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J Biol Chem, Vol. 275, Issue 14, 10235-10246, April 7, 2000
From the Kinetic rates and affinity are essential
determinants for biological processes that involve receptor-ligand
binding. By using a micropipette method, we measured the kinetics of
human Fc The structural segments of cell surface receptors consist of
distinct domains as follows: a glycosylated extracellular domain linked
to either a transmembrane
(TM)1 domain with a
cytoplasmic tail or to a glycosylphosphatidylinositol (GPI) moiety
without TM and cytoplasmic domains (1, 2). The anchor can influence the
function of a receptor. TM anchors of some receptors carry information
for protein internalization and subunit association, whereas those of
other receptors transduce signals (3, 4). The GPI moiety consists of a
glycan core sandwiched between ethanolamine and a lipid tail.
Ethanolamine is covalently attached to the carboxyl terminus of the
protein by an amide bond, whereas the lipid tail directly inserts into the outer leaflet of the membrane but does not cross the bilayer (5).
The GPI anchor has been implicated in facilitating the lateral mobility
of the protein on the cell surface (6) and enhancing receptor-mediated
cell adhesion (7, 8).
There are four known eukaryotic receptors that naturally exist in both
membrane anchor isoforms as follows: neural cell adhesion molecule,
lymphocyte function-associated antigen 3, vascular cell adhesion
molecule 1, and Fc CD16 is a 50-80-kDa highly glycosylated cell surface receptor for
monomeric IgG (see Ref. 22 and references cited therein). CD16b is
polymorphic with the two alleles being termed neutrophil alloantigen 1 (NA1) and 2 (NA2). CD16a and CD16b are products of two highly
homologous genes, and their 191 amino acid extracellular domains differ
by only 6 amino acids (Fig. 1). CD16b
lacks the 20-amino acid TM segment as well as the 25-amino acid
cytoplasmic domain of CD16a. In addition, the surface expression of
CD16a requires associated subunits, such as the Ligand binding of CD16 can be influenced by a number of factors. CD16a
on NK cells binds monomeric human IgG (hIgG) with higher affinity than
CD16b on neutrophils (23). It is not known, however, to what degree
this differing affinity is due to the differences in the extracellular
domain, in the membrane anchor, or in the cellular background of the
two membrane isoforms. The two alleles of CD16b, which differ by four
amino acids and two glycosylation sites in the ectodomain (Fig. 1),
have the same affinity for hIgG1 complex but different affinities for
hIgG3 complex (24). NK cell CD16a has a higher affinity for hIgG than
monocyte CD16a despite the fact that the two proteins have an identical
polypeptide core, suggesting that glycosylation can influence CD16a's
affinity for the ligand (25). Miller et al. (26) reported
higher affinity for murine IgG2a (mIgG2a) of CD16a than of CD16a-GPI, a
molecule created by replacing the TM and cytoplasmic domains of CD16a
with a GPI anchor (27). These authors suggested an affinity-enhancing role for the associated In the present work we quantified the kinetic rates and binding
affinity of CD16-IgG interaction using a recently developed micropipette method. We asked whether the membrane anchor (including the associated subunits) itself had any influence on these intrinsic binding parameters. We further asked what mechanisms might cause such
differences and what role the associated Our micropipette method determines reaction kinetics between CD16 and
IgG when the two molecules are bound to apposing surfaces as in the
case of cell-cell adhesion, i.e. two-dimensional or solid-state binding kinetic rates (28). Only recently have such measurements become experimentally possible, and relatively little data
exist in the literature (28-31). Here we report the systematic measurements of forward and reverse rate constants of two human CD16
membrane isoforms interacting with human, rabbit, and murine IgGs.
The micropipette method mechanically assays the chemistry of
receptor-ligand binding at the level of a single pair of cells and,
most frequently, at the level of a single pair of molecules. To
determine whether the membrane anchor effect, if detected, was
dependent on the two-dimensional nature of the experiment or on the
specific technique used, we also measured binding affinities of the
same CD16-expressing CHO cells for soluble IgGs from the same species,
i.e. three-dimensional affinity, by a conventional competitive inhibition method.
Our data show that, in comparison to CD16a-TM, CD16a-GPI bound with
faster forward rates and higher affinities to hIgG and rabbit IgG
(RbIgG) but with a slower forward rate and a lower affinity to mIgG2a.
Thus, the membrane anchor of CD16 influenced its ligand binding kinetic
rates and affinity, and the qualitative trend of such an effect was
inverted when the ligand was changed. Furthermore, this effect
exhibited the same pattern regardless whether binding was measured with
membrane-bound ligands using the micropipette method or with
fluid-phase ligands via competitive inhibition. The findings that the
anchor effect flipped with different ligands and that the same results
were observed in both two-dimensional and three-dimensional studies are
important. They enabled us to rule out the following six possible
mechanisms for the membrane anchor effect: differing lateral
diffusivity, rotational flexibility, molecular orientation, binding
site height, surface distribution, and functional clustering of the two
molecules. Furthermore, no major differential glycosylation between the
two CD16a isoforms was found by SDS-PAGE analysis. We hypothesize that
there is a conformational difference between the two CD16a isoforms
that causes the observed anchor effects. This proposed mechanism was supported by the finding that whereas an anti-CD16 monoclonal antibody
(mAb) bound with higher affinity for CD16a-GPI than CD16a-TM, another
mAb reacted strongly with CD16a-TM but only weakly with CD16a-GPI,
suggesting that an antigenic epitope was down-regulated after the
membrane anchor of CD16a had been changed from TM to GPI.
cDNA Constructs--
The cDNAs encoding human CD16A-TM
in a pSVL vector and CD16A-GPI in a pCDM8 vector were provided by Dr.
J. Ravetch (Sloan-Kettering Institute for Cancer Research, New York).
We further subcloned the CD16A-TM cDNA into the PCR3uni vector
(Invitrogen, San Diego, CA). The cDNAs encoding the two human CD16B
alleles, the Cells and Antibodies--
CHO cells transfected to express human
CD16a-TM, CD16bNA1, CD16bNA2, CD16a-GPI,
CD16a-
The anti-CD16 adhesion-blocking mAbs CLBFcgran-1 (murine IgG2a) and 3G8
(murine IgG1) as well as the irrelevant control mAb X63 (murine IgG1)
were produced in-house from hybridomas as described previously (20).
The anti-CD16 mAb VEP13 (murine IgM) was obtained from IV Leukocyte
Workshop. The FITC-coupled F(ab')2 fragment of goat anti-hIgG,
anti-RbIgG, anti-mIgG, and anti-mIgM polyclonal antibodies used in flow
cytometry were from Sigma. The cleaving of CLBFcgran-1 into Fab
fragment was done by Lampire (Pipersville, PA). The dimeric soluble
form of CD16a (sCD16a) and another soluble molecule sB7 that were used
as irrelevant controls were produced by our laboratory and will be
described elsewhere.2
IgG Ligands and Their Membrane Coating--
Total hIgG (Lampire
and Jackson ImmunoResearch, West Grove, PA), RbIgG (Sigma), and mIgG2a
(Sigma) were used as ligands for CD16. Before each competitive
inhibition experiment, IgG ligands were centrifuged at 100,000 rpm for
1 h to remove large aggregates from solution. IgG ligands were
coated onto human red blood cells via a chromic chloride method (32).
This method covalently links free protein in solution to proteins
expressed on the red blood cell surface via interactions at the
carboxyl groups. Different coating densities on the red blood cell
membrane are easily achieved by varying the soluble protein and CrCl
concentrations during the procedure. Procedures for collection and
isolation of red blood cells have been previously described (28).
Quantification of Surface Protein Density--
Quantification of
the cell surface densities of the receptor and ligand is necessary for
the two-dimensional kinetic measurements. This was done primarily by
flow cytometry but also was cross-checked by radioimmunoassay, as
described previously (28). Briefly, CD16-expressing and control CHO
cells were incubated first with primary mAb CLBFcgran-1 (25 µg/ml in
PBS for 30 min on ice) or without primary antibody for control and then
with the FITC-labeled, F(ab')2 fragment of goat anti-mIgG
secondary antibody (50 µg/ml in PBS for 30 min on ice). The
ligand-coated red blood cells were incubated directly with FITC-labeled
IgG species-matched (or -mismatched for control) goat anti-IgG
secondary antibody. Cells were analyzed by FACS® (Becton Dickinson,
San Jose, CA), and their mean fluorescence intensities were compared
with standard calibration beads (Flow Cytometry Standards Corp., San
Juan, Puerto Rico) to determine the mean number of fluorophores per
cell which was then converted into labeled protein per cell (33,
34).
Flow cytometry was also used to determine the expression of an
antigenic epitope recognized by mAb VEP13 on the CD16a molecules. In
this experiment the concentrations of the primary antibody (VEP13) were
titrated in serial dilutions to ensure that the comparison was not
biased by insufficient mAb binding at sub-saturation concentrations.
The radioimmunoassay for determining the CD16 expression on CHO cells
was done by a small alteration in the Scatchard protocol during the
three-dimensional binding assays (below). Determination of receptor
density by this method was less desirable than that of flow cytometry
because it required the constant availability of
125I-CLBFcgran-1 (Fab fragment); however, the correlation
between the two methods is good (R2 ~90%)
(28) and imparts more confidence on our surface protein density
estimations. Radiolabeling of proteins was done by using IODO-GEN-coated tubes (Pierce) (24).
Cell Surface Labeling and
Immunoprecipitation--
CD16-expressing CHO cells (1 × 107) were iodinated with 1 mCi of Na125I
(Amersham Pharmacia Biotech) using IODO-GEN. Cells were washed and
lysed in 5 mM iodoacetamide, 1 mM diisopropyl
fluorophosphate, 1% aprotinin, 1% bovine hemoglobin with either 1%
Triton X-100 (for CHO cells expressing TM-anchored CD16a) or 50 mM n-octyl Three-dimensional Binding Studies--
The binding affinities of
CLBFcgran-1 Fab for the two CD16a membrane anchor isoforms were
determined by Scatchard analysis (35). The low affinity of monomeric
IgG for CD16 makes direct measurement by the Scatchard method
unreliable. To circumvent this difficulty, a competitive inhibition
assay was used where the low affinity ligand IgG competes with the high
affinity antibody CLBFcgran-1 for receptor binding (36). Briefly, CHO
cells were grown in flasks until near confluence. Cells were rinsed
once in PBS and then removed from the flask using PBS/EDTA (containing 5 mM EDTA). After washing, they were resuspended at 1 × 106 cells/ml in PBS/EDTA, pH 7.5. Cells were then added
to V-bottom 96-well plates at 100 µl per well. The wells were
precoated with 1% IgG-free BSA (Sigma) in PBS by incubating at room
temperature for 2 h. They were rinsed with PBS/EDTA and kept on
ice until the cells were added. After adding cells, the plates were
spun at 2000 rpm for 2 min. The supernatant was removed, and a solution of 50 µl of PBS/EDTA and titrated amounts of IgG was added to each
well with mixing. Then, 50 µl of PBS/EDTA, 0.25-0.50 µg/ml 125I-CLBFcgran-1 Fab was added to each well, followed by a
45-min incubation on a shaker at 5 °C. After washing 3 times, the
cell pellets were removed and counted in a gamma counter.
In the presence of increasing concentrations of the low affinity ligand
(IgG, concentration cll), the
binding of the high affinity ligand (125I-CLBFcgran-1 Fab,
concentration clh) to the cell
surface receptor (CD16) is gradually reduced or displaced. The
displaced fraction (F), defined as the bound fraction (f) of CLBFcgran-1 normalized by the value when no IgG was
present (f0), can be expressed by Equation 1.
Two-dimensional Binding Studies--
Two-dimensional kinetic
rates of CD16-IgG binding with both interacting molecules being
cell-bound were measured by using our recently developed micropipette
method (28). Briefly, a microscope chamber filled with 3 ml of
half-isotonic HBSS (Sigma) plus 1% BSA was injected with 1 × 103 receptor-expressing CHO cells and 1 × 104 ligand-coated red blood cells. A single CHO cell and a
single red blood cell were respectively aspirated by two apposing
micropipettes and aligned via micromanipulation (Fig.
2). The suction pressure was precisely
controlled by a pressure regulation system such that the unaspirated
portion of the red blood cell in the free state remained a spherical
shape (Fig. 2a) but was easily deformed by subpiconewton
forces acting at the apex. A computer program drove the piezo
translator on which the red blood cell pipette was mounted to move the
two cells into contact for a pre-determined area and duration (Fig.
2b). Upon the pipette retraction, the two cells either were
immediately separated (i.e. no adhesion, scored 0) or
remained bound with the red blood cell being elongated (Fig.
2c) for a short while before being detached by force
(i.e. adhesion, scored 1).
This adhesion test cycle was then repeated hundreds of times to obtain
an estimate for the adhesion probability from the running adhesion
frequency versus test cycle count data (Fig.
3a). For the reversible
binding (28) observed in the present study, the total adhesion
probability (Pt) can be simply estimated from the
average adhesion score, or adhesion frequency, calculated at the last
test (= number of adhesions/number of tests). The probability of
specific adhesion (Pa) was then calculated by
removing that of the nonspecific adhesion (Pn)
according to Pa = (Pt The Adhesion Probability Measured from the Micropipette Binding
Tests Was Specific--
Because the micropipette method is designed to
measure adhesion mediated by a low number (may even be just one)
receptor-ligand bonds, which amounts to forces as low as even a single
piconewton, it is important that the nonspecific adhesion level be low.
Fig. 4 exemplifies some of the control
experiments performed to ensure that binding curves such as those shown
in Figs. 3b and 5 were results
of specific CD16-IgG interactions. When RbIgG- or mIgG2a-coated red
cells were allowed to contact CHO cells expressing either CD16a-TM or
CD16a-GPI, frequent adhesions were detected (Fig. 4a). In
contrast, contacts of red cells coated with an irrelevant protein (BSA)
with the same CHO cells resulted in much less frequent adhesions.
Similarly, only rare adhesions to IgG-coated red cells were detected
when the CD16-transfected CHO cells were replaced by either
untransfected CHO cells ( The Two-dimensional Kinetic Rates Depended on the CD16 Membrane
Anchor and This Dependence Varied with Ligand--
The micropipette
method quantifies the dependence of adhesion probability on contact
time and densities of the receptors and ligands, as exemplified in Fig.
3b. To allow for direct visual comparison of the kinetic
rates and binding affinity, the mass action effect, manifesting
itself in Fig. 3b as an upward or downward shifting of
the curves depending on the receptor and ligand densities (mr and ml), must be eliminated.
This is achieved by a simple transformation of Equation 2 into
Equation 3.
The transformed binding curves are shown in Fig. 5 for each of the two
human CD16a isoforms interacting with IgG from each of the three
species. It is evident from Fig. 5a that for hIgG the
CD16a-GPI binding curve achieved a >2-fold higher level than the
CD16a-TM binding curve at steady state (contact time t > 10 s). To test further this result, two additional ligands,
IgGs from rabbit and murine, were examined, since they were known to interact well with human CD16. A similar trend was obtained when RbIgG
was tested (Fig. 5b). When mIgG2a was tested, however, an inverted result was observed. The CD16a-TM binding curve reached a
~7-fold higher equilibrium level than the CD16-GPI curve (Fig. 5c). The plateau level of the transformed binding curve
provides a direct measure for the binding affinity because the far
right-hand side of Equation 3 approaches
AcKa as t approaches infinity.
The values for the reverse rate constant can also be visually compared
from the transformed binding curves. It follows from Equation 3 that
kr is equal to ln2 divided by the time required for
the curve to reach half-maximum, t1/2, as shown in
Equation 4.
Whereas the transformation given by Equation 3 makes the effect of the
membrane anchor on kinetic rates and binding affinity readily visible
without analysis, fitting Equation 3 to the data (Fig. 5) allows for
quantitative evaluation of kf,
kr, and Ka. The values of these
two-dimensional kinetic properties are presented in Table
I, which quantitatively confirm the
visual observations.
Thus, the micropipette data revealed the membrane anchor of CD16a as a
major determinant of its kinetic properties; it altered the forward,
but not the reverse, rate constant and thereby also altered the binding
affinity. Significantly, this effect varied with the ligand species,
with the GPI-anchored CD16a having higher affinity for hIgG and RbIgG
but lower affinity for mIgG2a than its TM-anchored counterpart. This
"flipping" phenomenon is interesting and may be useful in
understanding the underlying mechanism (see "Discussion").
The CD16 Membrane Anchor Effect on Binding Affinity and Its Ligand
Dependence Was Also Found in Three-dimensional Binding
Studies--
The preceding section represents the first application of
the newly developed micropipette method for determining two-dimensional kinetic rates in a comparative study of the ligand-binding property of
a cell surface receptor. Although our method has been carefully validated (28), it is important to further verify it by comparing its
predictions with those derived from more established methodologies. For
this reason, three-dimensional binding studies were conducted to
address the following questions. Was the CD16 membrane anchor effect on
ligand binding seen in Fig. 5 a true indication of the structure-function relationship of the interacting molecules, a real
reflection of the two-dimensional experimental conditions, or a mere
artifact of the micropipette method?
The affinities of an anti-CD16 mAb CLBFcgran-1 in fluid-phase for both
CD16a-TM and CD16-GPI on CHO cells were first determined by Scatchard
analysis (Fig. 6). The membrane anchor
effect (in this case using the Fab fragment of CLBFcgran-1 as ligand)
again is apparent in Fig. 6, with the GPI-anchored CD16a exhibiting a
higher affinity than the TM-anchored CD16a.
Because CD16 is a low affinity receptor for monomeric IgG, measuring
CD16-IgG interactions by the Scatchard method is rather difficult and
results so obtained may not be reliable. We therefore adapted a
competitive inhibition protocol using nonlinear curve-fitting based on
Equation 1. Representative data comparing the two human CD16a membrane
anchor isoforms are presented in Fig. 7,
a CD16a- CD16b Behaved Differently from CD16a-GPI--
There have been
conflicting reports on whether the small differences in the ectodomain
of CD16 isoforms (Fig. 1, a and b) can influence
binding (23, 37). Comparison and interpretation of these data are often
obscured by other differences that were also present, such as the
membrane anchor and cellular background. To delineate the effect on
binding of the six amino acid substitutions and the loss (or gain) of
one glycosylation site in the extracellular domain of
CD16bNA1 (or CD16bNA2), their three-dimensional
affinities were compared with that of CD16a-GPI. All CD16 isoforms were
expressed on CHO cells to obtain the same cellular background.
Comparisons were made in side-by-side experiments using the same
preparation of reagents to minimize inter-experimental variations. The
results of these experiments are presented in Table II, which show that
both alleles of CD16b bound to hIgG and RbIgG with 2 orders of
magnitude lower affinities than CD16a-GPI.
CD16a-TM and CD16a-GPI Showed No Detectable Differential
Glycosylation--
A possible mechanism for the differing ligand
binding kinetics of the CD16a isoforms may be their differential
glycosylation. TM- and GPI-anchored molecules may have different
resident times in the Golgi and other processing apparatus, which may
affect their post-translational modifications. To investigate this
possibility, various forms of CD16 purified from CHO cells were
analyzed by SDS-PAGE (Fig. 8). The
glycosylation differences were evident in the three GPI-anchored CD16
molecules that have the same number of amino acids in their protein
backbone. The difference in the mobility of CD16bNA1 (47 kDa), CD16bNA2 (56 kDa), and CD16a-GPI (51 kDa) correlates
well with the number of glycosylation sites (4, 6, and 5, respectively,
cf. Fig. 1). The faster mobility of CD16a-GPI than CD16a-TM
(55 kDa) can be accounted for by the deletion of the TM and cytoplasmic
domains (~5.4 kDa, calculated from 45 amino acids) and the addition
of the GPI moiety (~1 kDa, calculated from fatty acid compositions) of the former isoform. These data suggest that there is no major difference in glycosylation between the CD16a-GPI and CD16a-TM.
Anti-CD16 mAb VEP13 Reacted Strongly with CD16a-TM but Weakly with
CD16a-GPI--
Another possible mechanism underlying the observed
anchor effect is a conformational difference between the two molecular isoforms. To test this hypothesis we screened a panel of anti-CD16 mAbs
using flow cytometry to see if any changes in expression of antigenic
epitopes could be detected. Among the mAbs tested, VEP13 reacted
differently to the CD16a isoforms. It exhibited a
concentration-dependent and saturable binding to CD16a-TM (Fig. 9, left column), and its
fluorescence histogram matched that of another anti-CD16 mAb 3G8 at the
saturation concentrations (Fig. 9, 1:2 and 1:10
dilutions). In contrast, not until its concentration reached the level
at which reactivity to CD16a-TM was saturated did VEP13 begin to show
some weak binding to CD16a-GPI (Fig. 9, right column), and
its fluorescence histograms displayed substantial leftward shifts from
those of 3G8 toward much lower levels of expression even at the highest
concentration tested (Fig. 9, 1:2 and 1:10
dilutions). Our limited VEP13 reagent (ascites) did not allow us to
measure its binding affinity for the CD16a isoforms. Nevertheless, Fig.
9 shows a qualitative trend opposite that seen in Fig. 6. Thus, the
dependence of the anchor effect on the molecule to which the CD16a
isoforms bind was seen not only with IgG ligands but also with mAbs
against CD16a.
Several other anti-CD16 mAbs, including CLBFcgran-1, recognized both
isoforms of CD16a in a manner similar to 3G8 (data not shown). Thus,
the VEP13 epitope on the ectodomain of CD16a was down-regulated after
its membrane anchor had been changed from TM to GPI. The weak
reactivity with CD16a-GPI was not due to the GPI anchor per
se, since VEP13 is known to react with both the TM-anchored CD16a
and the GPI-anchored CD16b, respectively, expressed on NK cells and
polymorphonuclear leukocytes (38). It can also inhibit binding of
immune complexes and 3G8 to CD16 and block rosette formation of
RbIgG-opsonized ox red blood cells with CD16-expressing leukocytes (39,
40). Monoclonal antibody 3G8 identifies the ligand-binding site of CD16
(41). Thus, the epitope recognized by VEP13 is likely to be proximal to
the ligand bind site of CD16a-TM.
A major goal of the present work was to compare the
two-dimensional binding kinetics and affinities of two human CD16a
membrane anchor isoforms for the Fc domain of IgG. Since adhesion of Fc receptor-expressing leukocytes to IgG-coated targets is an initiating step for many immune responses, the determination of CD16-IgG kinetic
rate constants is important in both biological and clinical settings.
Unlike typical hormone receptors that bind soluble ligands (i.e. three-dimensional binding kinetics), adhesion
receptors bind membrane-bound ligands (i.e. two-dimensional
adhesion kinetics). Clearly, it is the two-dimensional, rather than the
three-dimensional, kinetic rate constants that are most relevant to
physiological situations such as adhesion of a CD16a-expressing NK cell
or macrophage to an antibody-coated target cell. Although
two-dimensional adhesion kinetic rates are of obvious importance, their
determination has not been possible experimentally until very recently
due to a lack of methodology. Our micropipette method is one of only
three existing approaches in the literature available to measure
two-dimensional kinetic rates, compares favorably to the other two
flow-based methods (29-31), and offers several technical improvements
(28). The results summarized in Table I expand our original data on the
two-dimensional kinetic rates of CD16a-TM binding to hIgG and RbIgG
(28).
By applying this newly developed method, we attempted to address the
following biological questions. Did the membrane anchor (including the
associated subunits) influence the kinetic rates? If so, what might be
the mechanisms causing the observed changes? To address these
questions, we isolated the effects of the extracellular domain and the
membrane anchor using the lipid-anchored CD16a-GPI construct. CHO cell
transfectants were used to obtain a uniform cellular background across
the CD16 membrane isoforms expressed. Under these conditions, the
membrane anchor effect was clearly revealed by the micropipette
experiment (Fig. 5). To the best of our knowledge, this is the first
experimental demonstration of the membrane anchor effect on the kinetic rates.
We also measured three-dimensional binding for the same interacting
molecules (Fig. 7) in order to elucidate the mechanism underlying the
observed anchor effect. Because much work is available in the
literature on three-dimensional CD16-IgG interactions, comparison to
those helps validate our two-dimensional measurements and strengthen
our conclusions. Indeed, the same trends for affinities were seen in
both two-dimensional and three-dimensional measurements, indicating
that the micropipette method is adequate for measuring structure-function relationship of cell-bound receptors.
Comparison to Published Results--
Vance et al. (23)
reported a higher three-dimensional binding affinity for monomeric hIgG
of CD16a on NK cells (3-10 × 106
M Possible Role of the Associated Subunits--
Miller et
al. (26) suggested that the associated
However, we found that this effect was ligand-dependent.
For CLBFcgran-1, hIgG, and RbIgG, the trend inverted, with CD16a-TM having lower affinity than CD16a-GPI (Figs. 6 and 7, a and
b). These three-dimensional results were supported by those
of the two-dimensional micropipette experiments (Fig. 5), which
involved direct visualization of over 20,000 controlled single cell
pair adhesion tests. In addition, affinity measurements showed that the
chimeric molecules CD16a- The Difference in Ligand Binding Kinetic Rates and Affinity of CD16
Isoforms Cannot Be Explained by Their Differing Diffusivities--
The
GPI-anchored CD16 molecules (including both alleles of CD16b and the
CD16a-GPI) exhibit a few folds faster translational diffusion on the
CHO cell membrane than TM-anchored CD16a as determined by preliminary
fluorescence recovery after photobleaching measurements (data not
shown). In addition, the GPI anchor is likely to provide more
flexibility to the ectodomain of the receptor, which increases its
rotational diffusion coefficient, a parameter more relevant to
enhancing two-dimensional binding than the translational diffusion coefficient. One should therefore consider whether the faster binding
of CD16a-GPI than CD16a-TM to hIgG and RbIgG could be explained by the
diffusional difference of the two membrane anchor isoforms. However,
the following three lines of reasoning argue against this explanation
and allow us to rule it out as the cause for the different kinetic
rates of CD16 anchor isoforms.
First, faster diffusion of CD16a-GPI cannot explain the observed effect
of the GPI anchor on the forward, and the lack thereof on the reverse,
rate constants (Fig. 5 and Table I). Existing theories have shown that
diffusion influences kf and kr
similarly but not Ka = kf/kr, since the diffusion
effects on the two rates cancel each other in the ratio (44, 45).
Second, the anchor effect was seen not only in the two-dimensional
micropipette experiment, but also in the three-dimensional binding
assays (Figs. 6 and 7 as well as Table II) where identical but soluble
ligands and the same CD16-expressing CHO cells were examined. Diffusion
should not affect the three-dimensional results not only because it was
the ratio Ka, not kf or
kr separately, that was measured but also because
diffusion is unlikely to be the rate-limiting step in the two-step
binding process (the other step being intrinsic reaction). The
diffusivity of proteins in fluid phase (>10 µm2/s) is
usually orders of magnitude greater than cell surface proteins (45).
Finally, the diffusion mechanism cannot explain the inversion of the
anchor effect; the faster diffusing CD16a-GPI bound with slower forward
rate and lower affinity to mIgG2a than CD16a-TM (Figs. 5c
and 7c as well as Tables I and II). This negative
correlation between diffusion coefficient and forward rate/binding
affinity provides direct and definitive experimental proof for the
inability of the differing diffusivities to account for the CD16a
anchor effect on kinetic rates and affinity.
We should point out that in the micropipette experiment accumulation of
receptors in the contact area by lateral diffusion is unlikely. The
diffusion coefficients for various CD16 isoforms on CHO cells are of
the order of 0.01 µm2/s (data not shown). The longest
contact time in the micropipette experiments was 20 s, which was
far from sufficient for the receptors to accumulate in an apparent
contact area of ~3 µm2. Furthermore, only a few bonds
were formed in an adhesion produced by the controlled contact in the
micropipette experiments (28), which is a negligibly small number
comparing to the hundreds and thousands of receptors in the contact
area. This will not generate any appreciable density gradient of free
receptors to drive them to diffuse into the contact area.
The Difference in Ligand Binding Kinetic Rates and Affinity of CD16
Isoforms Cannot Be Explained by Their Differing Orientations and
Lengths--
The lack of TM and cytoplasmic domains as well as the
associated subunits of the CD16a-GPI may alter the orientation of its extracellular domain. Moreover, the GPI moiety may extend the Fc
binding epitope further outward relative to the glycocalyx (Fig. 1).
The length of a receptor has been demonstrated to influence its ability
to support adhesion at 4 °C (but the effect diminished at higher
temperatures) (46) and under flow (but not static) conditions (47). It
is thought that a longer and more flexible molecule can explore larger
space above the membrane and assume more spatial configurations. This
lengthens the interaction range of the receptor, thereby facilitating
its effort to find the ligand when it is surface-linked (48). However,
although both orientation and length can influence ligand binding
kinetic rates and affinity, this effect should be qualitatively
monotonic for all ligands. Therefore, our observation that the GPI
anchor increases affinity for human and rabbit IgGs but decreases
affinity for murine IgG2a allow us to exclude orientation and length as
possible causes for the anchor effect.
The Difference in Ligand Binding Kinetic Rates and Affinity of CD16
Isoforms Cannot Be Explained by Their Differential Distribution and
Clustering--
Some GPI-anchored proteins have been suggested to be
clustered in glycosphingolipid and cholesterol-enriched domains,
e.g. caveolae (49). CD16a-GPI might appear to bind better
than CD16a-TM in the three-dimensional assay should the former isoform
be functionally clustered, since binding of aggregated soluble ligands
to receptor clusters might result in an apparently higher avidity.
Similarly, being distributed in different membrane domains might
potentially influence the two-dimensional binding properties of
CD16a-GPI. The forward rate kf and binding affinity Ka measured from the micropipette assay are lumped
with the contact area Ac. The true or functional
contact area Ac was not measured but should be
proportional to the apparent contact area directly visible under the
light microscope (Fig. 2b) (28). The CHO cell surface
displays extensive roughness; thus only the "hills," not
"valleys," of the membrane folds are likely to be part of the
functional
Ac.3 Since
the same CHO cells were used to express CD16a regardless of the anchors
and the same red blood cells were used to present IgG regardless of the
species, Ac would be a constant if the apparent
contact area was kept constant, as was the case in all of our
experiments. Thus, not knowing the value of Ac or of
which membrane microdomains it was composed should not affect conclusions based on relative comparisons, provided that the two CD16a
membrane anchor isoforms were similarly distributed in the contact
area. However, while some adhesion molecules (e.g.
Although the arguments in the preceding paragraphs may seem consistent
with the binding pattern seen in the human and rabbit IgG experiments,
they cannot explain the inversion of that trend observed in the mIgG2a
experiment. The differential distribution and clustering of receptors
should not flip when IgGs from different species were used as ligands
to assay kinetics. Moreover, differential distribution of GPI- and
TM-anchored CD16a should not affect the three-dimensional results,
since the soluble ligands should be able to access any membrane
domains, and the calculation is based on average measurements over the
entire cell surface, not particular compartmentalized domains. We thus
conclude that the differences in ligand binding kinetic rates and
affinity of CD16a isoforms are not due to their differential
distribution and clustering.
No Major Differential Glycosylation of CD16a Isoforms Can Account
for the Anchor Dependence of Ligand Binding Kinetic Rates and
Affinity--
GPI-anchored proteins may have different resident time
and may hence be processed differently in the Golgi apparatus than TM-anchored proteins, resulting in different carbohydrate
modifications. However, analysis of CD16 isoforms purified from CHO
cells using SDS-PAGE did not reveal major differences in
N-linked glycosylation. The ~4-kDa higher molecular mass
of CD16a-TM than CD16a-GPI (Fig. 8) can be well accounted for by the 45 amino acid TM and cytoplasmic domains (~5.4 kDa) of the former
isoform and the GPI moiety (~1 kDa) of the latter isoform. Of course,
more extensive analysis is required to test whether site-specific
differential glycosylation exists between the two CD16a membrane anchor
isoforms and, if so, whether it is the cause of the observed anchor
effects on kinetic rates and binding affinity.
Could the Difference in Ligand Binding Kinetic Rates and Affinity
of CD16 Isoforms Be Explained by Their Differing Conformations--
A
consequence of the possible concentration of CD16a-GPI in
glycosphingolipid-enriched membrane microdomains may be its being surrounded by different neighboring molecules. It is conceivable that
neighboring molecules of CD16a-GPI could affect its ligand binding. But
to affect binding in one way with hIgG, RbIgG, and mAb CLBFcgran-1 but
in an opposite way with mIgG2a and mAb VEP13 would most likely require
CD16a-GPI to associate with the neighboring molecules. Such an
association would most likely have to be in sufficiently close
proximity to render a conformational change of the receptor, resulting
in variable accessibility by different ligands and mAbs. So far, only
the myeloid cell-specific integrin
Finally, an alternative and perhaps simpler hypothesis may be that the
differing membrane anchors themselves, when they are inserted into the
cell surface, yield such a conformational difference. The view that
different conformations of the two CD16a isoforms may be the mechanism
underlying their different kinetic rates and binding affinity is
supported by the following resemblance between our observations and
those commonly accepted as valid evidence for conformational change in
integrins. Certain "activation-reporter" mAbs bind integrins only
after they have been converted from resting to activated states, which
is interpreted as a conformational change that allows for expression of
antigenic epitopes that are specific to the activated conformer
(e.g. see Ref. 52 and references cited therein). Another
characteristic of conformational changes of integrins is changes in
their abilities to bind soluble ligands and to mediate cell adhesion.
Here we have also identified a mAb (VEP13) that reacts strongly with
CD16a-TM but only weakly with CD16a-GPI, suggesting that the epitope
detected by mAb VEP13 is substantially down-regulated after the
molecule's anchor has been changed from TM to GPI. In addition, the
abilities to bind soluble ligands (and the mAb CLBFcgran-1) and to
mediate cell adhesion are different for the two CD16a membrane
isoforms, which are caused by their different kinetic rates and binding
affinities for ligands (and for mAb CLBFcgran-1). Similarly, Kukulansky
et al. (53) reported that following anchor cleavage by
phospholipase C, the reactivity of the solubilized Thy-1 with several
mAbs is lost, and its reactivity with polyclonal anti-Thy-1 antibodies
is markedly decreased. These authors interpreted their finding by a GPI
anchor-dependent conformational change of the Thy-1
molecule. Thus, our data strongly suggests that replacement of
polypeptide anchor with GPI anchor resulted in a conformational change
of CD16a.
The diversity of Fc receptors in both structure and function has long
been appreciated (22). The ability of Fc receptors to bind ligand has
been shown to be influenced by a variety of structural variations. In
this study we measured both two-dimensional kinetic rates and
three-dimensional affinities of Fc We thank Dr. R. P. McEver for carefully
reading the manuscript and providing helpful suggestions.
*
This work was supported in part by National Science
Foundation Grant BCS 9350370, National Institutes of Health Grant
AI38282 (to C. Z.), and National Institutes of Health Grant AI30631
(to P. S.).The costs of publication of this
article were defrayed in part by the
payment of page charges. The article
must therefore be hereby marked
"advertisement" in
accordance with 18 U.S.C. Section
1734 solely to indicate this fact.
§
Supported in part by National Institutes of Health Training Grant GM08433.
2
P. Li, S. Nagarajan, C. Zhu, and P. Selvaraj,
manuscript in preparation.
3
T. E. Williams, S. Nagarajan, P. Selvaraj,
and C. Zhu, submitted for publication.
The abbreviations used are:
TM, transmembrane;
Fc
The Membrane Anchor Influences Ligand Binding Two-dimensional
Kinetic Rates and Three-dimensional Affinity of Fc
RIII (CD16)*
§,,
George W. Woodruff School of Mechanical
Engineering and Department of Biomedical Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332-0363 and
¶ Department of Pathology and Laboratory Medicine, Emory
University School of Medicine, Atlanta, Georgia 30322
ABSTRACT
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DISCUSSION
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receptor III (CD16) interacting with IgG when the two
molecules were bound to apposing cellular membranes. CD16 is one of
only four eukaryotic receptors known to exist natively in both the
transmembrane (TM, CD16a) and glycosylphosphatidylinositol (GPI, CD16b)
isoforms. The biological significance of this anchor isoform
coexistence is not clear. Here we showed that the anchor influenced
kinetic rates; compared with CD16a-TM, CD16a-GPI bound faster and with higher affinities to human and rabbit IgGs but slower and with lower
affinity to murine IgG2a. The same differential affinity patterns were
observed using soluble IgG ligands. A monoclonal antibody bound
CD16a-GPI with higher affinity than CD16a-TM, whereas another
monoclonal antibody reacted strongly with CD16a-TM but weakly with
CD16a-GPI. No major differential glycosylation between the two CD16a
isoforms was detected by SDS-polyacrylamide gel electrophoresis
analysis. We suggest a conformational difference as the mechanism
underlying the observed anchor effect, as it cannot be explained by the
differing diffusivity, flexibility, orientation, height, distribution,
or clustering of the two molecules on the cell membrane. These data
demonstrate that a covalent modification of an Ig superfamily receptor
at the carboxyl terminus of the ectodomain can have an impact on ligand
binding kinetics.
INTRODUCTION
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ABSTRACT
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EXPERIMENTAL PROCEDURES
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DISCUSSION
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receptor III (FcRIII or CD16). Neural cell
adhesion molecule mediates Ca2+-independent homophilic
adhesion during the development of neurons with the GPI-anchored
isoform being expressed later in development than the TM-anchored
isoform (9, 10). Lymphocyte function-associated antigen 3 is expressed
on human erythrocytes as a GPI-anchored protein but on all nucleated
cells as both membrane anchor isoforms (11-13). Vascular cell adhesion
molecule 1 has been found with both anchors in murine but not in human
cells (14, 15). The TM-anchored FcRIIIa (CD16a) is expressed on
macrophages, natural killer (NK) cells, and subsets of monocytes and T
cells. The GPI-anchored FcRIIIb (CD16b) is only expressed on
neutrophils (16-21). The physiological significance of this
coexistence of two distinct membrane anchor isoforms for the same
receptor is not clear.
chain of the Fc
receptor or the 
chain of the T cell receptor, which form a homo-
(- or -) or hereto (-)-dimer in complex with CD16a
(Fig. 1). Binding of antigen-constrained IgGs brings about
cross-linking of CD16, which can trigger a variety of immune functions,
including immune complex clearance, phagocytosis,
antibody-dependent cellular cytotoxicity, release of
inflammatory mediators, and enhancement of antigen presentation.
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Fig. 1.
Schematic of CD16 isoforms.
a, two CD16a membrane anchor isoforms. b, two
CD16b alleles. c, two CD16a-subunit chimeras. The
extracellular domains, which begin at amino acid 18, are depicted as
two Ig-like globules with the glycosylation sites shown as
sticks. The amino acids in the ectodomain that differ among
the various molecules are listed. Comparing to those in CD16a, the
different amino acids in CD16b are underlined, and the
differences between the two CD16b alleles are shown in
italics. The lost glycosylation site in CD16bNA1
due to the change Ser-65 
Asn-65 is indicated by 
*,
whereas the gained glycosylation site in CD16bNA2 due to
the change Asp-82 Asn-82 is indicated by +*. The
CD16a-TM and the two CD16a-subunit chimeras differ by the TM and
cytoplasmic domains but have the same ectodomain and anchor to the cell
surface via the same TM mechanism. By comparison, the CD16a-GPI and the
two CD16b alleles use a lipid tail to insert into the outer leaflet of
the bilayer but do not cross the membrane. The GPI moiety is of similar
size to an Ig globule. Hence it may extend the membrane-proximal
ligand-binding Ig domain further above the membrane. It may also alter
the orientation and/or conformation of the molecule.
chain of CD16a.
or subunit might play.
EXPERIMENTAL PROCEDURES
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EXPERIMENTAL PROCEDURES
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DISCUSSION
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subunit of rat Fc
RI, the two chimeric CD16A- and
CD16B-, and the hygromycin or neomycin resistance genes have been
described (24).
, or CD16a-, as well as control CHO cells (untransfected,
K1, and transfected to express integrin
IIb
3, A5, a generous gift of Dr. Mark H. Ginsberg, Scripps Research Institute, La Jolla, CA) were cultured as
described previously (24, 28). The expression of various forms of CD16
on the CHO cells was periodically checked via flow cytometry.
-glucoside (for CHO cells
expressing GPI-anchored CD16). The cell lysates were immunoprecipitated
with CLBFcgran-1-Sepharose 4B and run on SDS-PAGE under nonreducing conditions.
Since the affinity to CD16 of 125I-CLBFcgran-1 Fab
(Kah) and the receptor
concentration (cr) were predetermined from a
separate experiment by Scatchard analysis, the only unknown in Equation 1 is the affinity of IgG (Kal).
Therefore, Kal can be calculated
from a single measurement of F without the experimental
displacement curve to include data at the IC50 point. To
increase the accuracy of the Kal
value, however, the predicted displaced fraction (Equation 1) was
nonlinearly fit to the entire F versus
cll data set.
![F≡f/f<SUB>0</SUB>={(c<SUB>r</SUB>+c<SUP>h</SUP><SUB>l</SUB>+c<SUP>l</SUP><SUB>l</SUB>K<SUP>l</SUP><SUB>a</SUB>/K<SUP>h</SUP><SUB>a</SUB>+1/K<SUP>h</SUP><SUB>a</SUB>)−[(c<SUB>r</SUB>+c<SUP>h</SUP><SUB>l</SUB>+c<SUP>l</SUP><SUB>l</SUB>K<SUP>l</SUP><SUB>a</SUB>/K<SUP>h</SUP><SUB>a</SUB>+1/K<SUP>h</SUP><SUB>a</SUB>)<SUP>2</SUP>−4c<SUB>r</SUB>c<SUP>h</SUP><SUB>l</SUB>]<SUP>1/2</SUP>}/2c<SUB>r</SUB>](/content/vol275/issue14/fulltext/10235/img001.gif)
(Eq. 1)
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Fig. 2.
Photomicrographs (and an overlay drawing) of
a typical adhesion test. A hIgG-coated red blood cell aspirated by
a micropipette, left, and a CD16a-TM-expressing CHO cell
also aspirated by another micropipette (only partially shown),
right. a, the unaspirated portion of the red
blood cell is shown in its free, spherical shape. b, the red
blood cell was pushed onto the CHO cell with an overall apparent
contact diameter of approximately 2 µm. The contact area and time
between the CHO cell and red blood cell were carefully controlled.
c, a red blood cell that previously contacted the CHO cell
was being retracted. It was elongated by the adhesion force, allowing
binding to be unambiguously detected despite the fact that there was
only ~10 pN force acting at the single attachment point at the apex.
d, overlay drawing of the photomicrographs shown in
b-c. The bars represent 5 µm.
Pa)/(1 Pn). Pa relates to the receptor and ligand densities
(mr and ml, respectively), the
contact area (Ac), and the contact time
(t) via Equation 2 (28).
where mr and ml were
measured independently by a separate experiment (above) and
Ac was kept constant throughout. The contact time
was kept constant in each sequential adhesion test series conducted
using a single cell pair to allow for measurement of
Pa that corresponded to that t. In different test series using different cell pairs, t was
systematically varied over a range. These data were then fit to
Equation 2 (Fig. 3b) using a
![P<SUB>a</SUB>=1−<UP>exp</UP>{<UP>−</UP>m<SUB>r</SUB>m<SUB>l</SUB>A<SUB>c</SUB>K<SUB>a</SUB>[1−exp(<UP>−</UP>k<SUB>r</SUB>t)]}](/content/vol275/issue14/fulltext/10235/img002.gif)
(Eq. 2)
-squared error minimization
method that returned the two-dimensional binding affinity
(Ka) and reverse rate (kr).
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Fig. 3.
Measurement of adhesion probability.
a, representative binding evolution curves. Repeated
micropipette adhesion tests were conducted using a single pair of CHO
and red cells. For each test, the cumulative adhesion scores up to that
test were divided by the most recent test number to calculate the
running adhesion frequency at that test cycle. Plotting running
frequency against the cycle number gives rise to a binding evolution
curve. For the reversible binding seen in the present study, a
characteristic binding evolution curve fluctuates when the test cycle
count is small, reflecting the inherent random nature of small number
receptor-ligand binding but stabilizes as the test number becomes
sufficiently large, perhaps >50. This allows the adhesion probability
to be estimated from the running adhesion frequency at large test
counts. Data from three sequential adhesion test series are shown with
the same contact time (10 s). The bottom curve results from
an IgG-free BSA-coated red blood cell touching a CD16a-TM-expressing
CHO cell, which illustrates the nonspecific binding background. The
other two curves result from two RbIgG-coated red blood cells
respectively interacting with a CHO cell expressing either CD16a-TM or
CD16a-GPI (indicated). While the corresponding densities for the
receptors and ligands (mr and ml)
were different for the two curves (indicated), the
mr × ml products were comparable
(2.4 and 2.2 × 105 µm 
4 for the
CD16a-TM and CD16a-GPI curves, respectively). According to Equation 2,
the higher curve indicates a greater binding affinity than the lower
curve, since the adhesion probability increases with
Ka when t and mr × ml are kept constants, as were in the present case.
b, measured (points) and predicted
(curves) adhesion probability as a function of the contact
time and ligand density. Specific adhesion probability (after
background removal) Pa was measured at 1, 2, 5, 10, and 20 s using CHO cells expressing 1250 molecules/µm2 of CD16a-TM. Two batches of red blood cells
were used, with RbIgG-coating densities 190 (diamonds) and
360 (squares) molecules/µm2, respectively. The
dependence of Pa on t and
ml is apparent. Equation 2 was simultaneously
fit to both data curves using a 2 minimization method
that returned the values of the two fitting parameters,
AcKa and kr
(Table I). Data are presented as mean ± S.E. of 2-4 cell pairs
of 50-200 tests for each pair to ensure the adhesion probability
measured at each point is estimated from no less than 400 adhesion
tests.
RESULTS
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EXPERIMENTAL PROCEDURES
RESULTS
DISCUSSION
REFERENCES
) or CHO cells transfected with an
irrelevant receptor (integrin IIb3).
Binding was inhibited by preincubation of the CD16a-expressing CHO
cells with the anti-CD16 adhesion-blocking mAb CLBFcgran-1 or by
preincubation of the RbIgG-coated red cells with the soluble competitor
sCD16a (Fig. 4b). In comparison, binding was unaffected by
preincubation with an irrelevant mAb X63 or an irrelevant soluble
protein sB7. Blocking experiments performed for the mIgG2a ligand
yielded results similar to Fig. 4b (not shown). These data
confirm our previous control experiment for hIgG (28) and collectively
demonstrate the specificity of the adhesion probabilities measured from
the micropipette binding tests.
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Fig. 4.
Demonstration of binding specificity.
a, the adhesion probability varied with the presence or
absence of the interacting molecules on the apposing cell surfaces.
When CD16a-TM or CD16a-GPI was expressed on the CHO cells along with
RbIgG (solid bars) or mIgG2a (hatched bars)
coated on the red blood cells (RBC), high adhesion
probabilities were observed. In contrast, when either no receptor ( )
or an irrelevant receptor (IIb3) was
expressed on the CHO cells, low adhesion probabilities were observed
for the same IgG coating on the red cells. Similarly, when an
irrelevant protein (BSA, open bars) was coated on the red
blood cells, the adhesion probability was reduced to low levels for the
same CD16a-expressing CHO cells. b, the adhesion could also
be inhibited by treating the cells with blocking agents. The addition
of the conditioned media from hybridoma secreting anti-CD16 mAb
CLBFcgran-1 (~10 µg/ml antibody) greatly reduced the adhesion
probability. Similarly, the addition of the conditioned media of
soluble CD16a secreting CHO cells (~10 µg/ml sCD16a) decreased the
adhesion probability to low levels. In contrast, conditioned media from
hybridoma secreting an irrelevant mAb X63 and of CHO cells secreting an
irrelevant soluble molecule B7 did not block binding. Each
bar in a and b represents mean ± S.E. of data from 2 to 4 series of 50-200 tests each at a contact time
of 5 s. In these control experiments the densities of CD16a-TM and
CD16a-GPI were not matched, nor were those of RbIgG and mIgG2a.
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Fig. 5.
Transformed binding curves of CD16a-TM- and
CD16a-GPI-expressing CHO cells interacting with red blood cells coated
with IgGs from human (a), rabbit (b),
and mouse (c). The adhesion probability data,
exemplified in Fig. 3b, were transformed according to
Equation 3 (the logarithm of the reciprocal of 1 Pa was taken and divided by mr
and ml) and then plotted against the contact time
t. By normalizing the ordinate with respect to
mr and ml, the family of curves
corresponding to the same pair of interacting molecules but different
densities of receptors and ligands collapse. Furthermore, the
dependence of binding affinity Ka and reverse rate
kr on molecular identity becomes apparent. The
higher plateau level indicates a greater Ka; and a
shorter half-time (t1/2, indicated) reflects a
faster kr. The theoretical predictions (Equation 3,
curves) were fit to each data set (points, mean ± S.E.) to
evaluate the Ka and kr values,
which are listed in Table I. The number of cell pairs examined were 43, 17, 20, 22, 11, and 12 for the CD16a-TM-hIgG, CD16a-GPI-hIgG,
CD16a-TM-RbIgG, CD16a-GPI-RbIgG, CD16a-TM-mIgG2a, and CD16a-GPI-mIgG2a
curves, respectively. Each cell pair was repeatedly tested 50-200
times to obtain a stable adhesion probability estimate.
It is evident that the far right-hand side of Equation 3 depends
only on the binding affinity (Ka) and the reverse rate constant (kr) of the interacting molecules,
provided that the contact area (Ac) is kept
constant, as in the present study. Thus, this transformation collapses
a family of Pa versus t curves
for the same receptor-ligand pair into a single curve on the
transformed ordinate. Only when different interacting molecules are
tested will the transformed binding curves shift, allowing for a direct
visualization of the distinct Ka and
kr values.
![y≡(m<SUB>r</SUB>m<SUB>l</SUB>)<SUP><UP>−</UP>1</SUP><UP>ln</UP>(1−P<SUB>a</SUB>)<SUP><UP>−</UP>1</SUP>=A<SUB>c</SUB>K<SUB>a</SUB>[1−<UP>exp</UP>(<UP>−</UP>k<SUB>r</SUB>t)]](/content/vol275/issue14/fulltext/10235/img003.gif)
(Eq. 3)
From the data shown in Fig. 5, the t1/2 values
appear to be comparable for the two CD16a isoforms interacting with the
same IgG species, suggesting their similar reverse rate constants.
(Eq. 4)
Summary of two-dimensional CD16a-IgG kinetic rates and affinities
2 error minimization method to
the data shown in Fig. 5 panels a-c for hIgG, RbIgG, and mIgG2a,
respectively. The S.E. were calculated from the data variance.
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Fig. 6.
Scatchard analyses of binding of
125I-labeled CLBFcgran-1 Fab to CHO cell transfectants
expressing CD16a-TM (squares) and CD16a-GPI
(circles). After subtracting background binding,
saturation binding curves were converted to bound/free CLBFcgran-1 as
functions of the bound CLBFcgran-1 concentration (Scatchard plot).
Points are experimental data, and the solid lines are least
squares linear fits to the two data sets. The slope of each line equals
the negative of the three-dimensional binding affinity,
Ka, whereas the x intercept provides a
measure for the receptor density. Three experiments were performed
side-by-side for CHO cells expressing the two CD16a membrane isoforms;
and the results shown are representative. The mean and S.D. of all
measured values are listed in Table II.
c, using hIgG, RbIgG, and mIgG2a as respective
competitors. It is evident from Fig. 7, a and b,
that CD16a-GPI had higher affinities than CD16a-TM for hIgG and RbIgG,
as the same ligand concentration inhibited the CLBFcgran-1 binding to a
greater extent. The fact that CLBFcgran-1 bound with a higher affinity
to CD16a-GPI than CD16a-TM (Fig. 6) should lead to less, rather than
more (as was the case), inhibition by hIgG or RbIgG. The situation in
Fig. 7c is less obvious. The lower curve in this
panel corresponds to CD16a-TM, which has a lower affinity than
CD16a-GPI for CLBFcgran-1 (Fig. 6). Hence it would be easier to be
inhibited even if mIgG2a were to bind both CD16a membrane isoforms with
the same affinity. However, fitting Equation 1 to the data in Fig.
7c revealed that CD16a-TM indeed had a higher affinity than
CD16a-GPI for mIgG2a, as was in the two-dimensional case. The
three-dimensional affinity results are summarized in Table
II.
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Fig. 7.
Competitive inhibition curves for determining
the binding affinities of CD16a-TM and CD16a-GPI for IgG of three
species. CHO cells expressing CD16a membrane isoforms were allowed
to bind 125I-labeled CLBFcgran-1 Fab in the presence of
varying concentrations of hIgG (a), RbIgG (b), or
mIgG2a (c). Points are data presented as mean ± S.D.
of triplicate wells, whereas each curve is a 2 fit to
that set of data using Equation 1. By fitting the entire curve,
multiple data points (each of which by itself can be used to calculate
a Kal) are utilized in the
calculation of the fitting parameter, the three-dimensional binding
affinity of CD16a for IgG, Kal.
The Kal values so obtained should
be more accurate and reliable, as they reflect the average prediction
from all data points. This also allows for determination of
Kal without competition to the
IC50 point, as in the CD16a-GPI curve in c. For
each IgG ligand, at least two experiments were performed in parallel
with two CD16a membrane isoforms to minimize inter-experimental
variations; the data shown are representative. The mean and S.D. of the
Ka values of each ligand to both CD16a-TM and
CD16a-GPI are listed in Table II.
Summary of three-dimensional CD16-ligand affinities
2 error
minimization method. Data represent the mean ± S.D. of
(N) experiments. ND, not determined.
and CD16a- Chimeras Behaved Similarly to
CD16a-TM--
In contrast to CD16a-GPI which is expressed on the CHO
cell surface by itself, CD16a-TM has to be coexpressed and complexed with a - dimer (Fig. 1a). It has been suggested that
the associated chain could enhance the ligand binding affinity of
Fc receptor (26). To define further the role of the chain, we
performed parallel experiments to measure the three-dimensional
affinities for various ligands of two chimeric molecules, CD16a- and
CD16a-. The chimeras were created by fusing the extracellular domain
of human CD16a with the TM and cytoplasmic domains of either the rat
chain or the human chain (Fig. 1c). The results of
these experiments are presented in Table II. It can be seen that the chimeric CD16a- and CD16a- behaved similarly to the TM-anchored CD16a but differently from its GPI-anchored counterpart.
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Fig. 8.
SDS-PAGE analysis of CD16 isoforms. CD16
was immunoprecipitated from lysates of 125I-surface-labeled
CHO cells stably transfected to express CD16bNA1
(lane 1), CD16bNA2 (lane 2), CD16a-TM
(lane 3), and CD16a-GPI (lane 4) using
CLBFcgran-1-Sepharose 4B and run on SDS-PAGE under nonreducing
conditions. Molecular mass markers are indicated (lane 5).
The experiment was repeated three times with similar results.
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Fig. 9.
Fluorescence histograms of CHO cells
expressing CD16a-TM (left) or CD16a-GPI
(right). Cells were stained for flow cytometry
using anti-CD16 mAbs 3G8 (thick curves), VEP13 (shaded
areas), or an irrelevant mAb X63 (thin curves). The
concentrations for each row were the indicated dilution of culture
media from hybridoma secreting 3G8 or of ascites containing VEP13. The
secondary antibodies were FITC-conjugated anti-mouse IgG (for 3G8) or
anti-mouse IgM (for VEP13) goat polyclonal antibodies. Experiments were
repeated several times, and the results shown are representative.
DISCUSSION
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DISCUSSION
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1) than of CD16b on neutrophils (value not
determined). The former is therefore referred to as an intermediate
affinity receptor, whereas the latter is considered as a low affinity
receptor. It was not demonstrated, however, to what degree this
differing affinity was due to the differences in the extracellular
domain, in the membrane anchor, or in the cellular background of the
two membrane isoforms. On the other hand, Tamm et al. (37)
reported a similar three-dimensional avidity of CD16a-GPI and
CD16bNA2, both expressed on a human embryonic kidney cell
line 293, for heat-aggregated hIgG1 (~20 × 106
M1). These authors suggested that the minor
differences in the ectodomains of the two molecules (Fig. 1,
a and b) would not affect their affinity,
although their data showed an ~2-fold higher avidity of CD16a-GPI
than CD16b for dimeric hIgG1 (3.7 and 2.1 × 106
M1, respectively). We found that in our CHO
cell system using hIgG and RbIgG, CD16a-GPI showed consistently higher
affinity than CD16a-TM in both two-dimensional (Table I) and
three-dimensional (Table II) binding studies. Furthermore, repeated
side-by-side experiments reproducibly showed that the order of
three-dimensional affinities for hIgG, hIgG1, and RbIgG was
CD16a-GPI > CD16a-TM > CD16bNA1 ~ CD16bNA2 (Table II and data not shown). The discrepancies
in the absolute values of three-dimensional affinities measured by the
different laboratories might be due to cell type-specific glycosylation of CD16 (25). However, our data were obtained using the same CHO cells
to express various CD16 membrane isoforms. Our results appeared to
indicate that in comparison to CD16a, the variations in the
extracellular domains of the two CD16b alleles (Fig. 1, a
and b), although very small, did significantly reduce their affinities for hIgG and RbIgG (Table II) and for hIgG1 (data not shown). This is not surprising, as two independent single nucleotide polymorphisms (resulting in amino acid changes Leu-48 to Arg-48 or
His-48 and Val-176 to Phe-176) in CD16a have been reported to alter the
affinity of IgG binding (42, 43). The CD16a-TM and CD16a-GPI molecules
used in the present study do not have these polymorphic variations
(27), as confirmed by sequencing during subcloning of the CD16a-TM
cDNA into the pcDNA3 vector.
chain could enhance the
ligand binding affinity of FcR. They reported at least an order of
magnitude higher three-dimensional affinity of CD16a-TM than CD16a-GPI
for mIgG2a. The affinity of CD16a-TM for mIgG2a obtained by these
authors (12 × 106 M1) is
much higher than our value, which may be due to the differences in the
cells and/or the chain used. Miller et al. (26) used monkey kidney COS cells to express transiently human CD16a-TM in
association with human chain. By comparison, we used stably transfected CHO cells to express human CD16a-TM in association with rat
chain. Furthermore, Miller et al. (26) employed direct Scatchard analysis, whereas we used indirect competitive inhibition to
measure affinity. Nevertheless, we found the same trend as Miller
et al. (26) that CD16a-TM had higher affinity than CD16a-GPI for mIgG2a (Fig. 7c and Table II).
and CD16a- bound similarly to CD16a-TM
but differently from CD16a-GPI for all ligands tested, including IgGs
from three species and a mAb (Table II). Thus, our findings indicate
that the role of the associated subunit, if it is indeed the cause of
the anchor effect, is not limited to the chain but also includes
the chain. Moreover, their putative role is not to enhance, but
rather to alter, the ligand binding affinity.
2 integrins) are more or less uniformly distributed on
the cell surface, others (e.g. L-selectin) are known to
localize on the microvillous tips (50). Because the latter molecules
are present at higher densities on Ac, their
kf and Ka values will be
overestimated by a calculation that assumes a uniform molecular
distribution. Thus, CD16a-GPI would appear to bind better than CD16a-TM
should CD16a-GPI be differentially enriched on the hills (accessible
area) or CD16a-TM be differentially enriched on the valleys
(inaccessible area) of the membrane folds.
M2 has
been reported to associate with the GPI-anchored CD16b (51). CHO cells
do not express 2 integrins. By comparison, in order for
it to be expressed on the CHO cell surface CD16a-TM must be associated
with the chain (Fig. 1a). Such an association has been
shown to alter the binding of not only CD16a but also CD64 (Fc
receptor I) (26).
RIII membrane isoforms for various
ligands, and we showed that the membrane anchor had an effect on these
binding properties, which is likely caused by a conformational
difference between the two CD16a membrane isoforms. These findings
provide insights into the biological significance of distinct anchors
of cell surface proteins.
ACKNOWLEDGEMENT
FOOTNOTES
To whom correspondence should be addressed: George W. Woodruff
School of Mechanical Engineering and Dept. of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0363. Tel.: 404-894-3269; Fax: 404-894-2291; E-mail:
cheng.zhu@me.gatech.edu.
ABBREVIATIONS
RIII, Fc receptor III;
GPI, glycosyl phosphatidylinositol;
h, human;
m, murine;
NA1 and NA2, neutrophil antigen 1 and 2;
NK, natural
killer;
Rb, rabbit;
mAb, monoclonal antibody;
FITC, fluorescein
isothiocyanate;
CHO, Chinese hamster ovary;
PBS, phosphate-buffered
saline;
PAGE, polyacrylamide gel electrophoresis.
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ABSTRACT
INTRODUCTION
EXPERIMENTAL PROCEDURES
RESULTS
DISCUSSION
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