prepare antibodies, identify adducts, purify adducted proteins, and then characterize the proteins--all making this some of the most labor-intensive work in mechanistic toxicology. In none of these investigations has the sequence context of specific adducts been unambiguously established. These approaches also are limited by the availability, quality, and specificity of antibodies to adducts. Indeed, the ability of the antibodies to uniformly recognize adducts on different proteins and in different sequence contexts is open to question. Progress in this field has been greatly hindered by the obstacles to identifying specific protein targets of chemicals and in mapping sequence specificity of adduction.
Several studies have applied MS to study the modification of peptides and small proteins by reactive electrophiles. Early work employed fast atom bombardment-tandem mass spectrometry (FAB-MS-MS) to map the sequence locations of adducts formed by
S-(2-chloroacetyl)glutathione,
S-(2-chloroethyl)glutathione, and
S-(
N-methylcarbamoyl)glutathione in short model peptides
in vitro (
29-
31). Further work from the Burlingame laboratory and subsequently from several other groups employed FAB-MS-MS or ESI-MS-MS to map xenobiotic modifications on several model proteins, including bovine serum albumin (BSA), human serum albumin, human hemoglobin, human apolipoprotein B-100, and
Escherichia coli thioredoxin (
32-
38). These investigators demonstrated the feasibility of using MS analysis to pinpoint covalent modifications of proteins on specific amino acid residues within a known protein sequence. Although the ability to map modifications generated
in vitro on known proteins represents an important advance, electrophiles in complex living systems probably modify many proteins whose identities are unknown. This requires the extension of MS-based adduct analyses to proteomes.
Proteomics approaches offer the opportunity to search for adducts in proteomes containing many proteins in both modified and unmodified forms. As noted above, a fundamental problem in such work is that the identities of the protein targets are usually unknown. An innovative approach to this problem was published by Qiu et al., who used MALDI-TOF MS to identify protein targets of the hepatotoxic analgesic drug acetaminophen (
39). Total hepatic proteins from mice treated with [
14C]acetaminophen were separated by 2D SDS-PAGE, and spots containing radiolabel were subjected to in-gel tryptic digestion and MS analysis (Figure 2). Postsource decay of peptide adduct molecular ions was used to determine sequence information for peptides derived from the proteins. The peptide masses and sequence information were used, in conjunction with protein database searching, to identify 25 protein targets, including the six that had been identified previously as targets by immunochemical methods over the past decade. This study was the first to implement a true proteomics approach to identify protein targets of reactive intermediates. Nevertheless, it is important to point out that these analyses did not, strictly speaking, identify adducts. Instead, they identified proteins that were present in two-dimensional gel spots that contained acetaminophen radiolabel. Although it is likely that the spots contained a mixture of adducted and unadducted proteins, adducts were not demonstrated on the proteins identified by sequencing. Another problem with this general approach is that radiolabel is required to select proteins from the two-dimensional gels for MS analysis. Aside from the expense and frequent synthetic difficulties of using radioisotopes, limitations in the specific activity of most useful radiochemicals will tend to bias the observer toward the detection of adducts to high-abundance proteins.
|
Figure 2. Proteomic approach employed by Qiu et al. (32) to identify putative protein targets of [14C]acetaminophen. |
As noted above, the only MS approach that unambiguously allows the assignment of sequence location of modifications in a protein is LC-MS-MS. For LC-MS-MS analysis, the proteins in a sample must first be digested to peptides. Because one does not know which peptides in a mixture bear the modifications, the only logical approach is to collect MS-MS spectra for all the peptides in a sample, both modified and unmodified, and then determine which spectra correspond to modified peptides.
The Sequest algorithm provides a useful means of identifying proteins represented in a peptide digest (see above). However, not all the MS-MS data obtained in LC-MS-MS analyses can be evaluated successfully with Sequest. Although Sequest can take into account some known peptide modifications on specific amino acids when matching spectra to database sequences, large or unexpected modifications or sequence variations not found in databases will yield inaccurate protein identifications. Thus, a data evaluation tool suited to detecting adduct-specific features in MS-MS spectra is needed to selectively identify spectra of modified peptides.
We have developed a novel algorithm and software called SALSA (Scoring Algorithm for Spectral Analysis) (40). The SALSA algorithm evaluates MS-MS spectra for specific, user-defined features, including product ions at specific m/z values, neutral or charged losses from singly or doubly charged precursors, and ion pairs or series (Figure 3). The MS-MS scans containing the specified features are then scored on the basis of the intensities of the detected ion signals. The scores are independent of the concentrations of the analytes in the mixture. Peptide adducts formed with the electrophiles 1,4-benzoquinone (41), 2-chloroacetyl chloride and S-(2-chloroacetyl)glutathione(42), 7,8-dihydroxy-9,10-epoxy-7,8,9,10-tetrahydrobenzo[a]pyrene (43), and dehydromonocrotaline pyrrole (40) all have been shown to display characteristic combinations of product ions, neutral or charged losses, or ion pairs that would enable detection of their MS-MS spectra with SALSA. Table 2 is a summary of characteristic product ions (PI), neutral losses (NL), charged losses (CL), and ion pairs (IP) that appear in MS-MS spectra of peptides bearing endogenous and xenobiotic-derived modifications. This list is not necessarily a complete compilation of such MS-MS features, yet it illustrates the variety of fragmentation characteristics that distinguish posttranslational modifications and xenobiotic adducts. SALSA is particularly applicable to identifying spectra of modified peptides that display multiple specific product ions, neutral losses, charged losses and ion pairs in different combinations (40).
|
Figure 3. MS-MS spectral features detected by the SALSA algorithm. Asterisk (*) indicates a specific production ion. See text for discussion.
|
The SALSA algorithm also can search for MS-MS spectra that display a series of ions spaced by designated m/z values. These series can correspond to b- or y-ion series that are indicative of specific peptide sequences (44). This feature is particularly valuable for finding MS-MS spectra from specific peptide sequences and can distinguish highly similar peptides of the same m/z value that have subtle sequence differences. SALSA is thus suited to the detection of MS-MS spectra displaying either specific posttranslational modifications (e.g., phosphorylation) or sequence variations (e.g., mutants or polymorphic variants).
It is important to emphasize that SALSA scores are determined by several factors, including a) the search strategy used, b) the length of the search motif, c) the number of ions that match the search series, and d) the intensities of the scored ions (44). SALSA scores do not provide an absolute measure of spectral quality or the fidelity of the match between the search motif and the MS-MS spectrum. Thus, the absolute values of SALSA scores are less important than the relative values for ranking MS-MS scans in a data set. A ranking of the MS-MS scans by SALSA score quickly identifies those MS-MS scans originating from the target peptide or its modified or variant forms. We have not attempted to statistically analyze the relationship between SALSA scores, MS-MS spectral characteristics, and peptide sequences. Depending on the search strategy used and features of individual spectra, SALSA may assign relatively high scores to spectra that do not contain the motif of interest. Thus, it is important that the user validate SALSA "hits" by inspection of the indicated spectra.
Recent work in our laboratory indicates that SALSA can be used to detect modified forms of BSA, which we have employed as a model for these studies (
44). These studies analyzed commercially purchased BSA (Sigma, St. Louis, MO USA), which was not subjected to any treatment with oxidants or xenobiotics prior to analysis. Tryptic digests of BSA were analyzed by LC-MS-MS, and the data then were analyzed with Sequest and SALSA. Figure 4 depicts a summary of the data. Sequest analysis of the data identified MS-MS spectra corresponding to 37 BSA tryptic peptides and accounting for 66.2% coverage by amino acid sequence. A SALSA analysis of the same data file was performed with ion series searches corresponding to the central sequence of each peptide. The SALSA analysis assigned significant scores to the same MS-MS spectra assigned to the peptides by Sequest. In Figure 4, the highlighted sequences indicate peptides to which MS-MS spectra were assigned by Sequest and SALSA. The first number in parentheses below each highlighted peptide corresponds to the number of MS-MS scans assigned to the sequence by Sequest; the second number corresponds to the number of MS-MS spectra assigned to the sequence by SALSA, and the third number indicates the number of modified or variant forms of that peptide sequence assigned to MS-MS spectra by SALSA. The data summary indicates that SALSA detected the MS-MS spectra for unmodified peptides also detected by Sequest. However, SALSA detected MS-MS spectra of several variant peptides not assigned by Sequest. All detected MS-MS scans assigned as variants of a target sequence displayed very strong y-ion series identity or homology (i.e., the series was displaced along the
m/z axis) with the unmodified peptides. As indicated in Figure 4, one MS-MS scan corresponding to a variant peptide was found for eight of the peptides detected. Two variants were found for another peptide (KVPQVSTPTLVEVSR), three variants were found for two other peptides (LFTFHADICTLPDTEK and TVMENFVAFVDK), and four variants each were found for three other peptides (MPCTEDYLSLILNR, CCTESLVNR, and CCAADDKEACFAVEGPK). Inspection of the MS-MS scans corresponding to variant forms of the peptides MPCTEDYLSLILNR and CCAADDKEACFAVEGPK indicated that they were due primarily to M+16 and M+32 variants, reflecting oxidative modification at the cysteine and cysteine/methionine, respectively.
|
Figure 4. BSA tryptic peptides for which MS-MS scans were detected by Sequest and SALSA. Detected peptides are indicated by highlighted sequences. Numbers in parentheses in the format (a,b,c) beneath each highlighted sequence indicate a) the number of MS-MS scans for the indicated sequence detected by Sequest, b) the number of MS-MS scans for the indicated sequence detected by SALSA, and c) the number of MS-MS scans for variants of the indicated sequence detected by SALSA. Reproduced from Mason and Liebler (41) with permission of the American Chemical Society. |
An important advantage of employing LC-MS-MS and SALSA as described in the example above is that modifications to a protein can be detected without prior knowledge of the exact chemical nature of the modifying species. In previous studies of chemical modification of proteins (see above), the investigators knew the chemical nature of the modifying species and thus could directly search the MS data for MS-MS spectra of known peptides modified by chemicals of known mass. However, unanticipated modifications would not be detectable by this approach. Moreover, some modifications by known electrophiles may yield adducts that undergo adventitious oxidation, hydrolysis, or other modifications during sample work-up. These unanticipated modifications would yield adducted peptides that would not be identified by directly searching the data for peptides of the expected mass modification. The advantage of SALSA in these situations is that it can identify MS-MS spectra for variant and modified peptides even when the exact nature of the modification is not known. This is because SALSA searching for spectra with ion series motifs will detect ion series patterns, which are at least partially conserved in spectra of modified peptide forms. Application of this approach to mapping chemical adducts on proteins is ongoing in our laboratory.
For analysis of modifications in complex proteomes, Sequest and SALSA will likely be used together to identify proteins in a sample and to map the sites of modifications. The general approach is illustrated in Figure 5. An initial analysis of the data with Sequest would successfully correlate many of the MS-MS spectra with database sequences. Even if some of the MS-MS spectra were of modified peptides and thus were not correctly identified by Sequest, other unmodified peptides from those same proteins would be identified. This initial Sequest search thus generates a list of proteins represented in the sample. Next, SALSA searches are done for the sequence motifs represented by peptides from those proteins. These sequence motif searches identify not only the MS-MS spectra of the unmodified peptides but also the MS-MS scans that have ion series homology to the unmodified peptide spectra. These spectra correspond to the modified peptides. Inspection of these spectra will then allow deduction of the mass and sequence location of each modification.
|
Figure 5. Integrated application of LC-MS-MS, Sequest, and SALSA to identify and map protein adducts. See text for discussion. |
It is important to emphasize that this approach is entirely dependent on the analyst's ability to obtain MS-MS spectra of the modified forms of peptides. This is not a trivial point, because modified forms may often represent a small fraction of any particular protein. The analytical challenge then becomes obtaining MS-MS spectra of the adducted peptides in the presence of larger amounts of unmodified peptides. Adduct detection becomes even more difficult when the target protein(s) is of low abundance in mixtures containing higher abundance proteins. This is a particularly relevant issue when one considers that protein abundances may vary over about six orders of magnitude. Despite these challenges, the application of multidimensional chromatographic separations prior to or parallel with MS-MS analyses has proven highly effective for analysis of complex proteomes (10,45). Elaboration of similar approaches will help maximize the opportunity to record MS-MS spectra of lower abundance peptide adducts in complex mixtures.
As MS and related proteomics approaches continue to evolve, more investigators will be able to identify protein adducts arising from exposure to environmental chemicals. Protein adducts may serve as markers of exposure to environmental agents. This idea is certainly not new, and a great deal of work has gone into developing methods and applications of hemoglobin and albumin adducts as markers of exposure to reactive chemicals [for reviews, see (
46-
48)]. Previous work on protein biomarkers has involved identifying specific, known chemical adducts on known protein targets and then developing sensitive, specific assays to detect those particular adducts.
Adaptation of proteomic approaches could lead to the discovery of new adduct biomarkers. For example, using LC-MS-MS coupled with sequence motif searching with the SALSA algorithm can identify multiple adduct variants of a target peptide based on ion series homology between the MS-MS spectra of the unadducted and different adducted peptides. We have recently demonstrated that this approach can detect multiple adducts of human hemoglobin exposed to mixtures of aliphatic epoxides in vitro (49). Once adducts are identified by proteomic approaches, LC-MS-MS or immunoassays may be developed to achieve quantification in samples from study populations.
Adduction of proteins may trigger deleterious effects in living systems. This is a long-standing hypothesis that has nevertheless gone largely unexplored because of limitations in analytical technology. New techniques that identify protein targets and that map protein modifications will undoubtedly reinvigorate this field. This will permit investigators to investigate several hypotheses regarding the mechanism(s) by which covalent modifications impact cellular functions. Certainly the dominant hypothesis over the years is that adduction results in an inhibition or loss of function of the adducted proteins (
12,
20,
50). However, recent work suggests that oxidative or alkylation modifications to proteins trigger signaling cascades that result in activation of stress genes and phenotypic changes. Work by the Tew and Ronai groups indicates that glutathione
S-transferase P1-1 (GSTP1-1) serves as a negative regulator of jun-N-terminal kinase 1 (JNK1) through complexation that sequesters JNK1 in an inactive form (
51-54). Prooxidant stress or ultraviolet irradiation leads to dissociation of GSTP1-1 from JNK1 and to JNK1 activation (
54). Similar studies indicate that the redox proteins thioredoxin (
55) and glutathione
S-transferase M1-1 (
56) serve as negative regulators of apoptosis signal-regulating kinase 1 (ASK1) through a similar mechanism, in which prooxidant stress results in thioredoxin or glutathione
S-transferase dissociation and ASK1 activation. Similarly, work by Yamamoto and colleagues revealed that the protein keap1 serves as a redox- and alkylation-sensitive switch for activation of the transcription factor NRF2, which is a principal activator of the electrophile response element. Modification of keap1 causes dissociation from NRF2, thus permitting NRF2 entry into the nucleus, complexation with other proteins, and activation of the electrophile response element (
57). Studies by Stevens and colleagues have documented the ability of the prototypical alkylating agent iodoacetamide to induce the synthesis of endoplasmic reticulum (ER)-associated stress proteins, including Grp78, Grp94, and calreticulin, and the cytoplasmic stress protein Hsp70 (
58-60). Other studies have provided evidence that ER proteins are prominent targets for reactive electrophiles produced from P450 enzymes, although no adducts have been mapped to specific proteins (
20). The ER stress response involves activation of a complex signaling system with "sensory" components in the ER that respond to unfolded or reduced proteins and transduce signals that result in the activation of stress gene transcriptional regulators (
61). Although the mechanics of the signaling mechanisms have been elucidated in yeast and appear to be similar in higher eukaryotes, the fundamental mechanisms by which ER protein modifications trigger the cascade are obscure. This situation typifies current understanding of other stress responses. Many of the intermediate signaling events are becoming clear, but the chemical/molecular initiating events remain unexplained except in the most general terms (e.g., "protein damages" "protein oxidation," etc.). Application of analytical proteomics approaches offers new opportunities to explicitly examine the roles of specific protein adducts as triggers for gene expression changes associated with stress.
The revolution in biology spawned by completion of genome sequences for humans and other organisms offers tremendous opportunities to explore mechanisms by which environmental agents affect living systems. Proteomics approaches and gene expression technologies will fuel the emerging discipline of toxicogenomics, which describes the deleterious effects of chemicals on the expression of genes and the functions of gene products. Analytical proteomics approaches employing MS instrumentation and new data analysis tools will enable us to study the interactions of reactive chemicals with cellular proteins and proteomes. A proteomics focus is particularly relevant to studies of environmental agents, because proteins are often the initial point of contact with an organism. Moreover, effects of chemicals on proteins trigger diverse responses that contribute to injury and disease. Proteomics approaches will soon describe these mechanisms and provide a new basis for understanding cell-environment interactions.
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Last Updated: January 22, 2002
