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Research

Quantitative Proteome Analysis of Human Plasma following in Vivo Lipopolysaccharide Administration Using ¹⁶O/¹⁸O Labeling and the Accurate Mass and Time Tag Approach^*,S

Wei-Jun Qian, Matthew E. Monroe, Tao Liu, Jon M. Jacobs, Gordon A. Anderson, Yufeng Shen, Ronald J. Moore, David J. Anderson, Rui Zhang, Steve E. Calvano, Stephen F. Lowry, Wenzhong Xiao^¶, Lyle L. Moldawer^||, Ronald W. Davis^¶, Ronald G. Tompkins^**, David G. Camp, II, Richard D. Smith^, and the Inflammation and the Host Response to Injury Large Scale Collaborative Research Program

From the Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, the Department of Surgery, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, New Brunswick, New Jersey 08901, the ^¶ Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, California 94304, the ^|| Department of Surgery, University of Florida College of Medicine, Gainesville, Florida 32610, and the ^** Department of Surgery, Shriners Burn Center and Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114

	ABSTRACT

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 
Identification of novel diagnostic or therapeutic biomarkers from human blood plasma would benefit significantly from quantitative measurements of the proteome constituents over a range of physiological conditions. Herein we describe an initial demonstration of proteome-wide quantitative analysis of human plasma. The approach utilizes postdigestion trypsin-catalyzed ¹⁶O/¹⁸O peptide labeling, two-dimensional LC-FTICR mass spectrometry, and the accurate mass and time (AMT) tag strategy to identify and quantify peptides/proteins from complex samples. A peptide accurate mass and LC elution time AMT tag data base was initially generated using MS/MS following extensive multidimensional LC separations to provide the basis for subsequent peptide identifications. The AMT tag data base contains >8,000 putative identified peptides, providing 938 confident plasma protein identifications. The quantitative approach was applied without depletion of high abundance proteins for comparative analyses of plasma samples from an individual prior to and 9 h after lipopolysaccharide (LPS) administration. Accurate quantification of changes in protein abundance was demonstrated by both 1:1 labeling of control plasma and the comparison between the plasma samples following LPS administration. A total of 429 distinct plasma proteins were quantified from the comparative analyses, and the protein abundances for 25 proteins, including several known inflammatory response mediators, were observed to change significantly following LPS administration.

Although the majority of plasma proteome characterization efforts to date have been qualitative or semiquantitative, the discovery of novel biomarkers or signature proteins would benefit significantly from quantitative measurements of the differences in plasma protein concentration from different states (e.g. normal versus diseased states). Recently several laboratories have reported the applicability of using postdigestion ¹⁶O/¹⁸O labeling as a quantitative proteomic approach for analysis of complex samples (9–13). In the work reported herein, we describe a global quantitative proteomic approach and its application for comparative analyses of two human plasma samples obtained from a healthy individual prior to (control) and after lipopolysaccharide (LPS) administration (LPS-treated). A 9-h time point was used in this work only for the initial demonstration of the approach. LPS is a purified bacterial endotoxin known to induce a broad range of inflammatory reactions, including cytokine production, cell migration, and production of acute phase proteins (14–16). One of our objectives was to identify acute phase plasma proteome changes in response to a prototypical inflammatory challenge at different time points (0–24 h) following the LPS administration. Our quantitative proteomic approach combines postdigestion trypsin-catalyzed ¹⁶O/¹⁸O labeling, strong cation exchange fractionation after labeling, and LC-FTICR analyses with the accurate mass and time (AMT) tag strategy (11, 17–19) for peptide identification and quantification. This ¹⁶O/¹⁸O labeling-AMT tag approach was demonstrated to be amenable for high throughput quantitative proteome analyses such as studying the proteomic changes in human plasma following the LPS administration. In a previous initial study, we reported on a qualitative comparison of the two plasma samples following LPS administration based on the number of peptide identifications from LC-MS/MS analyses. Here we demonstrate more accurate detection of proteomic changes following LPS treatment by using a quantitative approach. Several known inflammatory response or acute phase mediators were accurately quantified following the administration of LPS.

	EXPERIMENTAL PROCEDURES

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 
Human Plasma Sample Preparation—
Approval for the conduct of this study was obtained from the Institutional Review Boards of the University of Florida College of Medicine, the Robert Wood Johnson Medical School, the Stanford University School of Medicine, and the Pacific Northwest National Laboratory in accordance with federal regulations.

The human plasma samples were supplied by the Department of Surgery at the University of Florida College of Medicine, which serves as the Sample Collection and Coordination Site for a multicentered clinical study (Inflammation and the Host Response to Injury). The original sample was generated from a healthy adult subject at the Department of Surgery at the Robert Wood Johnson Medical School who, after signed informed consent, received an intravenous injection of Clinical Center Reference Endotoxin (Lot 2) LPS (2 ng/kg of body weight administered over 5 min). Arterial or venous blood was collected at various time points between 0 and 24 h following endotoxin administration. White blood cell counts and various vital signs including body temperature, blood pressure, and heart rate were monitored for the subject throughout the 24-h study period. This subject manifested signs and symptoms consistent with those observed after intravenous endotoxin administration to humans (20). The plasma samples were prepared from whole blood by centrifugation; samples at T = 0 h (control, base line immediately prior to endotoxin administration) and T = 9 h (LPS-treated, 9 h following LPS administration) were used for this study. Another set of reference plasma samples obtained from the Stanford University School of Medicine was also used to generate an initial data base of peptide identifications.

Aliquots of 200 µl each of the control and LPS-treated plasma samples were diluted and denatured using 8 M urea, 50 mM NH₄HCO₃, pH 8.2 for 1 h at 37 °C and reduced with 10 mM DTT for 30 min at 37 °C. Protein cysteinyl residues were alkylated with 40 mM iodoacetamide for 90 min at room temperature, and samples were desalted using a prepacked PD-10 column containing Sephadex G-25 (Amersham Biosciences). The protein concentrations for the desalted samples were measured using a BCA protein assay (Pierce) that gave total protein amounts of 15.0 and 13.9 mg for the control and LPS-treated plasma samples, respectively. The samples were then digested into peptides using sequencing grade trypsin (Promega, Madison, WI) overnight at 37 °C with a 1:50 (w/w) trypsin-to-protein ratio. Tryptic activity of residual trypsin was quenched by boiling the samples for 10 min and immediately placing the samples on ice.

Trypsin-catalyzed ¹⁶O/¹⁸O Labeling—
Trypsin-catalyzed ¹⁶O/¹⁸O labeling was carried out as described previously (11). After residual trypsin activity was quenched via the boiling and quick cooling steps, an aliquot of peptides (1 mg each) was removed from the control and LPS-treated samples, and each aliquot was lyophilized. To dissolve the dried peptides, 40 µl of acetonitrile were first added to the dried digest followed by the addition of 200 µl of 50 mM NH₄HCO₃ in either ¹⁸O-enriched water (95%, Isotec, Miamisburg, OH) or regular ¹⁶O water. Then 2 µl of 1 M CaCl₂ and 10 µl of immobilized trypsin (Applied Biosystems, Foster City, CA) were added to the digests, and the samples were mixed continuously for 24 h at 30 °C. Peptides from the control sample were labeled with ¹⁶O, and peptides from the LPS-treated samples were labeled with ¹⁸O. After labeling, supernatant was collected from each sample after centrifuging the samples for 5 min at 15,000 x g. The corresponding ¹⁶O- and ¹⁸O-labeled samples were pooled, combined, and then lyophilized.

Strong Cation Exchange (SCX) Fractionation—
The ¹⁶O/¹⁸O-labeled peptide samples from the control and LPS-treated plasma samples were fractionated by SCX similar to that described previously (7, 11). The lyophilized sample was resuspended in 1.5 ml of 10 mM ammonium formate, 25% acetonitrile, pH 3.0 and injected onto a 10 x 4.6-mm guard column attached to a polysulfoethyl A 200 x 4.6-mm (5-µm, 300-Å) column (Poly LC, Columbia, MD). The mobile phases consisted of solvent A (10 mM ammonium formate, 25% acetonitrile, pH 3.0) and solvent B (500 mM ammonium formate, 25% acetonitrile, pH 6.8). After sample loading, the separation was isocratic for 10 min with 100% solvent A with a flow rate of 1 ml/min. Peptides were eluted using sequential linear gradients from 100% solvent A to 50% solvent B over 40 min and from 50% solvent B to 100% solvent B over another 10 min. The mobile phase was held at 100% solvent B for another 15 min. 1-ml fractions (1 min/fraction) were collected after the start of the gradient using a Shimadzu FRC-10A fraction collector (Kyoto, Japan) and combined into 30 fractions. Each fraction was lyophilized and analyzed by reversed-phase LC-FTICR.

Reversed-phase Capillary LC-FTICR Analyses—
Peptide samples were analyzed using a fully automated custom built capillary LC system (21) coupled on line using an in-house manufactured ESI interface to an Apex III 9.4-tesla FTICR mass spectrometer (Bruker Daltonics, Billerica, MA). The capillary column was made by slurry packing 3-µm Jupiter C₁₈ bonded particles (Phenomenex, Torrence, CA) into a 65-cm-long, 150-µm-inner diameter fused silica capillary column (Polymicro Technologies, Phoenix, AZ). The mobile phase consisted of 0.2% acetic acid and 0.05% TFA in water (A) and 0.1% TFA in 90% acetonitrile, 10% water (B). Mobile phases were degassed on line using a vacuum degasser (Jones Chromatography Inc., Lakewood, CO). The SCX fractions were dissolved in 50 µl of 25 mM NH₄HCO₃, pH 8.0. 10-µl aliquots from each peptide sample were injected onto the reversed-phase capillary column for either LC-MS/MS or LC-FTICR analysis. The mobile phase was held at 100% A for 20 min followed by a non-linear exponential gradient elution generated by increasing the mobile phase composition to 70% B over 150 min using a stainless steel mixing chamber. The LC-FTICR mass spectrometer was configured and operated as described elsewhere (22).

Generation of a Peptide AMT Tag Data Base—
A data base of identified peptides was created based on the results of extensive LC-MS/MS analyses from multiple sample sources. The application of multidimensional LC-MS/MS analyses for profiling two different sets of plasma samples without depletion of any abundant plasma proteins was the same as described previously (4, 7). Human serum albumin and immunoglobulins were removed from the reference plasma sample obtained from Stanford by using a commercial anti-human serum albumin cartridge followed by a Protein G cartridge (Applied Biosystems, Framingham, MA) according to the manufacturer’s instructions. Both the flow-through following depletion and the eluent were subjected to trypsin digestion, further SCX peptide fractionation, and LC-MS/MS analyses. All LC-MS/MS data sets were analyzed using the SEQUEST algorithm (23) (ThermoElectron, San Jose, CA) for peptide and protein identification by searching the MS/MS spectra against the human International Protein Index data base (consisting of 41,216 protein entries, Version 2.29, April, 2004; available on line at www.ebi.ac.uk/IPI). A static mass modification on cysteinyl residues that corresponded to alkylation with iodoacetamide (57.0215 Da) was applied during the SEQUEST analysis. The results from all data sets were combined and further filtered for the generation of the AMT tag data base and the list of confidently identified peptides.

A set of recently developed filtering criteria (24) was applied to filter the data following SEQUEST analyses to generate a list of confidently identified peptides. These criteria are: for the 1+ charge state, Xcorr 2.0 for fully tryptic peptides and Xcorr 3.0 for partially tryptic peptides; for the 2+ charge state, Xcorr 2.4 for fully tryptic peptides and Xcorr 3.5 for partially tryptic peptides; and for the 3+ charge state, Xcorr 3.7 for fully tryptic peptides and Xcorr 4.5 for partially tryptic peptides; Cn value of 0.1 for all charge states. Two additional Cn cutoff values of 0.05 and 0.15 were applied to reduce false negatives while maintaining the same level of confidence for peptide assignments (24). With the Cn value 0.05, the minimum acceptable Xcorr value was raised to achieve a comparable false positive rate, and similarly, for Cn value 0.15, the minimum acceptable Xcorr value was reduced. In an attempt to remove redundant protein entries in the reported results, the software program ProteinProphet was used as a clustering tool to group similar or related protein entries into a "Protein Group" (25). All peptides that passed the filtering criteria were assigned an identical probability score of 1 and entered into the ProteinProphet program solely for clustering analysis to generate the final non-redundant list of proteins or protein groups.

Peptides that met the same criteria with the exception that Cn 0 were included in the AMT tag data base. The peptide retention times from each LC-MS/MS analysis were normalized to a range of 0–1 using a predictive peptide LC-normalized elution time (NET) model and linear regression as previously reported (26). An average NET value and NET standard deviation were assigned to each identified peptide if the same peptide was observed in multiple runs. Both the calculated accurate monoisotopic mass and NET of the identified peptides were included in the AMT tag data base.

LC-FTICR Data Analysis—
The LC-FTICR data sets were automatically analyzed using in-house software tools that included ICR2LS. The initial analysis of raw LC-FTICR data involved a mass transformation or deisotoping step using ICR2LS, which is based on the THRASH algorithm (27). The ICR2LS analysis generates a text file report for each LC-FTICR data set, and the report includes both the monoisotopic masses and the corresponding intensities for all detected species for each spectrum. Following ICR2LS analysis, data were processed automatically to yield a two-dimensional mass and LC elution time data set. Data processing steps included filtering data, finding features (i.e. a peak with unique mass and elution time) and pairs of features, computing abundance ratios for pairs of features, normalizing LC elution times, and matching the accurate measured masses and NET values of each feature to the corresponding AMT tag in the data base to identify peptide sequences. The peptide sequences of a given feature or pair of features were assigned when the measured mass and NET for each given feature matched the calculated mass and NET of a peptide in the AMT tag data base within a 5-ppm mass error and a 5% NET error.

The abundance ratios (¹⁸O/¹⁶O) for labeled peptide pairs were accurately computed using an equation (Equation 1) similar to that previously reported (28),

(Eq. 1)

where I₀, I₂, and I₄ are the measured intensities for the monoisotopic peak for a peptide without ¹⁸O label, the peak with a mass 2 Da higher than the monoisotopic peak, and the peak with a mass 4 Da higher mass than the monoisotopic peak, respectively. M₀, M₂, and M₄ are the predicted relative abundances for the monoisotopic peak for a peptide, the peak with mass 2 Da higher than the monoisotopic peak, and the peak with mass 4 Da higher than the monoisotopic peak, respectively. The M₂/M₀ and M₄/M₀ ratios are estimated using the following two equations (Equations 2 and 3) according to a recent report (29); M_r represents the peptide molecular weight.

(Eq. 2)

(Eq. 3)

Ratios from multiple observations of the same peptide across different analyses were averaged to give one ratio per peptide. All quantified peptides were rolled up to non-redundant protein groups using ProteinProphet, and the abundance ratio for each protein group was calculated by averaging the ratio of multiple unique peptides stemming from the same protein group.

	RESULTS

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 
The Quantitative Proteomic Strategy—
Postdigestion trypsin-catalyzed ¹⁸O labeling, SCX fractionation, and the AMT tag approach (11, 17–19) were combined to constitute a global quantitative proteomic strategy for analyzing protein abundance changes in human plasma following LPS administration (Fig. 1). A similar strategy has been demonstrated using the recently developed quantitative cysteinyl-peptide enrichment technology (11). The initial stage of generating the peptide AMT tag data base was based on extensive LC separations coupled with MS/MS to increase the overall coverage of the proteome. The calculated masses and NET values for all identified peptides were included in the AMT tag data base, which then served as a "look-up table" for identifying peptides from LC-FTICR analyses. In the second stage, the control and LPS-treated plasma samples were subjected to trypsin digestion separately. Following digestion, the control sample was labeled with ¹⁶O, and the treated sample was labeled with ¹⁸O via the trypsin-catalyzed oxygen exchange reaction. The two labeled samples were then combined for further SCX fractionation to increase the overall dynamic range of the approach. Each SCX fraction was analyzed by LC-FTICR under identical LC conditions, and ¹⁶O/¹⁸O peptide pairs detected by FTICR were directly identified using the AMT tag data base using the mass of the ¹⁶O-labeled peptide. The ¹⁸O/¹⁶O ratio of each observed pair provided a precise measurement of the relative abundance for the two versions of the identified peptide and thus the relative abundances of the peptide in the two samples.

View larger version (27K): [in this window] [in a new window]   FIG. 1. Strategy for quantitative proteome analysis using 16O/18O labeling and the AMT tag approach. The strategy is a two-stage process. In the initial stage, a peptide AMT tag data base for plasma is generated based on extensive analyses of plasma-derived peptide samples using multidimensional LC coupled to tandem mass spectrometry. In the second stage, samples to be compared are separately labeled with 16O and 18O, and then the labeled samples are combined for further SCX fractionation. The fractionated, labeled samples are analyzed by high throughput LC-FTICR, and peptide pairs are identified by matching to the AMT tag data base and quantified using 18O/16O abundance ratios.

Initial Generation of a Peptide AMT Tag Data Base for Plasma—
To generate a peptide AMT tag data base for the human plasma proteome with extensive coverage, we coupled SCX fractionation and capillary LC-MS/MS for the analyses of multiple plasma samples, i.e. the control plasma, LPS-treated plasma, and a reference plasma sample (4, 7). Depletion of human serum albumin and immunoglobulin G was performed prior to the 2D LC-MS/MS analyses to further improve the coverage of low abundance proteins.

When generating a peptide data base to be used for quantitative proteomics, we opted for a conservative approach using only the most confident peptide identifications. The number of peptides and thus proteins identified depends on the stringency of the filtering criteria applied after SEQUEST analyses (4). Our recent study using reversed data base searching (i.e. analyzing the data against a reversed human protein data base with the order of amino acid sequence reversed for each protein and thus containing a large set of "nonsense" peptides) (24) revealed potentially high false positive rates for human plasma/serum peptide identifications based on criteria reported previously (3–7). For example, the false positive rate for peptide identifications from human plasma using the criteria described by Washburn et al. (30) is 30% based upon a reversed data base searching (24). As a result of our previous study, we developed an improved set of filtering criteria for human plasma that provide an 4% false positive rate for peptide identifications (24). Application of these new filtering criteria in our current study resulted in the much more confident identification of a total of 7395 different peptides, covering 938 non-redundant plasma proteins or protein groups. The functional distribution of the 938 proteins is depicted in Fig. 2. Approximately 16% of the identified proteins were classified as "classical" plasma proteins (proteins secreted into plasma via mainly the liver and intestines), which includes circulatory and binding proteins, coagulation and complement factors, proteases and inhibitors, cytokines, and related proteins. Cellular tissue-derived proteins account for almost half of the total, and a high percentage (20%) of proteins were classified as unknown (either hypothetical or having no functional annotation). The presence of many tissue-specific cellular proteins in plasma, presumably from cellular "leakage," further supports the notion that plasma may provide tissue-specific biomarkers; e.g. cancer biomarkers. This complete list of protein/peptide identifications is provided as Supplemental Table 1. To generate the peptide AMT tag data base, we applied the same filtering criteria, but with a more liberal Cn filter (Cn 0) that allowed peptides with 0 Cn < 0.1 to also be used for peptide identifications when additionally validated by observation at the same NET by FTICR accurate mass measurement. The final AMT tag data base contained 8309 unique peptides with accurately measured normalized elution time and calculated monoisotopic mass for each peptide.

View larger version (33K): [in this window] [in a new window]   FIG. 2. Functional distribution of 938 non-redundant plasma proteins or protein groups (the complete list of identified proteins is available in Supplemental Table 1).

View larger version (15K): [in this window] [in a new window]   FIG. 3. LC-FTICR analysis of 1:1 labeled control plasma samples. A, a partial 2D display of the detected 18O/16O-labeled peptide pairs. The elution time is shown in a normalized scale between 0 and 1. Observed peaks (represented by spots) correspond to various eluting peptides. The heavy and light isotope-labeled pairs are easily visualized with a 4-Da mass difference. B, the ratio distribution of 891 detected peptide pairs.

View larger version (26K): [in this window] [in a new window]   FIG. 4. A, the mass error (left) and NET error (right) distributions of the 3534 identified peptide pairs. For those peptide pairs that were observed across multiple fractions, average mass error and NET error values were used in these plots. B, normalized -fold changes for the 429 quantified proteins following LPS administration. The abundance ratio for each protein shown was normalized to zero (R – 1) (35). For ratios smaller than 1, normalized inverted ratios were calculated as (1 – (1/R)). The error bar for each protein indicates the standard deviation for the abundance ratios from multiple peptides. Proteins without error bars were identified with single peptides.

View larger version (26K): [in this window] [in a new window]   FIG. 5. Mass spectra for selected peptide pairs. A, two different peptide pairs from C-reactive protein. B, three different peptide pairs from von Willebrand factor.

	DISCUSSION

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

There are several advantageous features of the trypsin-catalyzed ¹⁶O/¹⁸O labeling including: 1) the postdigestion labeling methodology incorporates two atoms of ¹⁸O in essentially all tryptic peptides, providing the framework for accurate quantitation; 2) the enzyme-catalyzed approach can be applied to label tryptic peptide samples from various biological sources, including tissues, cell lysates, and biological fluids; and 3) the ¹⁶O/¹⁸O labeling can be easily coupled to peptide-specific enrichment methods such as cysteinyl peptide enrichment (11) or to peptide fractionation techniques such as SCX to improve overall proteome coverage of the analysis. However, one of the concerns related to ¹⁸O labeling is that the ¹⁸O-labeled peptides may exchange back to ¹⁶O, albeit at a low rate of exchange, that will potentially decrease the overall efficiency of the stable isotope labeling and quantitation. Instead of using cysteine alkylation of trypsin under denaturing conditions as suggested by a previous report (13), we found that the residual trypsin activity following digestion can be effectively quenched by boiling the samples for 10 min and then immediately placing the samples on ice. In the postdigestion labeling methodology, the use of immobilized trypsin allows the enzyme to be completely removed following the labeling step. Using this protocol we have not observed any evidence of oxygen back-exchange even after the labeled samples were stored for several months in normal water (data not shown).

We have presented an initial demonstration of the comparative analysis of human plasma without depletion of any major proteins, quantifying a total of 429 non-redundant plasma proteins from clinical human plasma samples. The dynamic range of concentration for proteins present in human plasma is expected to be >10¹⁰; this presents a significant challenge for detecting low abundance proteins. Although the results from this study demonstrate the effectiveness of identifying proteomic changes between different clinical plasma samples, the limitation in overall dynamic range of detection has resulted in the quantification of proteins that are primarily considered as having medium to high abundance levels. Thus, the number of proteins observed with significant changes in concentration in plasma is limited by the coverage or low signal levels of lower abundance proteins due to the presence of a limited set of very high abundance proteins. To improve the overall dynamic range of detection, new depletion strategies for removing high abundance proteins (2) and novel enrichment methods for enriching specific subsets of peptides such as cysteine-containing peptides (11, 33) and N-glycosylated peptides (34) are essential and can be applied in combination with the present approach. The application of such depletion or enrichment methodologies is highly promising for extending the present quantitative analysis approach to much larger numbers of low abundance proteins in plasma.

The extension of the reported method to a study of the plasma time-dependent acute phase response following LPS administration is presently in progress and aims to provide an extended dynamic range proteome survey of potential mediators in inflammatory response that may contribute significantly to our understanding of systemic inflammation and sepsis syndrome. With the increased dynamic range of detection resulting from the additional application of depletion and/or enrichment strategies to proteomic samples, we anticipate broad utilization of this quantitative approach in clinical plasma/serum proteomics.

	ACKNOWLEDGMENTS

 
We thank Dr. Joshua N. Adkins for critical review of the manuscript. The Clinical Center Reference Endotoxin was kindly supplied by Anthony Suffredini, M.D., National Institutes of Health Clinical Center, Bethesda, MD.

	FOOTNOTES

 
Received, February 15, 2005, and in revised form, March 1, 2005.

Published, MCP Papers in Press, March 7, 2005, DOI 10.1074/mcp.M500045-MCP200

¹ The abbreviations used are: 2D, two-dimensional; LPS, lipopolysaccharide; SCX, strong cation exchange; NET, normalized elution time; AMT, accurate mass and time.

^* Portions of this work were supported by the NIGMS, National Institutes of Health Large Scale Collaborative Research Grant U54 GM-62119-02), National Institutes of Health National Center for Research Resources Grant RR18522, and the Environmental Molecular Science Laboratory (a national scientific user facility sponsored by the United States Department of Energy (DOE) Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL)). PNNL is operated by Battelle Memorial Institute for the DOE under contract DE-AC05-76RLO-1830. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

^S The on-line version of this manuscript (available at http://www.mcponline.org) contains supplemental material.

Additional participating investigators in the Large Scale Collaborative Research Program entitled, "Inflammation and the Host Response to Injury:" Drs. Henry V. Baker, Paul Bankey, Timothy R. Billiar, Bernard H. Brownstein, Irshad H. Chaudry, J. Perren Cobb, Adrian Fay, Robert J. Feezor, Brad Freeman, Richard L. Gamelli, Nicole S. Gibran, Brian G. Harbrecht, Doug Hayden, David N. Herndon, Jureta W. Horton, John Lee Hunt, Jeffrey L. Johnson, Krzysztof Laudanski, James A. Lederer, Tanya Logvinenko, Ronald V. Maier, John A. Mannick, Bruce McKinley, Carol L. Miller-Graziano, Joseph P. Minei, Michael Mindrinos, Ernest E. Moore, Fredrick A. Moore, Avery B. Nathens, Grant E. O’Keefe, Laurence G. Rahme, Daniel G. Remick, Jr., David Schoenfeld, Michael B. Shapiro, Robert L. Sheridan, Geoffrey M. Silver, Scott Somers, Mehmet Toner, H. Shaw Warren, Michael A. West, Steven E. Wolf, Martin Yarmush, and Vernon R. Young.

To whom correspondence should be addressed: Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, P. O. Box 999, MSIN: K8-98, Richland, WA 99352

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