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Originally published In Press as doi:10.1074/mcp.M600170-MCP200 on July 13, 2006.
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Molecular & Cellular Proteomics 5:1638-1646, 2006.
© 2006 by The American Society for Biochemistry and Molecular Biology, Inc.

Research

Identification of Protein Expression Signatures Associated with Helicobacter pylori Infection and Gastric Adenocarcinoma Using Recombinant Antibody Microarrays*

Peter Ellmark{ddagger}, Johan Ingvarsson{ddagger}, Anders Carlsson{ddagger}, B. Samuel Lundin§, Christer Wingren{ddagger} and Carl A. K. Borrebaeck{ddagger},

From the {ddagger} Department of Immunotechnology, Lund University, BMC D13, SE-22184 Lund, Sweden and § Institute of Biomedicine, Department of Microbiology and Immunology, Göteborg University, 40530 Göteborg, Sweden


   ABSTRACT
TOP
 ABSTRACT
EXPERIMENTAL PROCEDURES
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Antibody microarray based technology is a powerful emerging tool in proteomics, target discovery, and differential analysis. Here, we report the first study where recombinant antibody fragments have been used to construct large scale antibody microarrays, composed of 127 different antibodies against mostly immunoregulatory antigens. The arrays were based on single framework recombinant antibody fragments (SinFabs) designed for high on-chip stability and functionality and were used for the analysis of malignant and normal stomach tissue samples from Helicobacter pylori-positive and -negative patients. Our results demonstrate that distinct tumor- as well as infection-associated protein expression signatures could be identified from these complex tissue proteomes, as well as biomarkers such as IL-9, IL-11, and MCP-4, previously not found in these diseases. In a longer perspective, this study may improve the understanding of H. pylori-induced stomach cancer and lead to development of improved diagnostics.

Antibody microarray-based technology is a promising tool for the field of oncoproteomics, in which one of the most important applications is to compare proteome expression signatures of malignant versus normal samples (for review, see Refs. 1 and 2). This approach has the potential to identify disease-associated biomarkers, which may enable development of improved diagnostics as well as identification of new drug targets (1, 3). It may also further the understanding of the molecular aspects of disease progression in various cancers, such as gastric adenocarcinomas (for review, see Ref. 4), the second leading cause of cancer-related mortality worldwide (5).

Gastric adenocarcinoma is often associated with Helicobacter pylori infection, and 1–2% of all infected individuals subsequently develop stomach cancer (6). The immune response to H. pylori results in regulation of different cytokines, chemokines, and other immunomodulatory molecules and is one of the most important cofactors for tumor progression (7). Consequently, to study the components of the immune response that are associated with the initiation and progression of gastric adenocarcinoma requires analysis of a large number of different molecules, often using only small tissue samples. Antibody microarray-based analysis of relevant immunomodulatory proteins in stomach tissue offers a potential approach to address these basic questions as well as to detect disease-associated protein signatures and biomarkers (1–3).

Antibody microarrays, pioneered by MacBeath and Schreiber (8) and Haab et al.(9), have emerged as a sensitive and high throughput method for detection of proteins and peptides (1, 3). The prospect of using this technology for cancer research was rapidly realized (10–12), and antibody microarrays more recently have been utilized for analysis of larger sets of clinical samples from tissue and serum (13–18). Although the platforms used in these studies have been useful, the content, i.e. commercial off-the-shelf monoclonal and polyclonal antibodies, confer several limitations (19–21). First, different monoclonal antibodies are derived from different germ line genes and therefore have different frameworks. Consequently their on-chip performance will display an unacceptable variation (22), resulting in tedious validation of each new individual antibody. Second, scaling up the arrays would require production of thousands of monoclonal antibodies, which is both time consuming and costly. To overcome these limitations, we have developed antibody fragments by molecular design. These microarray adapted single framework antibody fragments (SinFabs)1 are single chain variable fragments (scFv) obtained from a phage display library (23), where all individual scFv are built around one single, stable framework (VH3–23/VL1–47) (23). These SinFabs have been used to develop an antibody microarray platform (19, 24, 25) suitable for sensitive analysis of complex proteomes, such as serum and tissue extracts.23

In this study, we have constructed an array using 127 different SinFab probes against both low and high abundant proteins, such as cytokines, chemokines, and complement factors. Protein extracts from three different tissue types such as H. pylori-positive gastric adenocarcinoma, H. pylori-positive normal gastric epithelium, and H. pylori-negative normal gastric epithelium has been analyzed. The aim of the study was to validate the platform in oncoproteomics and to demonstrate that specific protein signatures associated with both tumor as well as infection could be identified.


   EXPERIMENTAL PROCEDURES
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Patient Specimens and Protein Extraction from Tissue Samples—
In total, 35 samples from 20 patients (median age 75, range 55–87, 7 female, 13 men) undergoing gastrectomy at Sahlgrenska University Hospital, Göteborg, Sweden, were included in the study. The patients were either suffering from non-cardia gastric adenocarcinoma (GA) (15 patients) or from pancreatic adenocarcinoma (5 patients). The study was approved by the ethical review board at Göteborg University, and informed consent was obtained from each volunteer before participation. None of the patients had undergone radiotherapy or chemotherapy prior to gastrectomy.

The H. pylori-positive gastric adenocarcinoma samples (GA/Hp+) were obtained during surgery, and the H. pylori-positive normal gastric tissue samples (N/Hp+) were obtained as a strip of tumor-free tissue at least 5 cm from the tumor, whereas all H. pylori-negative normal gastric tissue samples (N/Hp–) were obtained from pancreatic cancer patients. The normal gastric tissue samples from both H. pylori-positive and H. pylori-negative retained their normal architecture and showed no sign of tumor cells. Tumor-free mucosa from both antrum and corpus of the same patient was obtained in 3 of 5 H. pylori-negative normal samples and 6 of 15 H. pylori-positive normal samples. However, we found no statistically significant differences in expressed antigens between the antrum and corpus populations (data not shown). From the tissue samples, biopsies with a size of ~10–20 mm2 were cut and stored at –70 °C for subsequent protein extraction. Proteins were extracted from the biopsies by incubating them in PBS containing 2% saponin, 100 µg/ml soybean trypsin inhibitor (Sigma Chemical Co.), 350 µg/ml phenylmethylsulfonyl-fluoride (Sigma Chemical Co.), and 0.1% bovine serum albumin at 4 °C overnight. The samples were then centrifuged (13000 x g for 5 min), and the supernatants were stored at –70 °C until biotin labeled as described below.

Diagnosis of H. pylori Infection—
The H. pylori infection status of the patients was analyzed by bacterial culture on Columbia-Iso plates and by serology. The serological analysis was performed using in-house ELISA methods detecting IgG or IgA antibodies to crude membrane preparations of H. pylori as described previously (28) as well as with a commercial ELISA kit (EIA-G III; Orion Diagnostics). Patients were considered to be H. pylori-positive if they were positive in culture and/or in at least 2 of 3 serology tests and considered to be H. pylori-negative if they were negative in culture and in all three serology tests.

Labeling of Protein Samples from Tissue Extracts—
The extracted protein samples were desalted on a Zeba desalt spin column (Pierce) and eluted in PBS. The protein concentration was determined using a Micro BCATM Protein Assay Reagent Kit (Pierce), and each sample was diluted to 0.5 mg protein/ml in PBS. For normalization purposes, cholera toxin subunit B (Sigma Chemical Co.) was added to all samples at a final concentration of 280 nM. Next, the samples was biotinylated with N-hydroxysulfosuccinimide-biotin (Pierce) by adding N-hydroxysulfosuccinimide-biotin to a final concentration of 0.6 mM for 2 h on ice, with careful vortexing every 20 min. Unreacted biotin was removed by extensive dialysis against PBS at 4 °C, and the samples were then aliquoted and stored at –80 °C prior to use.

Fabrication and Processing of Antibody Microarrays—
127 different antibodies selected from the n-CoDeR library (23) were kindly provided by BioInvent International AB (Lund, Sweden). Briefly, the scFv antibodies were produced in Escherichia coli and purified by affinity chromatography on nickel-nitrilotriacetic acid matrix (Qiagen, Hilden, Germany). Bound molecules were eluted with 250 mM imidazole, extensively dialyzed against PBS, concentrated (average concentration 277 µg/ml), and stored at 4 °C until used. The integrity and purity of the scFv antibodies were confirmed by 10% SDS-PAGE (Invitrogen).

For production of the antibody microarrays, we used a set up previously optimized.23 Briefly, 1 to 30 femtomoles (7 fmol average) of each scFv were spotted in eight replicates using a non-contact Biochip Arrayer1 (PerkinElmer Life Sciences, Inc.). The scFvs were arrayed by spotting 2 drops (333 pL/drop), which were dried in between, in each position onto black polymer MaxiSorp microarray slides (NUNC, Roskilde, Denmark). To assist the alignment of the grid during the subsequent quantification, a row containing Cy5-conjugated streptavidine (2 µg/ml) was spotted for every tenth row. To facilitate chip to chip normalization, a dilution series (23–370 µg/ml) of an anti-cholera toxin antibody (CT17) was spotted on each array. The arrays were blocked with 5% (w/v) fat-free milk powder (Semper AB, Sundbyberg, Sweden) in PBS over night at room temperature. All incubations were conducted in a humidity chamber at room temperature. The arrays were washed four times with 0.05% Tween-20 in PBS (PBS-T) and incubated with 250 µl of the biotinylated sample (0.1 mg/ml) in 1% (w/v) fat-free milk powder and 1% Tween in PBS (sample buffer) for 1 h. Next, the arrays were washed four times with PBS-T and incubated with 1 µg/ml Alexa Fluor 647-conjugated Streptavidin in sample buffer for 1 h. Finally, the arrays were washed four times with PBS-T, dried under a stream of nitrogen, and scanned using the confocal ScanArray Express microarray scanner (PerkinElmer Life Sciences) using four different scanner settings. The ScanArray Express software V2.0 (PerkinElmer Life Sciences) was used to quantitate the intensity of each spot and to compensate for background. The two highest and the two lowest replicates were automatically excluded, and each data point represents the mean value of the remaining four replicates.

Data Analysis—
Background corrected data from the different scanner settings from each sample were scaled to one particular setting, and only data from unsaturated spots were used for further analysis. Chip to chip normalization of the datasets was performed by multiplying the signal data from each sample (i) by a factor NI, which was calculated by n = S(CT17)/µ(CT17). S(CT17) is the signal intensity from CT17 (within the linear range of the dilution series) for each sample (i), and µCT17 is the average signal intensity of CT17 from all samples.

The log2 values were then calculated, and the differences between the sample classes were determined using significance of microarray analysis (SAM) (29). Antibodies that were found to give significantly different signal intensities between samples, using Wilcoxon two class unpaired test, were listed (see Tables I–III) and used for cluster analyses of the sample classes. The antigens that were differentially expressed between the sample classes, (i) GA/Hp+ versus N/Hp– were filtered at a false discovery rate of 0% and a delta value of 0.811,(ii) N/Hp+ versus N/Hp– were filtered at a false discovery rate of 0% and a delta value of 0.505, and (iii) GA/Hp+ versus N/Hp+ were filtered at the false discovery rate 3.5% and delta value of 0.546 using the SAM method. The SAM-based approach was compared with analysis of variance-based methods, which generated similar results. Furthermore, normal probability plots that were generated for representative datasets demonstrated that they were typically normal distributed (data not shown). Principal component analysis (PCA) was performed using Spotfire software (version 8.0, Spotfire AB). The data was Z-score normalized by antigens prior to the hierarchical clustering analysis, which was performed using the unweighted pair-group method with arithmetic mean using Spotfire 8 software.


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  		TABLE I Protein signature associated with H. pylori-positive tumor tissue

 

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  		TABLE III Protein signature associated with gastric adenocarcinoma tumor

 

   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
Here, we report the first study where recombinant, microarray-adapted antibody fragments have been used to construct large scale antibody microarrays for analysis of clinical tissue samples. This antibody microarray technology platform23 has several unique features because it is based on molecularly designed recombinant antibody fragments resulting in high conformity in terms of structural characteristics, on-chip stability, functionality, and sensitivity (19, 22, 24, 25). Antibody microarrays containing 127 different recombinant antibody fragments against 60 different antigens, such as cytokines, complement factors, and other immunologically relevant proteins, were used to analyze 35 malignant and normal stomach tissue samples from H. pylori-positive and -negative patients. One to four different SinFab clones were used to target each antigen, thus avoiding dependence on single antigenic epitopes, minimizing the risk of label induced artifacts.

Validation of the Microarray Platform—
The image of a representative microarray (Fig. 1) demonstrates the homogenous spot morphology as well as the low background obtained with this platform. Analysis of the scanned images demonstrated that the correlation of the replicate spots within the arrays was high, displaying an average correlation coefficient of >0.99 (Fig. 2A). Furthermore, the reproducibility of duplicate experiments was analyzed by calculating the average correlation coefficient and average coefficient of variance). The average correlation coefficient, based on samples run 2–3 weeks apart, was 0.94 (Fig. 2B), and the average interarray coefficient of variance was 18%, which compares well with other approaches (15, 16), demonstrating that the platform handled analysis of complex clinical proteomes well.


Figure 1
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  		FIG. 1. Microarray images from stomach tissue samples analyzed by recombinant antibody microarrays, containing 127 different SinFabs in 8 replicates. The magnification shows the dilution series of the anti-cholera toxin antibody (CT17) specific for subunit B of cholera toxin, which was added into the samples and used for chip-to-chip normalization.

 

Figure 2
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  		FIG. 2. Reproducibility of the antibody microarray analysis. A, reproducibility of the replicate spots within an array (correlation coefficient > 0.99). B, chip-to-chip correlation based on samples run 2–3 weeks apart (average correlation coefficient of 0.94).

 
The normalization procedure is vital for correct data interpretation (15), and we have investigated two different strategies3 and concluded that chip to chip normalization, using a spike-in protein (cholera toxin, subunit B) works very well.3 In Fig. 1, the magnification shows the dilution series of the SinFab clone (CT17) used for detection of the spiked-in cholera toxin. Moreover, most SinFab clones against different epitopes of the same antigen, e.g. IL-13 in Fig. 3, displayed a very similar overall pattern.


Figure 3
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  		FIG. 3. Two-way hierarchical cluster analysis of the combined infection and tumor signature based on the 43 antibodies that gave significantly different signals from the comparison of GA/Hp+ and N/Hp–. GA/Hp+ (red) represents tumor tissue from patients that are positive for H. pylori, whereas N/Hp– (blue) represents normal stomach tissue from H. pylori-negative patients. Capital A or C indicates that the sample was obtained from the antrum (A) or the corpus (C) from the patients. The numbers indicate individual patients. Red indicates up-regulation, green represents down-regulation, and black indicates no change.

 
Identification of Protein Expression Signatures Associated with H. pylori Infection and Gastric Adenocarcinoma—
Next, we identified disease-associated protein signatures, using the SAM method (29). First, the combined infection and tumor signature was determined by comparing the expression profiles of the gastrointestinal adenocarcinoma tissue from H. pylori-positive (GA/Hp+) samples with tumor-free normal stomach tissue from H. pylori-negative (N/Hp–) samples. This analysis resulted in 30 significantly differentially expressed antigens detected by 43 different antibody clones (Table I). Second, the signature associated only with infection was determined by comparing H. pylori-positive tumor-free normal tissue (N/Hp+) samples with N/Hp– samples (Table II), resulting in 14 significantly differentially expressed antigens recognized by 17 different antibody clones. Finally, the signature associated with only the tumor was determined by comparing GA/Hp+ samples with N/Hp+ samples resulting in 29 significantly differentially expressed proteins (Table III) recognized by 35 different antibody clones.


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  		TABLE II Protein signature associated with H. pylori infection

 
The protein expression signatures were further evaluated by two-way hierarchical cluster analysis (Figs. 3–5). Analysis of the protein expression signature associated with both tumor and infection (GA/Hp+ samples versus N/Hp– samples) (Fig. 3) revealed two well separated clusters, demonstrating both high sensitivity (100%) and specificity (88%). Next, the signature associated only with infection was analyzed by comparing the N/Hp+ samples with the N/Hp– samples, resulting in two well defined hierarchical clusters (Fig. 4), showing 95% sensitivity and 88% specificity. Finally, the protein signature associated only with the tumor (GA/Hp+ samples versus N/Hp+ samples) was examined, resulting in high sensitivity (100%) but a lower specificity (52%) (Fig. 5), resulting in more false positives compared with the infection signature. This observation might be explained by the fact that the tissue samples in the normal H. pylori-positive (N/Hp+) group was obtained from tumor-free tissue at least 5 cm from the primary tumor. It is possible that gastric tumors have global effects on the immune response also affecting the surrounding gastric tissue, or that the normal samples are affected by the so called "field effect," which refers to changes in non-malignant tissue associated with a cancer (30). This latter explanation is supported by very recent data obtained from the same patient material, which shows that both T-cell activity and antibody levels are altered both in the tumor area as well as in the tumor-free mucosa of H. pylori-infected gastric cancer patients, as compared with H. pylori-infected asymptomatic individuals.45


Figure 4
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  		FIG. 4. Two-way hierarchical cluster analysis of the infection signature based on the 17 antibodies that gave significantly different signals from the comparison of N/Hp+ versus N/Hp–. N/Hp+ (green) represents normal tissue from patients that are positive for H. pylori, whereas N/Hp– (blue) represents normal stomach tissue from H. pylori-negative patients. Capital A or C indicates that the sample is obtained from the antrum (A) or the corpus (C) from the patients. The numbers indicate individual patients. Red indicates up-regulation, green represents down-regulation, and black indicates no change.

 

Figure 5
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  		FIG. 5. Two-way hierarchical cluster analysis of the tumor signature based on the 35 antibodies that gave significantly different signals from the comparison of GA/Hp+ versus N/Hp+. GA/Hp+ (red) represents tumor tissue from patients that are positive for H. pylori, whereas N/Hp+ (green) represents normal stomach tissue from H. pylori-positive patients. Capital A or C indicates that the sample is obtained from the antrum (A) or the corpus (C) from the patients. The numbers indicate individual patients. Red indicates up-regulation, green represents down-regulation, and black indicates no change.

 
To further analyze the protein infection signatures and examine the differences between the sample classes, PCA was used to generate a two-dimensional view of the data (Fig. 6). Plots 6A and 6B show that both the combined infection and tumor signature as well as the infection signatures classes were well separated, with the exception of one outlying N/Hp+ sample (Fig. 6B). Moreover, the PCA representation of the tumor-associated signature comparing GA/Hp+ samples versus N/Hp+ samples demonstrated that these sample classes could also be separated, although with lower specificity (Fig. 6C). These data further supported the conclusions from the hierarchical cluster analysis (Figs. 4 and 5).


Figure 6
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  		FIG. 6. The first two principal components from the log-transformed normalized data using the antigens filtered by SAM. A, samples from the GA/Hp+ group (black dots) and N/Hp– group (red dots). B, samples from the N/Hp+ group (blue dots) and N/Hp– group (red dots). C, samples from the N/Hp+ group (blue dots) and N/Hp– group (red dots). The two first principal components in the different PCA account for 88% (A), 87% (B), and 80% (C) of the total variance, respectively.

 
Analysis of the Antigens in the Protein Signatures—
Several cytokines were significantly up-regulated in the infection signature, including typical TH2 cytokines such as IL-5, IL-6, and IL-13, as well as TH1 cytokines including IFN-{gamma} and IL-2 (Table II). In addition, IL-10 and TGF-ß, which are associated with regulatory T cells, were also up-regulated (Table II). This confirmed previous studies that demonstrated an increased frequency of regulatory T cells in H. pylori-infected stomach mucosa (33) and reflects the complex balance of the immune response in stomach tissue, which has been suggested to fall between a fully polarized TH1 or TH2 profile (7). In fact, TH1, TH2, and regulatory T cell cytokines have all previously been shown to be associated with H. pylori infection (34, 35). In particular, IL-10, which suppresses TH1 cytokines, has been suggested to be responsible for the persistency of the infection (36). Additionally, granulocyte-monocyte-colony stimulating factor and IL-1ß, as well as the chemokine eotaxin (Table II), were also detected, confirming previous observations of H. pylori infection (34, 37–40).

In the tumor-associated protein signature, the typical TH2 cytokine IL-4 was up-regulated (Table III), which might indicate that the tumor progression is associated with a local TH2 skewing of the immune response (41). Furthermore, the cytokine profile of tumor tissue was associated with increased levels of IL-10, as compared with both N/Hp+ samples and N/Hp– samples. Interestingly, increased IL-10 production was also recently observed in H. pylori-stimulated cultures of T cells from gastric cancer patients compared with asymptomatic H. pylori-infected individuals.5 Because IL-10 is associated with immune suppression, the combination of TH2 skewing and IL-10 production might facilitate tumor progression. In addition, the TH1 cytokine IL-12 was also up-regulated (Table III), again demonstrating the complex balance of the immune response in the stomach (7). In comparison to the infection signature where C1s was the only up-regulated complement factor, several complement factors, such as C1q, C3, C5, factor B, (FB), and esterase inhibitor (EI) (Table III) were up-regulated in the tumor-associated signature. These complement factors might be produced by the tumor cells as a result of TNF{alpha} induction, as was recently shown for C3 and factor B on gastric cancer-derived cell lines (42). Of note, additional chemokines, such as MCP-1, MCP-3, and IL-8 (Table III) were up-regulated, which could reflect a more severe inflammatory status and an increased tissue vascularization in the tumor (43–46). The latter suggestion was further supported by the up-regulation of vascular endothelial growth factor and angiomotin (Table III), which mediates angiogenesis and has previously been shown to be expressed by carcinomas (26, 47, 48). Finally sialyl-Lewis X, known to be highly expressed in gastric adenocarcinomas (27), and leptin, a possible neoplastic growth factor (31, 32), were also found to be up-regulated in the tumor-associated protein expression profile (Table III).

Overall, the proteins of the infection signature and the tumor signature displayed distinct differences, although common features were also evident. The Venn diagram in Fig. 7 provides an overview of the infection and tumor signature as well as the combined signature. All but one of the proteins in the infection signature were also found within the combined signature. Several proteins in the tumor signature were also found within the combined signature, although 10 uniquely regulated antigens were detected. There was a relatively small overlap between the infection and the tumor-associated signature, which would be important for diagnostic applications. In fact, several of the proteins in the different signatures have the potential to be used as biomarkers and for development of novel diagnostic methods for H. pylori infection and gastric adenocarcinoma, although further studies will be needed to validate our findings. In addition, some of the proteins associated with the infection signature such as IL-9 and IL-11 as well as with the tumor signature, e.g. MCP-4, which plays a role in accumulation of leukocytes during inflammation, have not previously been associated with H. pylori infection or gastrointestinal carcinomas, and further investigations into these findings may advance the understanding of these diseases.


Figure 7
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  		FIG. 7. Venn diagram showing the distribution of differentially expressed antigens in the different comparisons. Antigens that were differentially expressed in (i) GA/Hp+ samples compared with N/Hp– samples (top circle), in (ii) N/Hp+ samples compared with N/Hp– samples (right circle), and in (iii) GA/Hp+ samples compared with N/Hp+ samples (left circle).

 

   CONCLUSIONS
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
In this study, we have demonstrated the first example of an antibody microarray based on recombinant, microarray-adapted antibody fragments used for analysis of protein expression signatures in complex cancer proteomes. The results showed that we could identify protein expression signatures associated with either H. pylori infection or gastric adenocarcinoma, thus allowing us to distinguish a general disease signature as compared with a cancer associated signature.


   ACKNOWLEDGMENTS
 
The technical assistance of Ann-Charlott Olsson and Karin Enarsson is greatly appreciated. The clinical samples were kindly provided by Dr. Erik Johnsson, Department of Surgery, Sahlgrenska University Hospital.


  FOOTNOTES
 
Received, May 9, 2006, and in revised form, July 13, 2006.

Published, MCP Papers in Press, July 13, 2006, DOI 10.1074/mcp.M600170-MCP200

1 The abbreviations used are: SinFab, single framework recombinant antibody fragment; scFv, single chain variable fragment; GA, gastric adenocarcinoma; Hp, H. pylori; N, normal; SAM, significance of microarray analysis; PCA, principal component analysis; TH, helper T cell; C, corpus; CT, cholera toxin. Back

2 C. Wingren, J. Ingvarsson, L. Dexlin, D. Zul, and C. A. K. Borrebaeck, manuscript in preparation. Back

3 J. Ingvarsson, A. Larsson, A. Sjöholm, L. Truedsson, C. A. K. Borrebaeck, and C. Wingren, manuscript in preparation. Back

4 M. Quiding-Järbrink, K. Enarsson, A. Lundgren, M. Hansson, C. Johansson, M. Hermansson, E. Johnsson, and A.-M. Svennerholm, submitted for publication. Back

5 S. Lundin, K. Enarsson, A. Lundgren, E. Johnsson, M. Quiding-Järbrink, and A.-M. Svennerholm, submitted for publication. Back

* This study was supported by grants from the Swedish National Science Council (VR-NT), the Åke Wiberg Foundation, the Landshövding Per Westlings Foundation, and the Swedish Cancer Society. Back

To whom correspondence should be addressed. Tel.: 46-46-222-9613; Fax: 46-46-222-4200; E-mail: carl.borrebaeck{at}immun.lth.se


   REFERENCES
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 

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