Faculty of Biological Science and The Mervin Bovaird Center for Studies in Molecular Biology and Biotechnology, 600 S. College Ave., University of Tulsa, Tulsa, OK 74104
Received on April 7, 2004; revised on July 6, 2004; accepted on July 26, 2004
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Abstract |
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Key words: B lymphocyte / glycocalyx / lectin / sialyltransferase / Q-RT PCR
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Introduction |
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Over 20 different enzymes are currently known to meditate such sialylations, and they constitute a family of transferases that fall into three distinct subfamilies based on their general substrate and linkage specificities (Tsuji et al., 1996). There seems to be significant substrate overlap between sialyltransferases (STases) within a subfamily, at least when assayed in vitro (Tsuji, 1996
). However, Marth and co-workers have constructed several informative sialyltransferase gene knockout mice (KO), including the ST6Gal I/ (Hennet et al., 1998
), ST3Gal I/ (Priatel et al., 2000
), and ST3Gal IV/ (Ellies et al., 2002) lines. These studies demonstrate that each transferase has at least one unique and immunologically important substrate in vivo because each mouse line has a distinct immunological phenotype.
Although the ST6Gal I gene is expressed in multiple tissues throughout the body including the nervous system (Dall'Olio, 2001), the only major defect reported in the ST6Gal I KO mouse is an impaired humoral immune response. This is evidenced by reduced levels of circulating immunoglobin M (IgM), impaired B cell proliferation in response to various activation signals, and impaired antibody production in response to both T-dependent and T-independent antigens. Both surface IgM and CD22 levels are significantly reduced on ST6Gal I KO B cells as well (Hennet et al., 1998
). Sialic acid in an
2,6-linkage is at least part of the preferred ligand for CD22, a known regulator of B cell receptor (BCR) signaling (Tedder et al., 1997
); however, the ST6Gal I KO has a more severe phenotype than a CD22 KO, suggesting that loss of 2,6-linked sialic acid has other effects on the immune system beyond functioning as a ligand for CD22 (Hennet et al., 1998
). Again, although the ST3Gal I gene is widely expressed, the only immediately apparent defect in the KO mouse is an almost total absence of peripheral CD8+ T cells, which are lost by apoptosis, perhaps induced via CD43 clustering (Priatel et al., 2000
). It has been recently suggested that ST3Gal Imediated sialylation of CD8ß modulates its affinity for major histocompatibility locus (MHC) I during thymocyte maturation (Moody et al., 2003
). Collectively these studies provide solid evidence that the sialylation state of the immune cell glycocalyx is critical for the proper immune function and that changes to the sialylation state are under direct developmental control. Yet to be identified are the specific immune and genetic mechanisms controlling such changes.
Plant lectins have proven to be invaluable tools for the structural analysis of both the immune cell glycocalyx and its constituent glycoproteins and glycolipids, and for characterizing the general sialylation state of various tissues in both normal and KO mice (Martin et al., 2003). We use flow cytometry to quantify binding of the sialic acidspecific lectins from Sambucus nigra (SNA) and Maackia amurensis (MAH) and the Galß1,3GalNAc-specific lectin Arachis hypogaea (PNA) to murine splenic B cells following in vitro activation. We show that any of several well-defined immune signals induce significant and differential changes in the lectin-binding phenotype of the activated cells. Furthermore, using real-time relative reverse transcriptase polymerase chain reaction (RT-PCR), we show that for two of the three STases considered here such changes are well correlated with the mRNA abundance of the STase putatively responsible for synthesizing the lectin binding site. Finally, we show that the ST6Gal I gene exhibits differential exon usage in its 5' untranslated region (UTR) in response to differential signaling through various BCRs.
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Results |
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As shown in Figure 1, in vitro cross-linking of sIgM or CD40 led to the rapid appearance of CD69, a well-characterized marker of lymphocyte activation (Sanchez-Mateos and Sanchez-Madrid, 1991). Expression of intercellular adhesion molecule-1 (CD54) and CD44 were also increased (data not shown). Anti-CD40 but not anti-IgM stimulation also leads to increased CD80 expression, whereas incubation in culture medium alone is sufficient for significant CD86 expression (Bagriaçik and Miller, 1999
). When used alone, neither interleukin (IL)-2 nor IL-4 (Figure 1) nor any other tested cytokine (IL-6 and interferon [IFN]-
), affected these particular markers, nor did the cells proliferate significantly in response to any of these reagents administered individually (data not shown). However, incubation in IL-4 alone did enhance expression of MHC class II molecules, and treatment with IL-4, IL-6, or IFN-
led to increased B cell survival in culture as assessed by propidium iodide staining (data not shown). From these studies we conclude that the B cell preparations and reagents are behaving in a manner consistent with the known responses of small resting B cells to the specific stimulatory signals tested (DeFranco, 1999
).
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SNA binding and ST6Gal I expression are poorly correlated
SNA prefers sialic acid linked 2,6 to lactosamine but may bind to 2,6-linked sialic acid on other structures as well (Brinkman-Van der Linden et al., 2002
). Compared to freshly isolated resting B cells, SNA binding was significantly increased under all conditions of incubation (Figure 5), including incubation in medium alone (data not shown). It has been shown that SNA binding is virtually abolished in ST6Gal I KO mice (Martin et al., 2003
), but despite compelling evidence that ST6Gal I is the sole enzyme responsible for the presence of Sia-
2,6-Gal on the lymphocyte cell surface (Hennet et al., 1998
), there is no significant correlation between SNA binding and the expression of ST6Gal I coding sequence (r = 0.5, p > 0.1). This lack of correlation could result from one or more of several reasons. First, there may be a limited number of substrate molecules on which ST6Gal I can act, and any increase in enzyme activity above some threshold level saturates these substrates. It is also likely that there are posttranscriptional mechanisms controlling enzyme activity as well and that such mechanisms are also differentially affected by the various incubation conditions. Finally, it is possible that undefined membrane changes could lead to differential accessibility of SNA for binding sites, thus uncoupling the quantitative relationship between ST6Gal I expression and SNA binding.
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Because all ST6Gal I 5'UTRs seem to show enhanced relative expression when IL-4 was in the culture medium (Figure 6) and because anti-IgM induced signaling seemed to suppress ST3Gal I expression (Table I), we grouped the data with respect to the IL-4 and anti-IgM treatment conditions and performed a Student's t-test. Of the three ST6Gal I UTR sequences assayed, only X1 exhibited statistically different abundances between incubation conditions that either did or did not contain IL-4 (p < 0.001). The enhanced expression of X1 in the presence of IL-4 is not simply a result of an enhanced viability of these cultures because the anti-CD40+IL-2 cultures are just as viable at all time points as assessed by propidium iodide staining (data not shown). Thus IL-4 seems to be the primary stimulus for the increased abundance of X1 under any of the conditions tested.
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Discussion |
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ST3Gal IV
MAH is known to recognize 2,3-linked sialic acid bound either to Galß1,4(3)GlcNAc-R or Galß1,3GalNAc-R. In some instances it may also recognize 3-linked sulfate bound to these same substrates (Martin et al., 2002). Despite its apparent broad specificity, in this study we find that the correlation between MAH binding and ST3Gal IV mRNA abundance is extremely significant. Furthermore, examining the individual conditions of incubation reveals that MAH binding is significantly increased following mIgM cross-linking (Figure 2) and, of all ST3Gal transferases assayed (IVI), only ST3Gal IV mRNA abundance is specifically increased following such treatment (Table I and unpublished data). Anti-IgM treatment also leads to strong expression of PNA binding, suggesting that increased 2,3 sialylation of glycans other than Galß1, 3GalNAc-R is responsible for the increased MAH binding and that ST3Gal IV does not significantly compete with ST3Gal I for the Galß1, 3 GalNAc-R substrate in these cells. This observation is consistent with studies of ST3Gal IV/ mice in which PNA binding to lymphocytes was found to be only modestly increased (Ellies et al., 2002).
The study of Ellies and colleagues also found that of the four ST3Gal KOs tested (IIV), ST3Gal IV contributed most significantly to selectin ligand formation (sLex) as measured by P and E selectin binding. In studies from our laboratory sLex expression as measured by monoclonal antibody binding was increased under all incubation conditions tested relative to resting cells (Marino, unpublished data), including incubation in media plus IL-2 alone, a condition in which ST3Gal IV expression is significantly decreased (Table I). However, Ellies et al. noted that the reduction in E/P selectin binding to neutrophils seen with ST3Gal IV KO mice relative to wild type was significantly less than that seen with core 2 KO mice, thus implicating at least one other sialyltransferase in sLex biosynthesis in neutrophils. It is probable that this other enzyme (probably ST3Gal VI) is also at least partially responsible for significant sLex synthesis in B cells, and we find that ST3Gal VI mRNA abundance is indeed significantly increased (1.4-fold) following B cell incubation in IL-2 (Marino, unpublished data). This also suggests that sLex represents only a small fraction of all possible MAH binding sites in B cells and that most such sites are synthesized by ST3Gal IV.
Interestingly, the strong correlation between MAH binding and ST3Gal IV mRNA abundance also suggests that enzyme activity is tightly controlled by mRNA abundance and that there is little if any posttranscriptional control exerted on this enzyme under any of our assay conditions. This tight correlation of mRNA abundance and enzyme activity is also consistent with the observation of haplo-insufficiency for this gene (Ellies et al., 2002). Surprisingly, ST3Gal IV expression was also significantly correlated with SNA and PNA binding (Table II); however, this is most likely reflective of the strong correlations between MAH and PNA binding (0.9, p < 0.001) and between MAH and SNA binding (0.9, p < 0.001), which is probably indicative of the tight control maintained over the structure of the B cell glycocalyx in response to various differentiation signals.
ST3Gal I
Increased PNA binding to core 1 structures (Galß1,3GalNAc-ser/thr) could result from changes any one of three enzyme activities: (1) a reduction of ST3Gal I leading to exposure of core 1 structures, (2) an increase in the activity of the core 1 enzyme (ß1,3-galactosyltransferase) in excess of the ability of ST3Gal I modify the core 1 structure, or (3) an increase in core 2 enzyme, which competes with ST3Gal I for the core 1 substrate and whose activity does not block PNA binding. In most tissues, including the spleen, PNA binding is only modestly increased in the ST3Gal I KO mouse; however, binding to kidney glomeruli, the adenal cortex, and the thymic medula is significantly increased (Martin et al., 2002), suggesting that blocking of terminal Galß1,3GalNAc is mediated by solely ST3Gal I activity in these tissues, whereas in most other tissues additional STase activities may also participate. Binding of tomato lectin, which recognizes linear polylactosamine attached to core 1 structures, is significantly increased in the spleen of the ST3Gal I KO mouse, presumably due to increases in core 2 branched structures created by core 2 enzyme in the absence of terminal sialylation by ST3Gal I (Martin et al., 2002); however, tomato lectin binding is not increased in anti-IgM-activated B cells (data not shown), presumably due to reduced core 2 enzyme activity resulting from reduced core 2 mRNA expression (Table I). Furthermore, it is known that CD45 is the principal carrier of PNA binding sites on murine B cells following activation with LPS/dextran and IL-2 (Cook et al., 1987) and that core 2 enzyme is required for the synthesis of the B220 epitope on CD45 (Ellies et al., 2002). In our hands, PNA binding is increased and B220 binding and core 2 mRNA abundance are significantly reduced following LPS stimulation of murine B cells (Marino et al., unpublished data), which is consistent with the results of Cook et al. (1987)
. Interestingly, however, B cells activation via anti-IgM is not accompanied by a decrease in B220 binding, even though there is a significant reduction in core 2 mRNA expression. Furthermore, the principal PNA binding protein seen in western blots of cell surface proteins from anti-IgM activated cells is not CD45 (Marino et al., unpublished data), suggesting that the mechanisms for the appearance of PNA receptors under the two conditions of activation may be different. Further experiments are required to resolve this issue.
ST6Gal I
Gene KO studies have established that ST6Gal I is the enzyme responsible for generating most if not all SNA binding sites in the adult mouse (Martin et al., 2002) and although a second ST6Gal transferase (ST6Gal II) has been identified in humans (Krzewinski-Recchi et al., 2003; Takashima et al., 2002
), this enzyme seems to make no significant contribution to 2,6-linked sialic acid on lymphocytes. It was surprising therefore that ST6Gal I coding sequence abundance and SNA binding were not significantly correlated in this study. Possible explanations for this observation are: (1) substrates for the ST6Gal I enzyme are saturated following even the relatively modest increases in mRNA expression (2.2-fold) seen following anti-CD40 stimulation, thus uncoupling SNA binding and ST6Gal I mRNA expression, (2) the posttranslational control of ST6Gal I enzyme activity by phosphorylation (Breen and Georgopoulou, 2003
; Gu et al., 1995
), and/or (3) the differential translation of ST6Gal I mRNAs containing different 5'UTRs. With respect to this last possibility, analysis of the 5'UTR sequences with the MFOLD 3.1 RNA secondary structure program (Zuker et al., 1999
) suggests that the 5'UTRs have very different structures with very different folding energies, Q-O being the most stable at 170 to 210 kcal, whereas X2 at 35 to 40 kcal and X1 at 75 to 90 kcal are significantly less stable. These numbers are also consistent with correlations between 5'UTR abundance and SNA expression, with Q-O being negatively correlated and X1 and X2 positively correlated. However, these correlations were not statistically significant, and further studies are needed to validate this conclusion.
Studies from other laboratories have also shown that immune signaling molecules can modulate ST6Gal I expression. Hanasaki et al. (1994) demonstrated that both tumor necrosis factor
and IL-1 could mediate induction of ST6Gal I coding sequences in human endothelial cells and caused a differential sialylation of several cell surface proteins; in 1999, Lau's laboratory (Dalziel et al., 1999
) demonstrated that incubation in IL-6 caused increased expression from the H exonencoding P1 promoter active in mouse hepatocytes, implicating it in the acute phase response. However, subsequent studies using P1 gene KO mice failed to confirm this suggestion (Appenheimer et al., 2003
). In this study we demonstrate that IL-4 signaling can control the mRNA abundance levels of X1-containing ST6Gal I transcripts.
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Materials and methods |
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Preparation of resting small B cells
B cells were prepared as previously described (Bagriacik and Miller, 1999). Briefly, spleen cells are treated with NH4Cl/Tris, pH 7.2, to lyse red blood cells and then incubated in MLC (RPMI-1640 supplemented with 2 mM glutamine, 50 µM 2-mercaptoethanol, 5 mM HEPES, 10% fetal bovine serum) for 1 h at 37°C, 5% CO2 to remove adherent cells. Nonadherent cells are separated on a five-layer Percoll. These cells are treated with a combination of anti-Thy1.2, anti-CD4, anti-CD8, anti-Mac1
, anti-CD86, and anti-CD80 monoclonal antibodies at 4°C for 30 min and are depleted by low-tox guinea pig complement lysis (Accurate Chemical, Westbury, NY) for 45 min at 37°C. Depleted cells were separated on a five-layer Percoll (Pharmacia, Uppsala, Sweden) gradient (50%, 60%, 70%, 80%, and 100%) and cells from 6070% interface are recovered.
Cell activation
B cells are activated by incubation in 24-well tissue culture plates in complete medium (106/ml) containing either anti-CD40 (1 µg/ml) or anti-IgM (30 µg/ml) with or without IL-4 (5 ng/ml) or IL-2 (30 U/ml). After 2448 h, depending on the experiment, cells were harvested and used for lectin staining or RNA isolation.
Cell staining with lectins and antibodies and flow cytometry
Cell staining with lectins was performed as previously described (Bagriacik and Miller, 1999). All cells were stained with biotinylated lectins from Vector Labs (Burlingame, CA). Five x 105 cells were stained in 100 µl final volume of HBSS containing 520 µg/ml lectin for 30 min in the dark at 4°C. The cells were then counterstained with phycoerythrin-conjugated streptavidin (PharMingen, San Diego, CA) and fixed with 200 µl 3.7% formaldehyde prepared in Hank's balanced salt solution (HBSS). For antibody staining, cells (106/ml) were first incubated with an Fc receptor blocker (anti-CD16/CD32) according to the manufacturer's recommendations, and then titrated concentrations of monoclonal antibodies were conjugated either with phycoerythrin or biotin in 100 µl HBSS containing 0.01% sodium azide for 30 min at 4°C. The cells were then washed and fixed in 2% formaldehyde prepared in HBSS. For biotin-labeled antibody staining, phycoerythrin-conjugated streptavidin (Pharmingen, San Diego, CA) was used as a secondary reagent. Cells stained with lectins or antibodies were analyzed using an EPICS 751 flow cytometer interfaced with a Cicero data acquisition unit running Cyclops software (Cytomation, Fort Collins, CO).
RNA was prepared from cell suspensions using TRIzol Reagent (Gibco BRL Gaithersburg, MD) according to the manufacturer's instructions. Quality and quantity of RNA was assessed by measuring the A260/A280 ratio and by analysis on ethidium bromidestained 1% agar gels with and without heating to 65°C for 10 min in sterile, diethyl pyrocarbonatetreated, 18 M water. Gels were imaged on a Molecular Dynamics Storm and analyzed using ImageQuant software (Amersham Pharmacia Biotech, Piscataway, NJ). Any RNA preparations exhibiting significant qualitative or quantitative differences between heated and unheated samples were rejected. Isolated RNA was treated with DNase I (Invitrogen, Carlsbad, CA) prior to use for cDNA synthesis.
cDNA synthesis and PCR amplification
Reverse transcription was performed using 5 µg total RNA, random primers, and SuperScript II RT (Invitrogen) in a total volume of 20 µl. The reaction was incubated at 25°C for 10 min, followed by incubation at 42°C for 50 min. cDNA synthesis was followed by RNase H treatment (Roche Molecular Biochemicals, Indianapolis, IN). To minimize potential effects of differential synthesis during the RT reaction, three separate cDNA reactions were pooled for each RNA preparation analyzed. Real-time PCR is carried out using a Smart Cycler thermal cycler (Cepheid, Sunnyvale, CA). Each PCR reaction included 2.5 µl of 10x PCR buffer without MgCl2 (Sigma-Aldrich, St. Louis, MO), 1.0 µl 25 mM MgCl2, 0.5 µl 10 mM dNTPs (Perkin-Elmer, Wellesley, MA), 20 pM each primer, 0.25 µl Taq DNA Polymerase (Sigma-Aldrich), 0.2 µl 30% bovine serum albumin (Sigma-Aldrich), SYBR Green I (Molecular Probes, Eugene, OR) at a final concentration of 1:20,000 dilution of the commercial stock and an appropriate volume of the cDNA preparation. PCR cycling conditions included a 94°C heating step for 1 min at the beginning of every run. The tubes were then cycled at 94°C for 30 s, annealed at 6268°C for 60 s, and extended at 72°C for 60 s. Optical data were collected during the annealing step. A melting curve was generated at the end of every run to ensure product uniformity.
The primers used for Q-RT PCR were: 18S rRNA forward: 5'-TCAAGAACGAAAGTCGGAGGTT-3'; 18S rRNA reverse: 5'-GGACATCTAAGGGCATCACAG-3'; ST3Gal I forward: 5'-GGAGGAGGACACATACCGGTG-3'; ST3Gal I reverse: 5'-GGAGTCCTTCAGGTTACCGGAG-3'; ST3Gal III forward: 5'-TGTCAGTCACCAAAGAATACCGC-3'; ST3Gal III reverse: 5'-GCACTCACTCTCTCCTTGTAGACGAT-3'; ST3Gal IV forward: 5'-TAAAGAGCCTCGAGTGTCGTCG-3'; ST3Gal IV reverse: 5'-CCGACTCAGGATAGAAGAGACGTAT-3'; ST6Gal I coding forward: 5'-GTGTGGAAGAAAGGGAGCGAC-3'; ST6Gal I coding reverse: 5'-CAGAATCAGGATTCCTTCGGTTGTA-3'; ST6Gal I exon O forward: 5'-AGCCGGATGCTGAATGGTT-3'; ST6Gal I exon O reverse: 5'-CTCCAGGTCCTCAGGAGC-3'; ST6Gal I exon X1 forward: 5'-TCCTTTCCATCACTGTCTGCCT-3'; ST6Gal I exon X1 reverse: 5'-CTCCAGGTCCTCAGGAGCC-3'; core 2 forward: 5'-GGGATGCCTGGTGCTTGATAG-3'; core 2 reverse: 5'-GACAAGAGAAAAGTCTCCTCCGA-3'. Each primer pair has been validated by sequencing of the resultant RT-PCR product. All primers were designed using the X-Primer program and synthesized by the Recombinant DNA/Protein Resource Facility at Oklahoma State University (Stillwater, OK).
Data analysis
Optical data were exported from the Cepheid Smart Cycler as comma-separated values files (*.csv) and imported into MS Excel. We wrote a Visual Basic Excel macro that facilitates determination and conversion of the appropriate Smart Cycler optics data to a logarithmic format for subsequent analysis. The resultant data is then pasted into SAS JMPIN 4.0 for determination of slopes, intercepts, and their respective standard errors and correlation coefficients, which are subsequently pasted into a second spreadsheet for final calculation of relative expression level (Marino et al., 2003). Correlation analysis and Student's t-tests were conducted using Prism 3.0 (GraphPad Software, San Diego, CA).
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Acknowledgements |
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Footnotes |
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Abbreviations |
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References |
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