SAP Deficiency Results in a Striking Alteration of the Protein Profile in Activated CD4 T Cells Cris Kamperschroer†, Susan L. Swain†, Thomas Grussenmeyer,‡ and Ivan Lefkovits*,‡ Trudeau Institute, Saranac Lake, New York and University Clinics, Basel Switzerland Received March 3, 2006
Deficiency in a protein called signaling lymphocytic activation molecule-associated protein (SAP) causes X-linked lymphoproliferative disease (XLP) and helper T cell-dependent antibody defects. To identify proteins regulated by SAP, we performed proteomic analyses of SAP deficient vs wild type T cells. Our results reveal protein species whose abundances are profoundly altered by SAP. Our work therefore identifies candidate cellular mediators of SAP-dependent T cell help. Keywords: helper T cell • CD4 T cell • SAP • proteomics
Introduction X-linked lymphoproliferative disease (XLP) is a frequently fatal genetic disorder characterized by (1) excessive proliferation of lymphocytes following Epstein Barr virus infection, (2) increased incidence of Burkitt B cell lymphomas, and (3) low levels of circulating IgG and IgE.1-3 XLP is caused by genetic lesions that disrupt the function of a protein called signaling lymphocytic activation molecule-associated protein,4-6 which is abbreviated SAP.5 The defect in antibody production in XLP patients is also observed in SAP deficient (SAP.KO) mice. SAP.KO mice have reduced basal serum levels of IgG and IgE7,8 and have a profound defect in mounting an efficient IgG response to a wide range of pathogens7-9 and to various model antigens.10,11 However, the mechanism by which SAP promotes antibody responses is unknown. SAP is expressed in CD4 T cells, CD8 T cells, NK cells, NKT cells, and some B cell subsets.1-3 A major function of CD4 T cells is to provide “helper” signals that promote growth and differentiation of B cells. Therefore, SAP may act within either B cells or CD4 T cells to promote antibody responses. Although current evidence conflicts about whether SAP is required in B cells for normal antibody responses,11,12 it has been shown that SAP is required within CD4 T cells for normal antibody responses11,12 and that SAP is critical for T-dependent but not T-independent antibody responses.10 The antibody defect is not due to a loss of or a general inactivation of CD4 T cells because at all stages of the immune response in SAP.KO mice, there are normal numbers of antigen-specific CD4 T cells, and these cells can produce certain cytokines upon restimulation.8,9,12 Together, these findings suggest that SAP performs some function within CD4 helper T cells that allows for proper communication between helper T cells and B cells and thus promotes normal antibody responses. We are interested in * To whom correspondence should be addressed. Department of Research, University Clinics Basel, Vesalianum, Vesalgasse 1, CH-4051 Basel, Switzerland. E-mail:
[email protected]. † Trudeau Institute. ‡ University Clinics. 10.1021/pr0600778 CCC: $33.50
2006 American Chemical Society
understanding what specific molecules controlled by SAP allow for proper helper activity of CD4 T cells. SAP is a cytoplasmic signaling adaptor protein (126 a.a. in mouse and 128 a.a. in humans) that binds via its SH2 domain to several members of the signaling lymphocytic activation molecule (SLAM) family of transmembrane receptors, including CD84, CD229/Ly-9, NTB-A/Ly-108, CD244/2B4, CRACC, and SLAM itself.1-3 In the best studied of these interactions, SAP binds SLAM and also binds to the kinase Fyn, allowing Fyn to phosphorylate tyrosine residues in the cytoplasmic tail of SLAM and initiate signaling.13-15 Some of the components of these signaling pathways are known and a small number of target molecules regulated by SAP, such as IL-4, IL-10, and IL-13, have been identified.8,9,16,17 Despite this, a specific molecule or set of molecules responsible for mediating SAP-dependent T cell help for B cell responses has not been discovered. As an unbiased approach to identify differences in protein expression between SAP.KO and wild type (WT) CD4 T cells, we generated proteomic profiles of WT vs SAP.KO CD4 T cells following activation. We employed an analysis of proteins instead of cDNAs because some molecules involved in CD4 T cell help are regulated posttranscriptionally.18-20 The results of our analyses demonstrate that there are a number of proteins produced in either greater or lesser amounts in activated SAP.KO CD4 T cells than in similarly activated WT CD4 T cells, and in several cases the differences are dramatic. Our analysis therefore reveals a number of proteins regulated by SAP, some of which are likely to be critical for the helper activity of CD4 T cells. Although the proteomic profiles established upon biosynthetic labeling are not suitable for direct identification of the molecular entities by mass spectrometry, a set of candidate spots will be further scrutinized (in nonlabeled samples), and those that are present in adequate abundances will be processed to obtain a structural molecular definition.
Materials and Methods Cultures of Lymphocytes and Metabolic Labeling with [35S]Methionine. Using a FACSVantage DIVA (Beckton Dickinson, Journal of Proteome Research 2006, 5, 1785-1791
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research articles San Jose, CA), CD4+CD62LhighNK1.1- T cells were sorted from spleens and lymph nodes of T cell receptor transgenic OT-II mice or OT-II mice also deficient in SAP (SAP.OT-II). For stimulation, 6 well Costar plates (Fisher Scientific, Pittsburgh, PA) were coated overnight with 5 µg/mL anti-CD3 antibody (eBioscience, San Diego, CA) and 2 µg/mL anti-CD28 antibody (eBioscience) in PBS. Plates were then washed extensively with PBS to remove unbound antibody. Sorted CD4+CD62LhighNK1.1cells were then added to plates at a density of 2.5 × 105 cells/ mL along with 11 ng/mL IL-2 in complete medium consisting of RPMI 1640 (Invitrogen, Carlsbad, CA) containing 10% fetal bovine serum (Hyclone, Logan, UT), 10 mM HEPES (Research Organics, Cleveland, OH), 2 mM L-glutamine (Invitrogen), 100 IU penicillin (Invitrogen), 100 µg/mL streptomycin (Invitrogen), and 50 µM 2-mercaptoethanol (Sigma). Cultures were maintained in a humidified incubator at 37 °C and 5% CO2. Two days after initiation of cultures, cells were removed from antiCD3 and anti-CD28, placed in a new flask, and brought to twice the original volume with complete medium containing 5.5 ng/ mL IL-2. At 65 h (day 3) and 87 h (day 4) after initiation of the cultures, triplicate 1 mL cultures were admixed with 10 µL [35S]Methionine [100 µCi/culture] (Amersham, London, UK) and cultured for 4 h at 37 °C, 5% CO2. The cells were spun down (1000 rpm, 5 min), supernatant was removed, and 60 µL solubilizing buffer containing 2% NP-40 (Sigma), 1% 2-mercaptoethanol and 9 M urea (Merck, Darmstadt, Germany) was added to the pellet. Flow Cytometry. Aliquots of cells from cultures were stained with antibodies specific for CD4, CD44, CD62L, and CD25, or with isotype control antibodies (BD Pharmingen, San Jose, CA) in PBS containing 1% BSA and 0.1% sodium azide (FACS buffer). Cells were washed twice in FACS buffer and data was collected using a FACSCalibur flow cytometer (Beckton Dickinson). Data were analyzed using FlowJo software (Treestar, San Carlos, CA). 2D Protein Separation. The first as well as the second dimension of the 2D gel electrophoresis was performed using the Anderson’s ISODALT system21,22 for the simultaneous analysis of 20 samples (instruments produced at the former Basel Institute for Immunology, Basel). The first dimension, isoelectric focusing (IEF) is based on separation of polypeptides according to charge of the molecules (isoelectric point) using a wide range (pI 3.5-8.5) ampholine carriers (Biorad, Richmond, CA). The second dimension yields a separation of polypeptides according to their molecular mass, covering a range of 10 to 160 kDa. For the first dimension of separation, 20 µL of the solubilized sample was applied. The IEF gel “rod” with the chargeseparated polypeptides (14 000 Vh, 700 V for 20 h) was introduced to the polyacrylamide gel of the second dimension (casted as 10-20% acrylamide gradient) and electric current applied for overnight SDS size separation (constant 140 V). Upon completion of the electrophoretic run, the gels were impregnated with PPO (diphenyloxazol), dried and radiofluorographed [exposed at -70 °C to XAR5 films] for 14 days. Often shorter or longer exposures were prepared, though only one of the exposures was scanned using Molecular Imager FX System (Biorad), applying 100 µm steps at OD range 0-3.0. Image Analysis. (a) Matching. Until recently, we have used the Kepler image analysis system - originally developed by John Taylor of Norman Anderson’s team at the Argonne National Laboratory.22,23 We currently employ the PDQuest system, which is similar to Kepler but it has a more advanced user1786
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friendly interface. The image files are processed for noise and streak removal and background correction, and then converted into spot files by spot modeling and fitting. Upon spot detection, the entire pattern is inspected for those artifacts that were not removed by the software algorithms. Such spots are eliminated or alternatively merged with other spots if appropriate. In the final spot lists, each spot is defined by the x and y coordinates and by the spot volume (a measure of abundance). Then, one of the patterns is chosen as a master pattern, and all other images are compared and matched to the master. Each spot on every analyzed pattern will receive a “master number”, and at the end of the matching process the master pattern will contain all the spots occurring in each of the images. (b) Normalization. Quantitative evaluation and comparison of gel patterns is performed either directly with the image analysis data, or upon “normalization”. The sum of all abundances (of all spots) on each image was calculated, and the ratio of these values was used as the “normalizing multiplication factor” by which all spot intensities on a given image were adjusted. The “goodness” of normalization can be checked by performing scatter plot(s) of replica samples, as mentioned in the next paragraph. (c) Scatter Plot. The scatter plot (on log/log scale) is a convenient way to identify up- and down- regulated gene products. The plot takes into consideration data from two patterns (x-axis for one pattern, y-axis for another one). In the initial screening, we only consider gene products that are more or less abundant by a factor of 2 or more. Two helper diagonals delineate the levels that we consider as meaningful for defining up- and down- regulation. Therefore, dots lying outside the diagonals are candidates for an up- or down- regulated gene product. Since each dot on the scatter plot is “clickable” to inspect the actual spot, it is rigorously used as an indispensable interactive method of proofreading to check for possible “spot mismatches”. The scatter plot offers also the possibility to test on replica samples whether the spots fall within the helper diagonals, which in turn allows to exclude the outlier values, since only mismatches and technical faults should fall to the outside space (see also discussion). (d) Boolean Intersection. PDQuest software has a built-in utility that compares all spots (or a selected subset of spots) for “commonality” or for “selective expression”. Any parameter of the spots (presence, absence, abundance, ratio of abundances) can be subjected to Boolean quest.
Results Phenotype of WT and SAP.KO Effector CD4 T Cells. In the present work, we compare two populations of cells, WT CD4 T cells and SAP.KO CD4 T cells, and we intend to assess the differences in the expression of the protein species detected as spots upon 2D gel electrophoretic separation. To obtain activated populations of WT CD4 T cells and SAP.KO CD4 T cells for comparison, naive CD4 T cells from WT and SAP.KOmice were purified and activated with antiCD3 plus anti-CD28 as described in the Materials and Methods. Prior to biosynthetic labeling, we determined whether the two cell populations were grossly similar. Throughout the culture period, there were no discernible differences between WT and SAP.KO in the general health or numbers of CD4 T cells (data not shown). On days 3 and 4 of culture, cell surface markers were stained to identify transgenic cells and to assess cell activation. Subsequent flow cytometric analysis confirmed that the vast majority of cells by day 4 were indeed transgenic
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Proteomic Analysis of Activated SAP Deficient CD4 T Cells
Table 1. Cell Samples, Gels, Spot Countsa,f samples
Figure 1. Phenotype of WT vs SAP.KO effector CD4 T cells. Figure 1A shows that the vast majority of cells by day 4 express VR2, the VR gene segment utilized by the OT-II transgenic TCR. In Figure 1B, shaded histograms represent staining of the indicated markers on SAP.KO (KO) cells, dark lines represent staining of WT cells, and light gray lines represent isotype control stainings. Day 4 data are shown, while staining of WT and SAP.KO cells was similar at day 3.
no. of spots
gels
cells
day
detectedb
ET9807 ET9808 ET9809 ET9810e
WT SAP.KO WT SAP.KO
3 3 4 4
1140 1031 888 1294
matchedc
percentaged
982 923 752 1294
86.1 89.5 84.7 100
a At 65 h (day 3) and 87 h (day 4) after initiation of the cultures, triplicate 1 mL cultures were labeled with [35S]-Methionine for 4 h as described in Materials & Methods. The cells were then spun down, the pellet was solubilized and the resulting sample submitted to 2D gel electrophoresis using the ISODALT system. Upon completion of the electrophoretic run and further processing of the gels, four representative radiofluorographic images ET9807, ET9808, ET9809, and ET9810 were chosen for evaluation. Controls were included both at the culture and at the labeling; furthermore, at each run of 2D gel electrophoresis, control samples (from known lymphocyte populations) were included both at the isoelectric focusing and size separation. The PDquest image analysis requires that the experimenter chooses on the raw image a faint spot as well as a cluster of high-intensity spots; this constitutes a “subjective” component in the analysis that does influence the outcome of the matching. b Spots detected by the software. c Matched spot upon automatic and hand matching; the value 1294 (line 4) is the same as in the preceding column because the master pattern matches to itself. d Portion of matched spots (given as percentage). e Pattern ET9810 was chosen as a master pattern. f In the table there are only data from gels that are shown in Figures 2-6. These patterns are based on representative gels of triplicate samples.
(Figure 1A) and showed that by 4 days of stimulation, both WT and SAP.KO CD4 T cells upregulated CD44, downregulated CD62L, and upregulated CD25 to similar extents (Figure 1B). Expression of these markers of T cell activation was also similar at day 3 of culture. Although the cells in culture appeared to be healthy and quite large by day 2, robust biosynthetic labeling was achieved only from day 3 onward. Therefore, samples from days 3 and 4 were used for proteomic analysis of WT vs SAP.KO CD4 T cells. The cells were pulsed at these time points with [35S]methionine as detailed in Materials and Methods, and then proteins were solubilized and submitted to proteomic analysis. The four experimental groups are given in Table 1. Generating Proteomic Profiles of WT and SAP.KO Effector CD4 T Cells. Briefly, the radiofluorographs of the mentioned four samples were scanned using the Molecular Imager FX System (Biorad), applying 100 µm steps at OD range 0-3.0, and data were submitted to PDQuest software for image analysis. The starting point of the image analysis is the “raw image” that the software converts (upon removing streaks, background noise and other artifacts) into a filtered image, and then to a “Gaussian model”. The gel patterns chosen for analysis were ET9807, ET9808, ET9809, and ET9810; image ET9810 was chosen as a master pattern to which all other patterns were matched. Several preliminary experiments were performed in order to identify top parameters for obtaining the best quality of cells, optimal labeling conditions and adequate proteomic patterns. The gel patterns reported in this communication were part of a large experiment (as mentioned in Materials and Methods we analyze in the Anderson’ Isodalt system 20 samples simultaneously). Thus, the presented patterns are based on representative gels of triplicate samples. The master pattern is depicted in Figure 2.
Figure 2. Master pattern. Radiofluorography of gel ET9810 revealed the richest pattern from the four groups and this has been chosen as a master pattern. Frame (i) (kDa 68-90, pI 4.75.7) refers to the set of spots that are further detailed in Figure 5. Frame (ii) indicates a region of interest at kDa 15-20, pI 4-8 and is visualized in Figure 6.
Up- and Down-Regulated Protein Species. In Figure 3, a tableau of four scatter plots is displayed to show differences in protein abundance between WT and SAP.KO CD4 T cells at days 3 and 4 of activation. To facilitate the comparison, the plots are flanked by the gel patterns, which are depicted primarily as a point of reference. The scatter plot at the top of the tableau compares the spots from the WT samples with spots of SAP.KO samples on day 3 while the plot at the bottom of the tableau compares the spots from the WT samples with spots Journal of Proteome Research • Vol. 5, No. 7, 2006 1787
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Figure 3. Tableau of four scatter plots with thumbnails of the WT and SAP.KO patterns. Scatter plots at the top and the bottom of the tableau compare the spots from the WT samples with spots of SAP.KO (KO) samples on day 3 (upper panel) and day 4 (bottom panel). Scatter plots at the left and the right of the tableau compare changes within the WT samples on days 3 and 4 (left panel) and within the SAP.KO samples on day 3 and 4 (right panel). Parallel diagonals represent “interpretation lines” for the 2-fold enhanced or diminished expression of the protein species. The numbers near the diagonals indicate the number of spots in that region of the plot. Normalization of data inputs was performed by calculating the sum of all abundances (of all spots) on each image, and establishing the ratio of the resulting values. All spot intensities were adjusted by the normalizing multiplification factor.
of SAP.KO samples on day 4. Scatter plots at the left and the right of the tableau compare changes within the WT samples on days 3 and 4 (left panel) and within the SAP.KO samples on day 3 and 4 (right panel). Although most of the data form a cluster of dots that fall within the 2-fold difference cutoff, there is a portion of dots that fits the category of up- and downregulated protein species. It is worth mentioning here that the dots that fall outside the diagonal lines do not form distinctly separate clusters (i.e., they are part of a “continuum”). It is our experience that the up- and down- regulation is not an all or none phenomenon. Indeed, a continuum is an acceptable model, as will be explained in the discussion. At least in some instances, paradoxically, a “strongly partitioned dot” does not mark an upregulated (or for that matter downregulated) protein, but a mismatched spot set. Of the four scatter plot panels, those comparing WT to SAP.KO at the two culture days (day 3 and day 4) are of primary concern. On day 3, there are 94 and 106 polypeptide species 1788
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that are selectively expressed in WT and SAP.KO populations, respectively. On day 4, there are 127 and 109 species belonging to the mentioned categories. Enhanced and diminished expression is also observed during the progression of cultures from day 3 to day 4. Table 2 summarizes these data and also shows the correlation coefficient of each scatter plot. Replicate samples are expected to have high correlation coefficients [> 0.9] while samples originating from different cell populations (with divergent expression patterns) have, as expected, diminished correlation coefficients. Combining the data, we can address two Boolean questions: (1) which are those spots that refer to protein species selectively expressed in the WT populations at both time points (day 3 and 4) or in the SAP.KO populations at both time points (day 3 and 4), and (2) which spots imply modulated protein expression with time from day 3 to day 4 for both studied populations (WT and SAP.KO)? The Boolean intersect yields 21 spots that are more abundant in the WT population both on
Proteomic Analysis of Activated SAP Deficient CD4 T Cells
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Table 2. Selective Expression of Protein Species in the Analyzed Cell Populationsa,b comparison cell population
WT/KO WT/KO comparison
day
overexpressed in WT
overexpressed in KO
correl. coeff
3 4
94 127
106 109
0.7271 0.6936
overexpressed
overexpressed
correl.coeff
days
cells
on day 3
on day 4
3 vs 4 3 vs 4
WT KO
84 123
115 113
0.7619 0.7166
a Values under the heading overexpressed in WT or KO, and overexpressed on day 3 or day 4 were obtained from the scatter plots given in Figure 3. The values refer to the spots that fall outside of the interpretation diagonals. Note that all four scatter plots have been normalized as detailed in the legend to Figure 3. b The correlation coefficients (range 0.69-0.76) deviate from values obtained when replica samples are compared (as a rule > 0.9); they serve as an indicator that selective expression rather than random divergence is observed.
Figure 5. Area of interest near spot 1715. A portion of the radiofluorography of all four gels (ET9807, ET9808, ET9809, and ET9810) is represented, and it refers to frame (i) (kDa 68-90, pI 4.7-5.7) of Figure 2. The adjacent bar graphs show quantitative alterations of indicated spots in the chosen frame.
Figure 4. Quantitative histogram representation of the selectively expressed protein species. As shown, 21 spots are more abundant in the WT population both on day 3 and day 4 (A), and 16 such spots are more abundant in the SAP.KO population (B); 12 molecular species decrease (C) and 14 species increase (D) in both WT and SAP.KO CD4 T cells in progression from day 3 to day 4. The color coded bars refer to the relative abundance of biosynthetic labeled polypeptides in the detected spot. The manydigit number (with a designation ppm INT*Area indicates the grayness level of the spot, i.e., the absolute abundance).
day 3 and day 4 (Figure 4A) and 16 such spots in the SAP.KO population (Figure 4B). Furthermore, in the progression from day 3 to day 4, there are 12 molecular species that decrease (Figure 4C) and 14 species that increase (Figure 4D) in both WT and SAP.KO CD4 T cells. Among practitioners of proteomics there is a desire to find high abundance spot differences. However, in our analyses, we have chosen to include all spots where we see differences between WT and SAP.KO samples, regardless of whether those spots represent proteins that exist in the cell at high or low
copy numbers. We have done so to avoid overlooking important regulatory proteins that would likely exist in low copy number within the cell. In presenting the results from Figure 4, we have unduly simplified our task; we have taken into consideration only those spots that “behave” in the same way at both time points. However, upon inspection of the data matrix (not shown), one can detect several spots that reflect proteins that are more abundant in SAP.KO cells than in WT cells on day 3, but are of equal abundance on day 4. This is clearly an acceptable result, and it would be rather unwise to disregard such findings (see Discussion). Choice and Selection of Candidate Spots. There are some spots that are strikingly different in the WT and KO gels. These differences are apparent by visual inspection of the radiofluorographs, and are confirmed by quantitative image analysis. In Figure 5 a portion of the gel images (WT and KO) is shown (referring to frame (i) of Figure 2, kDa 68-90, pI 4.7-5.7), and it is apparent that spot 1715 is present at a high abundance in the WT population, while it is absent in the SAP.KO population. This is the case in both the day 3 and day 4 samples. Conversely, spots 710 and 713 are present on the SAP.KO pattern, and missing on the WT pattern, but this is the case only in the day 3 samples, whereas the presence (and also the intensity) is completely reconstituted at day 4. Spot 708 is present in both populations on day 3 and decreases in progression to day 4. A panel of fourteen histograms (including Journal of Proteome Research • Vol. 5, No. 7, 2006 1789
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Figure 6. Area of interest near spot 1103. A portion of the radiofluorography of all four gels (ET9807, ET9808, ET9809 and ET9810) is represented, and it refers to frame (ii) (kDa 15-20, pI 4-8) of Figure 2. Histograms show quantitative alterations in several protein species and two of them (spots 1103 and 5101) are indicated by arrows.
those for the mentioned 1715, 710, 713, and 708) is shown at the bottom margin of Figure 5, indicating the various patterns of spot abundances in this frame. Another area of the analyzed gels that has been scrutinized for differences is at a size range of 15-20 kDa (and pI 4-8), and the protein alterations are given in Figure 6. The chosen region refers to frame (ii) of Figure 2. Spots 1103 and 5101 are marked with an arrow; spot 1103 represents a protein species selectively expressed in WT population both on day 3 and 4, and 5101 absent in KO population on day 3. On the master frame (Figure 5, top) 16 spots are marked, and at the bottom margin of Figure 6, six selected histograms are depicted (see also Discussion). In the reported set of experiments, we have developed a procedure for studying pure populations of WT or SAP deficient CD4 T cells, and we have produced biosynthetically labeled proteomic patterns of these cells. We have identified a large set of protein spots that show an enhanced or diminished expression, suggesting that their expression is regulated by SAP. In experiments using nonlabeled cell samples, we shall attempt to identify the molecular structure of candidate proteins using a mass spectrometry approach.
Discussion Available data suggest that SAP promotes activities of CD4 T cells that allow them to communicate properly with B cells to promote normal B cell function. Although SAP promotes signaling through SLAM family receptors in CD4 T cells,1-3 downstream targets involved in helping B cell responses have not been identified. In this study, we have compared WT CD4 T cells to SAP.KO CD4 T cells in order to identify molecular species of proteins controlled by SAP. Our experimental protocol, especially the use of biosynthetic labeling, allows us to visualize putative structural (high abun1790
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dance) and regulatory (low abundance) proteins. Structural identification by mass spectrometry requires that at least 109 protein molecules are present in the analyzed spot (this corresponds to an abundance of some 104-105 copies per cell).24,25 Regulatory proteins (102-103 copies per cell) can be targeted only by “sub-proteome analysis” for which other experimental protocols and tools are required. Detection of cytokines in cellular preparations remains in most instances an elusive undertaking (I. L., unpublished observation). In our quest to understand the mechanistic basis of SAP deficiency, we shall utilize an “in house” 2D database as well as public databases (e.g., www.expasy.org) to attempt to deduce the identity of some of the altered proteins, and we shall employ mass spectrometry to identify other ones. Although the rationale for using scatter plots is to “segregate” spots according abundance, in the presented set of experiments the cluster of spots does not seem to partition into clearly distinct subsets. There is no clear-cut “border” between the cluster of unaltered polypeptide species and the up- or down regulated ones. We interpret these findings to be due to the fact that we view several variables simultaneously (in the world of “single markers” there is a clear-cut segregation, while each additional marker contributes to fuzzy interrelationships). Despite these characteristics, we consider the selected set as shown in Figure 4 and elsewhere, highly informative. For technical reasons, we have refrained from establishing the variance inherent in the quantitation. This will be performed in context of collecting data for MS analysis. In the results section, we have mentioned our approach to interpreting the presence and absence of the protein species in the studied populations. Here we reiterate that some spots (i.e., spot 713) are selectively upregulated in WT CD4 T cells at day 3 but not at day 4. We speculate that some of these spots represent proteins whose expression is regulated by T cell receptor (TCR) signals. The cells were removed from TCR stimulus on day 2 of culture, so the amounts of proteins induced by TCR signals would be expected to decrease from day 2 to day 4. On the basis of our understanding of how CD4 T cells deliver “helper” signals to B cells, we suspect that the defect in helper activity of SAP.KO CD4 T cells results from altered expression of either cytokines or cell-surface receptors like CD40L, or both. The gel area highlighted in Figure 6 contains differences in spots with predicted molecular masses consistent with those of cytokines (generally
