(Received for publication, May 4, 1995)
From the
The kinetic binding characteristics of four Bacillus
thuringiensis CryI insecticidal crystal proteins to a Cry-binding
protein, purified from Manduca sexta brush-border vesicles,
were analyzed by an optical biosensor. This 120-kilodalton binding
protein, previously determined to be aminopeptidase N, was converted to
a 115-kilodalton water-soluble form by removing the attached
glycosylphosphatidylinositol anchor with phospholipase C. The
solubilized form recognized the three major subclasses of CryIA toxins
but not CryIC even though all four CryI proteins are toxic to larvae of M. sexta. CryIA(a) and CryIA(b) toxins bound to a single site
on the solubilized aminopeptidase N molecule whereas CryIA(c) bound to
two distinct sites. Apparent kinetic rate constants were determined for
each binding reaction. All three CryIA toxins exhibited moderately fast
on rates (10
M
s
) and a slow reversible off rate
(
10
s
). Although the second
CryIA(c)-binding site retained a moderately fast association rate, it
was characterized by a rate of dissociation from the aminopeptidase an
order of magnitude faster than observed for the other CryIA-binding
sites. CryIA(c) binding to both sites was strongly inhibited in the
presence of N-acetylgalactosamine (IC
= 5
mM) but not N-acetylglucosamine, mannose, or glucose.
CryIA(a) and CryIA(b) binding were unaffected in the presence of the
same sugars. Our results serve to illustrate both the complexity and
the diverse nature of toxin interactions with Cry-binding proteins.
During sporulation the Gram-positive bacterium Bacillus thuringiensis produces a variety of intracellular insecticidal crystal proteins in the form of crystalline inclusions. These inclusions, when ingested by susceptible insects, are solubilized in the alkaline environment of the insect midgut where the protoxins undergo enzymatic conversion to the active toxin form by resident gut proteases(1) . After activation, CryI toxins have been shown to bind to specific high affinity receptors on the surface of the midgut epithelial cell layer(2, 3) . Independent studies have shown that B. thuringiensis toxins are able to form ion channels in susceptible cultured insect cells at low concentrations (4) or in artificial lipid bilayers in the complete absence of receptors at high toxin concentrations(5, 6) . Perturbation of the intracellular ionic homeostasis created by these ion channels is thought to ultimately result in cell death by lysis(7) .
Integral to our understanding of the toxin mode
of action is the study of toxin interactions with their specific
binding proteins. Most of the in vitro studies characterizing
these proteins to date have utilized brush border membrane vesicles
(BBMVs) ()purified from the gut epithelium of susceptible
insects (8) or immunochemical staining of midgut
sections(9) . Characterization of toxin-binding sites on BBMVs
from a variety of larvae has revealed highly complex patterns of toxin
binding. The existence of separate distinct classes of toxin-binding
proteins as well as single binding sites capable of recognizing
multiple toxins has been clearly demonstrated by numerous competition
and ligand blotting
studies(3, 10, 11, 12, 13, 14, 15, 16, 17) .
However, the presence of multiple binding proteins on the surface of
BBMVs, high levels of nonspecific binding, and inherent toxin
integration into the membrane have made the interpretation of brush
border binding results rather difficult and have led to some very
complex interaction models(3, 17, 18) . In
one notable case, a colony of Trichoplusia ni, having
developed resistance to CryIA(b) by laboratory selection, failed to
show resistance to CryIA(c) although in vitro binding studies
demonstrated they share the same binding site(17) . These
results suggest that binding site predictions based on BBMV studies do
not necessarily correlate with in vivo toxicity. Clearly,
studies on isolated toxin-binding proteins would be crucial in
resolving the relationship between binding sites and binding proteins.
Recently, three independent sources have described the purification of
a 210-kilodalton (kDa) CryIA(b) (19, 20) and a 120-kDa
CryIA(c) (21, 22) toxin-binding protein from Manduca sexta. The CryIA(c)-binding protein was functionally
determined to be aminopeptidase N (21, 22) whereas the
CryIA(b)-binding protein was reported to share sequence similarity with
the cadherin family of glycoproteins(20) .
In this study, using surface plasmon resonance (SPR), we provide the first detailed kinetic analysis of the interaction between three B. thuringiensis CryIA toxin subclasses and a solubilized form of the 120-kDa CryIA(c)-binding protein purified from M. sexta. SPR is an optical detection technique which allows direct interaction analysis between a ligand immobilized on a modified dextran sensor chip and a specific analyte in a continuous flow system(23) . The reactants are monitored in real time without the use of labels thus permitting the determinations of kinetic rate constants, binding affinities, and binding site characterization(24, 25, 26) . SPR has been used previously to determine the kinetics of CryIA binding to BBMVs from both the spruce budworm (Choristoneura fumiferana) and the diamondback moth (Plutella xylostella)(27, 28) . In this report, we present a detailed kinetic analysis of CryIA toxin binding interactions with a specific membrane protein purified from the brush border of the lepidopteran larva M. sexta. Detailed evidence is presented showing that two toxins, CryIA(a) and CryIA(b), recognize a single binding site on the purified 115-kDa protein and that CryIA(c) binds to two binding sites. Furthermore, we show that the latter binding is selectively inhibited by the amino sugar N-acetylgalactosamine.
To determine k, eight different toxin concentrations
ranging from 50 to 1500 nM were injected over immobilized APN.
As with the k
determination, all data
were fitted to either a one-site (A+B ⇔ AB) or a two-site
model (A+B1+B2 ⇔ AB1+AB2). Whenever possible, fitted
lines from either model were compared to each other using an F-test
comparison. All models were verified by residual plots which calculate
the difference between the observed and the fitted curves for each data
point. To determine the goodness of fit of the data to the model, both
association and dissociation rate constants were chosen from
sensorgrams producing a chi
value less than one. All models
used in our kinetic evaluations were further evaluated by lag plots to
determine the relationship of neighboring data points thus providing
additional support for binding model selection (data not shown).
Figure 1:
SDS-PAGE and ligand blot analyses of
solubilized BBMVs and purified 115-kDa protein. Stained SDS-PAGE (A) and protein blot (B) probed with I-CryIA(c). Lane 1 represents the molecular mass
markers (indicated in kilodaltons). Lane 2 contained
solubilized M. sexta BBMV proteins and lane 3 purified 115-kDa protein.
Figure 2: Stoichiometric analysis of CryIA binding. Saturating levels of CryIC, CryIA(a), or CryIA(b) (1500 nM) and of CryIA(c) (1000 nM) were injected at 5 µl/min over a surface of immobilized 115-kDa CryIA(c)-binding protein representing approximately 1000 RU. At the end of the injection, toxin flow was replaced by buffer alone and the sensorgram allowed to continue for an additional 100 s to demonstrate the rate of complex dissociation.
To confirm the number of toxin-binding sites and
determine the kinetic rate constants, different toxin concentrations
were injected over a surface of immobilized 115-kDa ligand. The binding
data from each curve were fitted by non-linear least-squares fitting to
either a one- or two-site model. The model fitting further suggested
that CryIA(a) and CryIA(b) bound to a single site on the immobilized
ligand whereas CryIA(c) best fit a two-site model (F-test comparison
between the two models gave a probability = 1). The possibility
that the second observed CryIA(c) dissociation rate was caused by the
rebinding of toxin to immobilized aminopeptidase was eliminated since a
plot of the log of the drop in response against time interval produced
a linear rather than a curved response, a normal indicator of
rebinding. ()Residual plots (i.e. a plot of the
difference between the observed and the calculated response for each
data point) of the dissociation segment of the response curves were
created to verify the appropriateness of the binding model chosen. As
shown in Fig. 3, A and B, the data point
distribution is random around the x axis and the signal noise
is no greater than background (±2 RU) thus indicating that the
quality of fit was good for CryIA(a) and CryIA(b) to a single binding
site model. The
values, testing for the
goodness-of-fit, for all the sensorgrams from the different toxin
concentrations were <1.0. Fig. 4shows a fitting of the
CryIA(c) data to a one-site (A) and a two-site (B)
model. The distribution of points for fitting of the data to a one-site
model is clearly not random and is greater than background noise
suggesting a poor fit. All
values for the one-site
model were found to be >1.0. If the data are fitted to a model which
accounts for two separate toxin-binding sites on the ligand, the point
distribution becomes random indicating a good fit. Furthermore, all
values obtained from the different sensorgrams fall
below 1.0 providing additional support for the two-site hypothesis.
Residual plots performed on data from the association area of the
sensorgram were similar to those shown above for the dissociation
segments (data not shown).
Figure 3: Dissociation rate residual plots for CryIA(a) and CryIA(b). Residual plots, representing the randomness of data point distribution around a fitted curve, are shown for a typical binding data set taken 40 s after the start of complex dissociation for CryIA(a) (A) or CryIA(b) (B) when applied to a one-site model (A+B ⇔ AB). In the Response plot, the actual dissociation data points are represented by a solid line and the fitted curve by a dashed line. In the Residual plot the response differences (residuals) in RU of the fitted line around the dissociation data are represented by solid dots. A zero difference reference line was added to help visulize the randomness of point distribution.
Figure 4: Dissociation rate residual plots for CryIA(c). A residual plot of a typical CryIA(c) binding data set applied to a one-site model (A+B ⇔ AB) is shown in panel A, and applied to a two-site model (A+B1+B2 ⇔ AB1+AB2) shown in panel B. As indicated in Fig. 3, the data set was taken 40 s after the start of complex dissociation. In the Response plot, the actual dissociation data points are represented by a solid line and the fitted curve by a dashed line. In the Residual plot the response differences (residuals) in RU of the fitted line around the dissociation data are represented by solid dots. A zero difference reference line was added to help visulize the randomness of point distribution.
A summary of the apparent rate constants
is shown in Table 1. Each toxin studied displayed a moderately
fast association rate showing at most a 5-fold variation. The most
interesting differences were found in the various dissociation rates.
In general, all the E. coli produced NRD-12 toxins showed
similar k values; however, the second
CryIA(c)-binding site demonstrated a much faster rate (
an order of
magnitude) of toxin-receptor complex dissociation than that calculated
for the other CryIA toxins. Furthermore, the B. thuringiensis produced CryIA(c) toxin, although showing k
rates indistinguishable from the E. coli produced
CryIA(c), demonstrated a 2-fold faster dissociation rate for both sites
than the E. coli produced toxin. Despite the observed
variations in kinetic rates, the three CryIA toxins from NRD-12
essentially share the same affinity for the immobilized aminopeptidase
with CryIA(c) also binding to a second site at a lower affinity.
Figure 5: Co-injection of CryIA toxin pairs. Saturating levels of CryIA(a) or CryIA(b) were injected either alone or together over 1000 RU of immobilized aminopeptidase (A). Saturating levels of CryIA(b) or CryIA(c) were injected either alone or together over a similar surface (B).
Figure 6: Inhibition of CryIA(c) binding by N-acetylgalactosamine. A 150 nM stock solution of CryIA(c) was preincubated with various concentrations of N-acetylgalactosamine for 30 min followed by injection over a low density surface (1000 RU) of APN. The maximum binding level was determined and corrected for refractive index changes caused by the amino sugar, and the level of inhibition when compared to the response in the absence of sugar preincubation was plotted as a function of amino sugar concentration.
An essential step in the mode of B. thuringiensis toxin action is the recognition and binding to high affinity sites
on the intestinal brush border surface of susceptible insects. Much of
our current knowledge of toxin-receptor interactions has been based on
studies using vesicles purified from this tissue; however, numerous
factors such as multiple Cry-binding proteins, radiolabeling of toxins,
and the inherent ability of B. thuringiensis Cry toxins to
integrate into lipid bilayers serve to complicate interpretations of
binding data from these vesicles. The recent purification and
functional identification of a CryIA(c) toxin-binding protein (21) provided a unique opportunity to assess how insecticidal
toxins of B. thuringiensis specifically interact, at the
molecular level, with a single binding protein in the absence of these
complicating factors. This 120-kDa aminopeptidase is normally anchored
in brush border membranes of M. sexta by a
glycosylphosphatidylinositol anchor(36) . When solubilized by
detergents, this form of the protein is tightly complexed with four
other proteins. Therefore, in order to purify the CryIA(c)-binding
protein to homogeneity, removal of the anchor by a phospholipase C was
required. To establish that the removal of this
glycosylphosphatidylinositol linkage did not affect the binding
characteristics of this protein, CryIA toxin interactions with the
purified complex were examined. Preliminary evidence showed that all
three CryIA toxins can also bind to the immobilized complex suggesting that the solubilized form of the aminopeptidase does
not show any gross alterations in its binding properties.
In this
study we used an optical biosensor approach which eliminated the
additional complication of using labeled toxin while permitting the
measurement of toxin binding in real time. Using four different toxins
known to display toxicity toward M. sexta(40) , we
show that three CryIA toxins but not CryIC specifically recognized and
bound to APN. Two separate lines of evidence involving stoichiometric
binding data and non-linear fitting of binding data clearly
demonstrated the presence of a single binding site on the
aminopeptidase for either CryIA(a) or CryIA(b) toxins whereas binding
by a third toxin, CryIA(c), best fitted a two-site model. The fact that
multiple toxins bind to a single molecule is not necessarily surprising
as Cry toxins tend to be very similar in overall structure despite a
large disparity in amino acid composition as recently demonstrated by
Grochulski et al.(48) with CryIA(a) (41) and
CryIIIA(42) . In contrast to our results, CryIA(b) binding to
the 120 kDa protein (glycosylphosphatidylinositol-linked APN) was not
observed by Vadlamudi et al.(19) in ligand blots of
BBMVs prepared from M. sexta. Instead, binding of CryIA(b) to
a 210-kDa protein band was observed. Moreover, two reports (20, 43) suggested that the 210-kDa protein is the
binding protein, noted by van Rie et al.(3) , which
binds all three CryIA toxins. Unfortunately, with the exception of
CryIA(b), this speculation was based solely on ligand blots and not
quantitative kinetic or affinity binding data. In light of our results,
this discrepancy presumably reflects either the limitations of using
denatured membrane proteins with the ligand blotting technique or an
even more complex toxin binding pattern than that originally proposed
by van Rie et al.(3) . CryIA(c) bound to one site on
the APN molecule with an affinity essentially identical to the
calculated K values determined for both
CryIA(a) and CryIA(b). However, CryIA(c) bound to a second site on the
same molecule at a 3-4-fold lower affinity. The kinetics of this
lower affinity site were interesting from the viewpoint that the lower
affinity was due primarily to a faster dissociation rate especially
since the rate of complex association of the lower affinity site was
actually higher (more than 3-fold faster) than measured for the high
affinity site. This faster dissociation rate was even more pronounced
for the CryIA(c) protein produced by the HD-73 strain of B.
thuringiensis rather than by E. coli indicating that
subtle differences in the primary structure of Cry toxins from the same
subgroup may exert a direct effect on binding rates. The NRD-12
CryIA(c) protein used in our studies has four amino acid differences
with the HD-73 CryIA(c) sequence as described by Adang et
al.(44) . Three of these differences are localized to
Domain II, the specificity domain, with two relatively conservative
amino acid substitutions (L366F and F439S) and one drastic change,
which is the deletion of a negatively charged residue (Asp) at position
442(45) . Additional experiments using E.
coli-produced HD-73 toxin should be performed to eliminate the
possibility that the differences are due to expression in different
hosts rather than sequence differences. However, since only the
dissociation rate was affected, it is reasonable to assume that
sequence differences rather than some undetermined host factor like
toxin glycosylation (46) is the primary reason for the altered
dissociation rates.
The affinity constants for the three CryIA
toxins determined by SPR varied substantially both in overall affinity
and in relation to each other when compared to values derived from M. sexta BBMVs using either displacement or homologous
competition experiments(3) . Vesicle binding experiments showed
that CryIA(a) and CryIA(c) share similar K values in the 0.2-0.4 nM range with CryIA(b)
showing a 3-5-fold lower affinity. Our results show that these
three toxins share similar affinities to the solubilized aminopeptidase
and that the K
values are approximately
two orders of magnitude higher than those determined by van Rie et
al. (3). We cannot say, at this stage, exactly which factor(s)
between the two methods are responsible for the observed differences,
but the presence of other biomolecules, particularly lipids, may be
partly responsible. The recent affinity determination of CryIA(c) for
BBMVs from P. xylostella using SPR(28) , which
produced a K
only 2-fold higher than the
value determined by equilibrium binding using labeled CryIA(c) and P. xylostella BBMVs(47) , is consistent with this
hypothesis and shows that the different techniques can produce similar
results.
Competition studies are a limitation of the SPR technique unless one molecule is small and exerts little or no shift in refractive index. In our SPR experiments the optical biosensor could not discriminate between different toxin classes; therefore, we relied on an alternative approach using total binding. In all binary combinations studied, an additive effect was never observed suggesting that the toxins shared a common site. However, considering the size of the toxin molecules in comparison to the toxin-binding protein, one cannot rule out the blocking of separate unique sites on the molecule due to steric hindrance. Because two sites were observed during CryIA(c) binding, it is probable that these two sites are further away from each other. On the other hand, since an additive effect was not observed using CryIA(c) in combination with either of the other CryIA toxins, it is possible that one site is shared whereas the second CryIA(c) site is unique to CryIA(c). An alternative explanation as to why two sites are observed during CryIA(c) binding is that two ligand populations are present (one with and one without a sugar moiety). Therefore, there would only be one binding site normally, but it may or may not be occupied by a sugar molecule. The fact that CryIA(c) toxin binds twice as much as the other two suggests that this is not the case and that there is only one ligand population. Furthermore, both CryIA(c) sites were inhibited by the amino sugar. To eliminate the idea that the 150 nM toxin concentration preferentially showed the high affinity site only, therefore concealing the possibility that the low affinity site was not inhibited, the inhibition experiments were repeated at saturating levels of CryIA(c) toxin. Since no overt differences to the lower toxin concentration were observed (data not shown), it is reasonable to assume that the ligand population was homogeneous in possessing two N-acetylgalactosamine-sensitive sites.
The existence of multiple N-acetylgalactosamine-sensitive CryIA(c)-binding sites on the same molecule may provide an explanation for the observed resistance in the T. ni colony described by Estada and Ferrè(17) . These authors showed that although CryIA(b) and CryIA(c) compete for the same binding site, resistance to CryIA(b) did not automatically translate into resistance to CryIA(c). If CryIA(c) binding to T. ni is also N-acetlygalactosamine sensitive as is the case with M. sexta and other insects(14, 37, 38) , alterations in a CryIA(b)-binding site may not necessarily affect CryIA(c) binding. Alternatively, if the shared site was indeed altered for both toxins, the existence of a second N-acetylgalactosamine CryIA(c)-binding site on the same molecule could account for continued CryIA(c) sensitivity.
So far, with only one exception, carbohydrate inhibition of toxin binding has only been observed with CryIA(c). In accordance with this observation, our results clearly show that in the case of M. sexta, N-acetylgalactosamine is a component of the 115-kDa binding protein. The recognition of this amino sugar occurs only with CryIA(c) thus illustrating the broad heterogeneity of toxin receptors. The combination of having multiple sites on a single receptor molecule and separate receptors for the same toxin may well account for the difficulty in developing insect resistance to B. thuringiensis.