To discover serum biomarkers associated with liver toxicity, SELDI based proteomic analysis was performed on serum samples obtained from mice treated with APAP. Fourteen male C57/BL mice were treated with APAP at 300 mg/kg IP. Serum samples were obtained at 0.5 hr (6 mice), 4.0 hr (4 mice), and 24.0 hr (4 mice) after dosing. Control serum samples were obtained from 4 mice that received saline injections IP. All mice were obtained from the Jackson Laboratory (Bar Harbor, Maine, USA). Biochemical assessment of toxicity was measured through assays for aspartate transaminase (AST) and alanine transaminase (ALT) using commercially available kits (Sigma).

Both non-fractionated and fractionated serum samples were analyzed using the CM10 weak cation exchange ProteinChip arrays (Ciphergen Biosystems, Inc., Fremont, CA) suggested by (Fung et al., 2000). Fractionated samples were subjected to anion exchange chromatography with stepwise pH elution into six fractions. Briefly, serum (20 μl) was centrifuged for 10 minutes and then mixed with 30 μl chaotropic U9 denaturing and solubilization buffer (9 M urea, 2% CHAPS, 50 μM Tris-HCL, pH 9) for 30 minutes. Sample fractionation was performed on a Q HyperD F resin plate (180 μl resin) (Ciphergen, Fremont, CA). The plate was prewashed and equilibrated with U1 solution (1 M urea, 0.2% CHAPS, 50 μM Tris-HCL, pH 9) prior to the addition of samples to the 96 well fractionation plate. The anion exchange fractionation included the following elution steps: (1) 50 μM Tris-HCL, 0.1% OGP (a nonionic detergent, b- D-glucopyranoside), pH 9; (2) 50 μM HEPES, 0.1% OGP, pH 7; (3) 100 μM Na Acetate, 0.1% OGP, pH 5; (4) 100 μM Na Acetate, 0.1% OGP, pH 4; (5) 50 μM Na Citrate, 0.1% OGP, pH 3; and (6) 33.3% isopropanol, 16.7% acetonitrile, 0.1% TFA (trifluoroacetic acid). Each fractionated serum sample, as well as the non-fractionated serum, was spotted in duplicate on CM10 chips. A matrix of sinapinic acid (SPA) was added to each spot, and the chips were read at two appropriate laser energies to permit ionization and visualization of proteins below 20kD and those above 20kD, respectively.

Previously we have shown that the data examined in the present study were of high quality and did not show significant systematic variability between plates, chips or spots (Hong et al., 2005). The raw SELDI mass spectra were pre-processed prior to subsequent analysis of the expression profiles. Normalization by Total Ion Current (TIC) was applied to all spectra to minimize the variability in spectra obtained at different times using Ciphergen ProteinChip 3.2 software (Ciphergen Biosystems, Inc., Fremont, CA). The normalization process averaged the intensity used for all the spots, and adjusted the intensity scales so that all spectra could be displayed on the same scale. Baseline subtraction was conducted prior to normalization, as recommended by Ciphergen. Baseline subtraction offsets in the spectra were the result of both electrical noise and noise from the matrix (SPA). The lowest spectral amplitude was identified and was then used to correct the peak height and area. The Biomarker Wizard software application from Ciphergen was used to automatically detect the peaks present in all of the spectra. The spectral region from 0 to 2500 Da is unreliable for both normalization and peak detection due to matrix interference and was therefore not included in the analysis. A signal to noise ratio greater than 5 was used for the first pass selection of peaks, and a signal to noise ratio greater than 3 was used for the second pass. A cluster mass window of 0.3% was used. The presence of cluster sets in a minimum of 5% of samples was set because different protein expressions were expected in samples obtained from mice treated with APAP for varying time courses. Estimated peaks were added into the final profiles by the Biomarker Wizard application.

Statistics were performed on groups of profiles for identification of potential biomarker proteins. Comparisons were examined between control mice and treated mice (at all time points) using the Biomarker Wizard software application. Intensity differences among the various groups of samples were calculated for all peaks (m/z positions). A peak was considered to have potential discriminatory power if statistically significant differences in its intensities were observed in all analyses (P < 0.01). Principal component analysis (PCA) is widely employed in signal processing, statistics and neural computing to examine the maximum variability in highly dimensional data. Therefore, all possible combinations of peaks having potential discriminatory power in each comparison were tested by PCA using Spotfire DecisionSite 7.1 software (Somerville, MA, www.spotfire.com) to find a subset of protein peaks (potential biomarkers) best able to differentiate groups of samples.

Statistical analysis was performed on all peaks for groups of profiles for identification of biomarker proteins. The p values were generated with nonparametric tests from the Biomarker Wizard software application. Peaks with p values less than 0.01 were selected as the candidates for further analysis for identification of biomarkers. All possible combinations of the candidate peaks in each comparison group were analyzed using PCA to identify a subset of peaks having the most discriminatory power for the coarmpison.

Using SELDI-TOF mass spectroscopy proteomics technology (Ciphergen Biosystems), potential liver toxicity biomarker proteins differentially expressed in serum from mice at different time points after treatment with APAP were identified. A representative spectral view of specific candidate liver toxicity markers is shown in Fig. 1. The PCA results are listed in Table 1.

When comparing differences in the proteome between mice with AST levels less than 400 and mice with AST levels greater than 400, the peaks with m/z values of 3333.1, 6722.4, and 6674.7 in the SELDI spectra of fraction 1 were able to differentiate the two groups. No biomarker proteins (peaks in spectra) from other fractions or from the nonfractionated samples were found to differentiate the two groups of mice.

A unique set of biomarker proteins that could differentiate each of the treatment groups from the controls could not be found, either in the fractionated samples, or in the non-fractionated samples. Fraction 5 revealed peaks with m/z values of 2561.2, 4962.5, and 2083.4 that were identified as biomarkers to differentiate control mice from APAP treated mice.

Spectra from the non-fractionated serum with peak m/z values of 18719.6, 8750.4, and 4240.9 were found to differentiate the control plus 0.5 hr treatment groups from the 4 hr plus 24 hr treatment groups. Spectra from fractions 1, 2, and 3, with peak m/z values of 6722.4 and 3333.1, 4320.8 and 3787.5, and 6285.8, 6301.3, 3218.7, and 3786.5 respectively, were used to differentiate the two groups (control plus 0.5 hr versus 4 hr plus 24 hr). Differentiation of the two groups was most clearly seen in fraction 1.

The group of mice composed of the controls plus 0.5 hr and 4 hr after APAP exposure could be distinguished from the group of mice 24 hr after APAP exposure using either the spectra of (1) the non-fractionated samples (m/z values of peaks: 9105.0, 7957.7, 26349.7), (2) fraction 1 (m/z values of peaks: 9109.1, 3490.6), (3) fraction 2 (m/z values of peaks: 6465.3, 4320.8, 11683.4,), (4) fraction 4 (m/z values of peaks: 4316.7, 8963.6), and (5) fraction 5 (m/z values of peaks: 4244.4, 4268.6, 4601.6). The best separation of the groups was obtained by using the fraction 5 spectral peaks.

Only the peaks with m/z values of 4320.8, 3787.5, and 6870.7 in the spectra of fraction 2 were identified as able to separate the controls plus 0.5 hr treatment group from the 4 hr plus 24 hr treatment group.

Peaks with m/z values of 3333.1, 6722.4, 6674.7, and 11900.3 in the spectra of fraction 1, 3787.5, 4320.8, and 8608.6 of fraction 2, and 6285.8, 3218.7, 6301.3, 12170.4, 3786.5, 4095.0, and 4320.0 of fraction 3 were able to differentiate three groups of mice: the controls plus 0.5 hr treatment group; the 4 hr treatment group; and the 24 hr treatment group. Furthermore, fraction 2 was the best for the differentiation.

To summarize the PCA results: (1) There were no biomarkers (peaks) showing differential expression levels that distinguished all time points following treatment; (2) biomarkers for treatment effect were detected in fraction 5; (3) protein expression patterns for the control plus the 0.5 hr treatment group were very different and could be easily differentiated from the four hr after treatment with APAPgroup; (4) the protein expression pattern was changed even at 4 hr after treatment with APAP; (5) the protein expression pattern at 0.5 hr is not only different from the controls, but also different from the pattern after longer treatment with APAP (4 hr and 24 hr after treatment).

Biochemical measures of toxicity (AST and ALT levels) are shown in Fig 2. As demonstrated, AST and ALT values were significantly increased at 4 and 24 hr after APAP. These findings are consistent with the SELDI analysis. Fig. 3 shows the PCA analysis of the SELDI-TOF mass spectra from fraction 2 using biomarker peaks at m/z values of 3878.5, 4320.8, and 8608.6. As shown in Fig. 3, the control samples and the samples from 0.5 hr treatment group (red points) differed from the 4 hr treatment (blue points) samples and from the 24 hr treatment group (yellow points).

Early toxicity detection is very important for protection. The traditional clinical chemistry measures did not show a statistically significant difference between the samples from 0.5 hr after treatment with APAP and the control samples (Fig. 2), consistent with previous data (James et al., 2005). SELDI analysis identified biomarker proteins after 4 hr treatment with APAP, consistent with the AST and ALT data. In addition, SELDI analysis also identified biomarkers predictive of early toxicity. The SELDI analysis on fraction 2 identified predictive biomarker peaks at m/z values of 2991.3, 6870.7, and 6577.4 that can differentiate early APAP treated samples (0.5 hr) from the control samples (PCA figure shown in Fig. 4).

Fractionation separates highly abundant proteins into a limited number of fractions, reducing signal suppression effects on lower abundance proteins. Moreover, fractionation prior to profiling increases the number of peaks detected and therefore increases the probability of novel biomarkers being identified. To compare the difference between fractionating and not fractionating, and demonstrate the utility of fractionation, all serum samples were assayed with and without fractionation using SELDI. Biomarker peaks diagnostic of liver toxicity (4 and 24 hr treatment groups) as differentiated from the control and 0.5 hr time point were identified from analysis of SELDI spectra of the non-fractionation samples. However, biomarker peaks identified from the analysis of SELDI spectra of the fractionation samples were better able to separate the same groups as shown in Fig. 5. The differences of AST and ALT levels between the group of samples at 4 hr and 24 hr treatment of APAP versus the group of samples from the control and 0.5 hr treatment of APAP are large. Therefore, biomarkers can be identified, independent of whether fractionation is applied. But, the differences of AST and ALT levels between samples at 4 hr after treatment with APAP and samples at 24 hr after treatment with APAP are much smaller (Fig. 2). Biomarkers that could separate the three groups (control plus 0.5 hours, 4 hours, and 24 hours) were not identified from non-fractionation samples analyzed by the SELDI spectra, consistent with the biochemical findings. Even the best subset of peaks was not able to separate the three groups well using non-fractionated samples (Fig. 6A), while fractionation of the same samples successfully separated these three groups of samples (Fig. 3). The levels of AST and ALT could not distinguish 0.5 hr APAP treatment from the controls (Fig. 2). Similarly, the best subset of peaks from the SELDI spectra of non-fractionated samples was unable to separate these samples as shown in Fig. 6B. The fractionation approach, however, resulted in predictive biomarkers that can distinguish the control samples from 0.5 hr treatment with APAP (Fig. 4). These results demonstrated that fractionation permits identification of predictive serum biomarkers that can not be identified without fractionation.