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Article
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Electrostatic Potential on Human Leukocyte Antigen: Implications for Putative Mechanism of Chronic Beryllium Disease James A. Snyder,1 Ainsley Weston,1,2 Sally S. Tinkle,1 and Eugene Demchuk1,3 1Health Effects Laboratory Division, National Institute for Occupational
Safety and Health, Centers for Disease Control and Prevention, Morgantown,
West Virginia, USA; 2Department of Plant and Soil Science,
West Virginia University, Morgantown, West Virginia, USA; 3School
of Pharmacy, West Virginia University, Morgantown, West Virginia, USA Abstract The pathobiology of chronic beryllium disease (CBD) involves the major histocompatibility complex class II human leukocyte antigen (HLA) . Although occupational exposure to beryllium is the cause of CBD, molecular epidemiologic studies suggest that specific HLA-DPB1 alleles may be genetic susceptibility factors. We have studied three-dimensional structural models of HLA-DP proteins encoded by these genes. The extracellular domains of HLA-DPA1*0103/B1*1701, *1901, *0201, and *0401, and HLA-DPA1*0201/B1*1701, *1901, *0201, and *0401 were modeled from the X-ray coordinates of an HLA-DR template. Using these models, the electrostatic potential at the molecular surface of each HLA-DP was calculated and compared. These comparisons identify specific characteristics in the vicinity of the antigen-binding pocket that distinguish the different HLA-DP allotypes. Differences in electrostatics originate from the shape, specific disposition, and variation in the negatively charged groups around the pocket. The more negative the pocket potential, the greater the odds of developing CBD estimated from reported epidemiologic studies. Adverse impact is caused by charged substitutions in positions ß55, ß56, ß69, ß84, and ß85, namely, the exact same loci identified as genetic markers of CBD susceptibility as well as cobalt-lung hard metal disease. These findings suggest that certain substitutions may promote an involuntary cation-binding site within a putatively metal-free peptide-binding pocket and therefore change the innate specificity of antigen recognition. Key words: chronic beryllium disease, gene-environment interactions, genetic marker, genetic susceptibility, HLA-DP, human leukocyte antigen, lung, sensitization. Environ Health Perspect 111:000-000 (2003) . doi:10.1289/txg.6327 available via http://dx.doi.org/ [Online Online 11 August 2003] |
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Address correspondence to E. Demchuk, NIOSH, MS-3030, 1095
Willowdale Rd., Morgantown, WV 26505 USA. Telephone: (304) 285-5869. Fax:
(304) 285-6041. E-mail: edemchuk@cdc.gov
We thank A. Wlodawer and G. Vriend for helpful discussions.
Public availability of WHATIF program is gratefully acknowledged.
Authors declare they have no conflict of interest.
Received 11 March 2003; accepted 11 August 2003.
Chronic beryllium disease (CBD) is a seriously debilitating, life-threatening
occupational disease that occurs primarily among workers engaged in beryllium
(Be) extraction and purification and preparation of Be metal and its alloys
(Kolanz 2001; Powers 1991). High-risk, high-exposure job categories include
machining and ceramics production (Kreiss et al. 1997). In addition, certain
downstream users are also known to be at risk, for example, dental technicians
who file or grind prostheses containing Be alloy (OSHA 2002). In addition, Be
delivered on clothing or contaminated motor vehicles could constitute a risk
to family members not actually employed in the Be industry (Sanderson et al.
1999).
CBD is generally recognized to be preceded by immunologic sensitization to
the metal, a phenomenon shared with conditions related to a number of metals
like gold, silver, nickel, vanadium, cobalt, chromium, and copper (Budinger
and Hertl 2000; Lawrence and McCabe 2002). Skin sensitization to Be (BeS) was
recognized 50 years ago, and a Be-lymphocyte proliferation test (BeLPT) was
developed in the 1970s. Surveillance programs, using the BeLPT, identify approximately
10% of Be workers who are not symptomatic but who are sensitized. Be-sensitized
individuals may progress to CBD, and approximately 2-5% of all Be workers
develop CBD (Maier 2001).
The recognition that CBD appeared to affect a subset of exposed workers and
included an immunologic component suggested a genetic basis for susceptibility
to BeS and CBD. Anti-human leukocyte antigen (HLA) antibodies blocked the
Be-specific proliferation response in lymphocytes of workers sensitized to Be
(Saltini et al. 1989). Subsequently, a molecular epidemiologic study implicated
HLA-DPB1 encoding a glutamic acid in position 69 of the mature protein
with susceptibility to CBD (Richeldi et al. 1993).
Figure 1. Class II MHC antigen-binding
pocket. Model shown is for the HLA-DPA1*01031/B1*1701 genotype. |
Recent studies have confirmed that genetic susceptibility to CBD involves HLA
glycoproteins, T-cell receptor clonality, and have suggested the involvement
of tumor necrosis factor polymorphisms (Tinkle et al. 2003). These HLA glycoproteins
are heterodimers comprising an -chain
and a ß-chain, which form an antigen-binding moiety. Binding of the antigen
occurs in an elongated groove on the surface of this complex (Figure 1). The
floor of the groove is composed of ß-sheets, and the walls of *-helices
(Li et al. 2000).
By September 2003, 106 different HLA-DPB1 alleles had been described,
65 encoding lysine (K) at codon 69, 5 encoding arginine (R), and 36 encoding
glutamic acid (E) (EBI 2003; Marsh 2003). Data from molecular epidemiologic
studies that used high-resolution allele-specific DNA-sequencing technology
preliminarily suggest an HLA-DPB1 allele hierarchy (Wang et al. 1999).
Although numbers of study subjects are small, we have hypothesized that inheritance
of certain alleles conveys more risk than others, with the following order:
HLA-DPB1*1701 (most risk) > *0601 > *0901 = *1001 > *1301
> *1901 = *0201 > *0401 (Rossman et al. 2002; Saltini et al. 2001;
Tinkle et al. 2003; Wang et al. 1999, 2001).
To investigate the basis for the suggested interindividual genetic susceptibility
to CBD, we constructed three-dimensional structural models of HLA-DP representing
several distinct allotypes and calculated the electrostatic potential on the
surface of these models. The allotypes were predicated upon two types of -chain
sequences and five types of ß-chain sequences. The -chain
sequences encoded by HLA-DPA1*0103 and *0201 alleles, and ß-chains
encoded by HLA-DPB1*1701, *0201, *1901, *1301, and
*0401 alleles were selected, allowing for 10 possible analytic models.
Materials and Methods
Molecular Modeling There are no experimentally determined coordinates for HLA-DP in the Protein
Data Bank (PDB; http://www.rcsb.org/pdb/).
We modeled the extracellular part of HLA-DP by homology to a known HLA species.
Overall, the primary, secondary, and tertiary structure in the class II family
of major histocompatability complex (MHC) is well conserved. We have selected
one HLA-DR, PDB entry 1FV1 (HLA-DRA*0101/B5*0101; Li et al. 2000)
as a template. Our choice was based on a relatively good resolution of the template
compared with other HLA entries (1.90 Å, R-factor = 0.233), the
total number of crystallographically resolved residues, and the results of a
sequence alignment using the GCG program (Accelrys, Inc., San Diego, CA).
The SYBYL 6.6 molecular modeling software (Tripos, Inc., St. Louis, MO) was
used to make modifications to the HLA-DR template. It was first necessary to
repair several residues in the template, in which coordinates of some side-chain
(SC) atoms were missing. Next, specific SC substitutions were introduced, which
converted the sequence of HLA-DR protein to that of HLA-DP. The deletion of
two residues in the gap required reconstruction of one loop region. After addition
of all hydrogen atoms, positions of the selected SC atoms and the backbone (BB)
of the engineered loop were subjected to 50 iterations of the steepest descent
minimization, followed by an exhaustive minimization using the Powell method
(Powell 1977), until the full convergence by the energy gradient was reached.
For minimization, the Kollman all-atom force field (Weiner et al. 1984) with
distance-dependent dielectric and a cutoff radius of 10 Å was used. All
BB and Cß (except proline Cß) atoms were held
fixed by treating them as a rigid body aggregate. Stereochemical quality of
the HLA-DP model was tested using the WHATIF program (Vriend 1990). Modeled
coordinate sets are available from the authors upon request.
Electrostatic Calculations The electrostatic potential was calculated using the DelPhi module of the
Insight II program (Accelrys). Insight II was also used to generate the isopotential
surface maps. The electrostatic potential was calculated by numerically solving
the linearized Poisson-Boltzmann equation using a finite difference method.
The low-dielectric macromolecule (protein interior) was embedded in a high-dielectric
continuum environment (water exterior). A solution with charged ions was simulated
having an assigned ionic strength of 0.145, which typifies conditions at the
physiologic pH. Using an ionic radius of 2.0 Å for the solvated ions,
a Stern layer was constructed around the solvent-accessible surface, having
a null ionic strength inside, which determines the maximum distance that an
ion can approach the solvent-accessible surface. The assigned temperature was
298 K. The resulting system was discretized on a grid, and the potential at
the grid points was solved iteratively starting from the Debye-Hückel boundary
conditions. To improve the accuracy of calculated potentials, we used a method
of grid focusing. This involved three additional calculations in which the molecule
was allowed to occupy a successively larger fraction of the cubic grid volume
so that values of the potential at the grid boundary could be calculated using
the larger grid from each preceding calculation. The grid dimensions were selected
to be 1,200 Å (spacing 4 Å/grid point), 600 Å (spacing 2 Å/grid
point), 300 Å (spacing 1 Å/grid point), and 100 Å (spacing
0.75 Å/grid point). The interior and exterior dielectric constants were
4 and 80, respectively. The solute boundary was defined by a solvent-accessible
surface generated by a rolling probe sphere of 1.4 Å radius. Partial atomic
charges and radii were taken from the PARSE (Sitkoff et al. 1994) parameter
set, which was originally optimized for proteins by fitting the solvation free
energies of amino acid analogs.
Estimation of the Odds of Adverse Health Outcome Associated with Specific
HLA-DPB1 Haplotypes
in Beryllium Workers It was suggested that susceptibility to CBD conferred by different HLA-DPB1*E69 haplotypes is not equal. In Be-exposed workers inheritance of certain HLA-DPB1*E69 haplotypes might convey greater susceptibility than others (Wang et al. 1999).
Initially, we examined the data they reported and attempted to deduce allele-specific
risk estimates (McCanlies et al. 2003; Tinkle et al. 2003). Because the number
of cases was small (n = 20), we further pooled these data with data from
several more-recent studies (Rossman et al. 2002; Saltini et al. 2001; Wang
et al. 2001). A simple allelic hierarchy was developed based on the proportional
allelic frequencies reported for CBD, Be-sensitized, and controls. Odds ratios
(ORs) were calculated using the Mantel-Haenzel estimate (SAS Institute, Inc.
1999) of the common relative risk for each allele (HLA-DPB1*0201, *0401,
*0601, *0901, *1001, *1301, *1601, *1701,
and *1901) by analyzing each study population individually and by pooling
data from all studies.
Results
In this study we investigated a putative link between electrostatic properties
of HLA-DP allotypes and corresponding ORs of epidemiologically observed susceptibility
to CBD. The molecular electrostatic potential (MEP) represents the electrostatic
properties of a molecule, quantum-mechanical effects notwithstanding. Stretching
around the molecule, the MEP comprehensively describes intermolecular electrostatic
interactions, which often have a significant impact on ligand binding.
The electrostatic potential around the extracellular part of the HLA-DP protein
was calculated using computer-generated homology models of HLA-DP allotypes.
The calculations were performed in the absence of antigenic peptides, which
for HLA-DP are not yet known. Further, these calculations did not take into
account the phospholipid membrane responsible for anchoring the protein. Because
the antigen-binding groove and the transmembrane domain of class II MHC are
located a relatively large distance apart (approximately 60 Å) at the
opposite ends of the extracellular part of the complex, the effect of the membrane
on the electrostatic potential in the vicinity of the binding groove was assumed
to be similar in different HLA-DP allotypes. Thus, the present analysis focuses
on the distinctions in electrostatic potential on different HLA-DP allotypes.
Figure 2. Sequence alignment
of (A) HLA-DPA1*0103 with a-chain of HLA-DR2 (PDB: 1FV1A) and (B) of HLA-DPB1*1701
with b-chain of HLA-DR2 (PDB: 1FV1B). Residues in bold indicate mismatches. |
Figure 3. Histogram of WHATIF
scores for rotamer check of a- and b-chains of HLA-DPA1*0103/B1*1701 model.
Bin = 0.01 |
The homology models of HLA-DP allotypes were generated from the X-ray coordinate
set of the HLA-DR protein, Brookhaven code 1FV1. The amino acid sequence alignments
between the corresponding chains of 1FV1 and HLA-DP are shown in Figure 2. The
sequence alignment yielded approximately 64% identity in a 181 amino acid overlap
between 1FV1A and the HLA-DPA1 sequences, and 65% identity in a 190 amino acid
residue overlap between 1FV1B and the HLA-DPB1 sequences. There was a single
two-residue gap in the alignment of ß-chains, beginning at position 24,
and there were no gaps at all in the alignment of the -chains.
Overall, the results of sequence analysis indicate a high homology between the
three-dimensional structure of 1FV1 and HLA-DP. Usually, the high structural
homology between two protein species implies an unequivocal reconstruction of
unknown species from the coordinates of a known template.
Further validation of the derived structural models was conducted using the
WHATIF program (Vriend 1990), and the coordinates of homology models were subjected
to a number of quality control tests. To predict the probabilistic likelihood
for the particular rotamer of a given SC in the model, the distribution of the
*1 torsion angles of the model was compared with a distribution of
rotamers in the WHATIF database. Scores between 0 and 1 were computed, in which
a value close to 1 implies high likelihood for that rotamer. A histogram of
the scores collected for all residues in both the *- and ß-chains is presented
in Figure 3. In general, for most crystal structures subjected to this test,
less than 10% of residues have scores lower than 0.4. Only three residues in
the -chain scored below 0.4, and there were no scores in any of the chains
lower than 0.3. These results agree with expectations arising from a comprehensive
analysis of the PDB database (Vriend G. Personal communication).
An additional WHATIF check involved looking at the fine-packing quality for
both the homology model for HLA-DPA1*0103/B1*1701 and the 1FV1 protein. Quality
control values for individual residues are intended to measure the fit of a
residue in the particular part of the structure. If the z-score for the protein
is greater than -2.5 (2.5 standard deviations less than the mean), then
the structure is acceptable or good. A z-score less than -3 suggests that
the structure likely is poor. For all BB-SC contacts, the z-scores were -2.78
for the 1FV1 protein, and -2.87 for the model structure. For all BB-BB
contacts, the z-scores were 0.38 and 0.62 for 1FV1 and model, respectively.
It appears that reconstruction of the loop region in the model produced an improvement
in quality with respect to BB-BB contacts, as this was the only modification
made to the BB of the 1FV1 template. The z-scores for all types of contacts
(BB-BB, BB-SC, SC-BB, and SC-SC) were -1.52 and -1.27 for the 1FV1
and model, respectively, indicating an overall improvement in the packing quality
for the homology model compared with the template.
As a final WHATIF check, a torsion angle evaluation was performed. The computed
scores indicate, for each residue, how normal the torsion angles are, as described
by normality scores using all torsion angles except *. Average values and standard
deviations are extracted from residues in the WHATIF database and used to compute
z-scores. A residue with a z-score less than -2.0 is poor, indicating that
more than one torsional angle is in a highly unlikely position. For 1FV1, a
total of 5 residues (3 in the -chain and 2 in the ß-chain) were listed
as poor. For the HLA-DPA1*0103/B1*1701 model, a total of seven residuals were
listed as suspicious (2 in the -chain and 5 in the ß-chain). Residues
having forbidden *-* combinations are listed, along with residuals with unusual
* angles, which are angles that deviate by more than 3* from the normal value.
It is typical to find that approximately 5% of the residues in a structure have
unusual *-* combinations. There were 17 residues (out of 369 in the structure)
listed with poor scores for the 1FV1 protein, and 22 residues (out of 367 in
the structure) listed for the model. The *1/*2 correlation
z-score indicates the quality of correlation between *1 and *2 angles. Both values are considered acceptable. Thus, all quality control tests
performed on the structures indicate a high likelihood of the derived homology
model.
Figure 4. Sequence alignment
of (A) HLA-DPA1*0103 with HLA-DPA1*0201 and (B) HLA-DPB1*1701 with HLA-DPB1*1901,
*0201, *1301, and *0401. Underlined residues represent substitutions that
contribute a change in sign of charge depending on the identity of the residue
in that position. |
Table
1 |
Figure 5. Statistical odds of
(A) conferring CBD and (B) BeS versus formal charge on the b-chain of HLA
allotypes. |
The initial assessment of electrostatics-related effects was carried out using
the primary structures of HLA-DP allotypes. Figure 4 represents the amino acid
sequence alignments of commonly found HLA-DP alleles. Epidemiologic studies
have not provided convincing evidence for an association between and susceptibility
to CBD of -chain
alleles of HLA-DP (Figure 4A). Nevertheless, they are required for reconstruction
of the three-dimensional structure of the extracellular part of MHC class II
complex. Some ß-chain alleles have also been surveyed. Figure 4B represents
the ß-chain alleles for which epidemiologic association data were available
at the time of the study.
Except for the residue *50 (Figure 4A), all substitutions involving ionizable
residues are confined in a relatively short stretch of the ß-chain alignment
(Figure 4B). These are residues ß55-ß56, ß69, and ß84-85.
From the analysis of the three-dimensional model, it appears that all residues
involved in the substitutions affecting the charge are spatially adjacent to
the groove (Figure 1). Table 1 lists the total charge on the ß-chains
encoded by the HLA-DPB1 alleles. The variation in total charge between
the different alleles is determined by the presence of acidic (-1) or basic
(+1) substitutions in the three aforementioned regions. For example, comparing
*0401 and *1701 allotypes, the substitution of Asp ß55, Glu ß56,
Asp ß84, and Glu ß85 for Ala ß55, Ala ß56, Gly ß84,
and Gly ß85 produces a difference of -4e, and substitution
of Glu ß69 for Lys ß69 contributes a -2e difference,
making *1701 six units more negative than *0401. HLA-DPB1*1701, *1601, *1001,
*0901, and *0601 contain similarly charged residues in the three regions. Thus,
all these ß-chains carry the same total charge, and the results for other
alleles belonging to this group are expected to be similar. Only one of these
ß-chains, *1701, was used in this study.
In Table 1 the ß-chain alleles are partitioned in four groups based
on the total charge on the chain (column 2). Grouping ß-chain alleles
by charge in this way provides for a more robust statistical analysis. The ORs
for either CBD or BeS are reported in columns 6 and 7, respectively. The highest
estimates of ORs for CBD were found consistently in ß-chain alleles coding
for isotypes that carry the greatest negative charge. Conversely, the lowest
ORs for CBD were associated with alleles coding for the least negatively charged
HLA-DPB1*0401 (K69) isotype. HLA-DPB1*1901 and *0201 and *1301 carry intermediate
negative charge, and they also were associated with intermediate ORs for CBD.
Overall, there seems to be an obvious link between the amount of negative charge
on the ß-chain and corresponding ORs for CBD.
The highest ORs for BeS were also found in ß-chain alleles that code
for isotypes carrying the greatest negative charge. Comparison of these ORs
with ORs associated with less negatively charged isotypes also indicates a likely
possibility of connection between the charge and ORs. However, in the case of
BeS, the picture is masked by the HLA-DPB1*1901 allele, whose OR disrupts
the otherwise regular rank order in column 7. At present we do not have a comprehensive
explanation for this observation, except that it may be the result of a small
number of observations. The epidemiologic basis for the calculated *1901 OR in BeS is one reported case; this is reflected in large, overlapping confidence
intervals (CIs) calculated for this allele. In addition, the deviation may be
caused by interference of unknown -chains (see below), which have not been
reported in the source studies.
It is evident from the table that the ORs have an approximately log-linear
relationship to the magnitude of negative charge on the ß-chain of HLA-DP.
Figure 5 shows fits of ungrouped log-ORs using the log-linear regression. The
variance-covariance matrix analysis suggests that log-ORs for both CBD and BeS
are reasonably well correlated with the charge. The correlation coefficients
are -0.85 and -0.67, respectively. As the data are naturally stratified
by charge, we further broke the matrix into lack-of-fit and pure-error terms.
The lack-of-fit term represents the deviation of fitted values from group means,
i.e., it characterizes the quality of the fit. The pure-error term specifies
a scatter of the data around the means, which is the statistical noise that
interferes with the regression. The lack-of-fit test indicated a statistically
representative fitting with log-linear regression of both the CBD and BeS data.
The calculated test-statistic numbers F2,5 were 0.16 and 1.04,
respectively, indicating that the null hypothesis for a poor fit could not be
accepted even at the 60% level. A fairly large scatter of the data within the
groups, which is seen in Figure 5, is due to the limited data available for
individual alleles. The coefficients of determination, which measure the random
scatter, were 0.71 and 0.45 for the CBD and BeS regressions, respectively. Despite
this, both regressions were statistically significant according to the standard
test for the zero slope of regression line (F1,7 are 17.51
and 5.68, respectively). Thus, we conclude that the analyzed epidemiologic association
data support the hypothesis of an association between the charge on an allele
and observed ORs for both CBD and BeS.
Figure 6. Electrostatic potential
surface maps for the binding groove. (A) HLA-DPA1*0103/B1*0401. (B) HLA-DPA1*0103/B1*1701.
The scale indicates color-coded values of the electrostatic potential (kT/e).
The surface is a solvent-accessible surface generated as described in the
text. The wire frame denotes a putative location of the antigen as it is
given by the 1FV1 template. |
Figure 7. Isopotential surface
maps for the binding groove region. (A) HLA-DPA1*0103/B1*0401. (B) *0201.
(C) *1301. (D) *1701. Red and blue represent contours at –2 and +2
kT/e, respectively. The isopotential surface map is superimposed on the
solvent-accessible surface (shown in white), so that only a part of the
isopotential surface that lies on or above the solvent-accessible surface
is visible. The wire frame denotes a putative location of the antigen as
it is given by the 1FV1 template. |
Figure 8. Isopotential surface
maps for the binding groove region. (A) HLA-DPA1*0103/B1*1301. (B) *1901.
(C) *1701. Red and blue represent contours at –2 and +2 kT/e, respectively.
The solvent-accessible surface is displayed in white. The wire frame denotes
a putative location of the antigen as it is given by the 1FV1 template. |
Figure 9. Isopotential surface
maps for the binding groove region. (A) HLA-DPA1*0201/B1*0201. (B) HLA-DPA1*0201/B1*1701.
Red and blue represent contours at –2 and +2 kT/e, respectively. The
solvent-accessible surface is shown in white. The wire frame denotes a putative
location of the antigen as it is given by the 1FV1 template. |
To more effectively analyze how the charge distribution varies among HLA-DP
proteins, we calculated the electrostatic potential on the solvent-accessible
surface of each protein. Figure 6 illustrates the locations of the three regions,
which according to Table 1 determine the variation of charge on the ß-chain.
Each of the three regions is located in proximity to the peptide-binding groove.
Figure 6A and B compare the electrostatic potential at the solvent-accessible
surface of HLA-DP allotypes involving the least and one of the most negatively
charged ß-chain alleles. They are HLA-DPA1*0103/B1*0401 and HLA-DPA1*0103/B1*1701,
respectively. These maps clearly show intensification of negative potential
in the binding-groove region of the protein encoded by *1701 allele compared
with that encoded by *0401. In addition to the solvent-accessible potential
surface maps, which show how the potential varies along the surface drafted
by the centers of the rolling probe sphere (e.g., the ion), it is also useful
to analyze the MEP on the protein, which is given by the isopotential surface
maps. Figure 7 illustrates the MEP at a contour level of -2 kT/e (red) and +2 kT/e (blue) for HLA-DPA1*0103/B1*0401, *0201, *1301, and
*1701, respectively. The MEPs on HLA-DPA1*0103/B1*1301, *1901 and *1701 are
compared in Figure 8, in which the view is rotated by 90° to analyze the
effect of the substitution in position ß56. Variations of MEP in other
parts of the protein are fairly minor, as the substitutions of charged residue
are confined to the binding groove only.
Comparison of MEPs in Figures 6-8 shows how the potential surface is
altered by the amino acid substitutions, and hence, the charge in the regions
ß55-ß56, ß69, and ß84-ß85. In the case
of the *0401 protein (Figure 7A), in which the ß-chain does not carry
any negatively charged residues in positions ß55, ß56, ß84,
and ß85, there is virtually no appearance of negative MEP in the peptide-binding
groove region. Instead, the central part of the groove is occupied by a stretch
of positive MEP, which is due to a positively charged Lys ß69. Figure
7B illustrates how substitutions for negatively charged residues in positions
ß55, ß56, and ß69 induce negative MEP in the binding groove
of the *0201 protein. Similarly, Figure 7C shows the emergence of negative MEP
resulting from substitutions for negative residues in the positions ß69,
ß84, and ß85 of the *1301 protein. Comparison of Figure 7B and C
indicates how much Glu ß69 contributes to the negative MEP on allotypes
involving the *0201 and *1301 alleles. Figure 7D demonstrates
a situation in which negatively charged residues are substituted for all residues
constituting the three genetic marker regions. When Figure 7B-D is compared,
we can see that the substitutions in the three regions result in a roughly additive
increase of the surface of negative MEP in and around the groove. In the future,
in-depth quantitative analysis will be applied to determine the magnitude of
negative MEP inside the groove. However, the shape of negative MEP around Glu
ß69 in Figure 7D is apparently enlarged compared with Figure 7B and C,
probably originating from the increased negativity of MEP inside the groove.
If this is the case, it may partially explain the link between charge and ORs
reported in Table 1 and Figure 5.
The effects of replacing Ala ß55 in the *1301 protein with Glu ß55
in the *1901 protein and replacing both Ala ß55 and Ala ß56 with
negatively charged residues in the *1701 protein are illustrated in Figure 8.
Similar to Figure 7, substitution for charged residues results in a corresponding
appearance of increasingly more negative MEP. All studied HLA-DP ß-chain
proteins contain either an Asp or Glu residue in the position ß57, which
itself influences the shape and magnitude of MEP in this region. For example,
there is a Glu in position ß57 in the *0201 protein, whereas the *1701
protein has an Asp in this position. Evidently, the presence of Glu or Asp in
this position affects the appearance of the negative MEP around the region of
ß56, ß57, as can be seen by examining Figure 7B and D (or Figure
9A and B, described below).
The total charge on the -chain encoded by HLA-DPA1*0201 is -17,
and it is -18 on HLA-DPA1*0103. The replacement of Gln50 in the
-chain encoded by HLA-DPA1*0103 with Arg50 encoded by HLA-DPA1*0201 imparts one additional unit of positive charge. The MEP on two HLA-DP allotypes
involving the *0201 allele of the -chain is shown in Figure 9. The figure represents
homology models of HLA-DPA1*0201/HLA-DPB1*1701 and HLA-DPA1*0201/HLA-DPB1*0201.
These should be compared with Figures 7B and 7D, which are for HLA-DPA1*0103/HLA-DPB1*1701
and HLA-DPA1*0103/HLA-DPB1*0201, respectively. Comparison of these two pairs
shows that the effect of adding one additional unit of positive charge on the
-chain can be deduced in the absence (Figures 7B and 9A) and presence (Figures
7D and 9B) of negative charge in the ß84-ß85 region of the
ß-chain. The Q50R substitution in the -chain causes a profound effect
on the MEP in the immediate vicinity of *50. However, it is not clear how much
this substitution affects the MEP inside the groove. Conversely, unlike ß69,
this residue is located at the very edge of the peptide-binding groove, and
thus its effect on the binding may be fairly small. Alternatively, the other
two important markers of the ß-chain, ß55-ß56 and ß84-ß85,
are similarly distant from the center of the groove as *50. From Figures 8 and
9, we already know that the effect of substituting different residues in one
particular region can, to some extent, alter MEP in an adjacent region. Additional
epidemiologic studies may help to clarify the role of -chain substitutions
in the etiology of CBD. From a structural point of view we do not see obstacles
to interference with binding from yet unknown substitutions in the -chain.
Alternatively, if such substitutions indeed do not affect ligand binding, additional
computational investigations are needed to explain the physiologic differences
between substitutions in the *- and ß-chains.
Discussion
Occupational or environmental exposures to metals may cause severe health
disorders related to modulation of immune homeostasis. Metal immunomodulation
has been implicated in chronic inflammatory processes, autoimmune diseases,
and related adverse effects (Cunningham-Rundles et al. 2000; Lawrence and McCabe
2002). Molecular mechanisms of immunological actions of metals are largely unknown.
In this work we explored a possible link between molecular properties of genetic
products conferring susceptibility to Be and the health effects of Be exposures.
The results of this work are useful for interpreting the molecular recognition
in MHC class II proteins and for investigating their interactions with Be2+.
Previous studies have suggested Glu ß69 as an important genetic marker
for conferring susceptibility to CBD (Díaz et al. 1998; Fontenot et al.
2000; Lombardi et al. 2001; Potolicchio et al. 1999; Richeldi et al. 1993, 1997;
Rossman et al. 2002; Saltini et al. 2001; Wang et al. 1999, 2001). More recent
studies have led some investigators to suggest that the charged residues Asp
ß55 and Glu ß56 might also contribute to the recognition of HLA-DP
by T cells (Díaz et al. 1998; Fontenot et al. 2000; Potolicchio et al.
1999). In a study of the binding of cobalt to HLA-DP, Potolicchio et al. (1999)
compared the binding affinity for cobalt by HLA-DPB1*0201 (which has Asp ß55,
Glu ß56, and Glu ß69) and by HLA-DPB1*0402 (which has Asp ß55,
Glu ß56, and Lys ß69), and also by HLA-DPB1*0401 (which has Ala
ß55, Ala ß56, and Lys ß69). They found that the binding affinity
for radioactive 57Co2+ decreased in the order HLA-DPB1*0201
> HLA-DPB1*0402 > HLA-DPB1*0401, confirming earlier evidence
for involvement of Glu ß69 but also suggesting that positions ß55
and ß56 may also be involved in the interaction. A similar study with
HLA-DPB1*0101 and HLA-DPB1*0401, which both carry Ala ß55,
Ala ß56, and Lys ß69, failed to present Be2+ to CD4+ T cells, whereas HLA-DPB1*0202 and other alleles with Glu ß55,
Asp ß56, and Glu ß69 were positive for presentation of Be2+
to T cells (Fontenot et al. 2000).
In this study the electrostatic potential on model proteins encoded by several
HLA-DPA1 and HLA-DPB1 alleles was analyzed. The genetic polymorphisms
in different alleles are responsible for substitutions of charged residues (Glu,
Asp, Arg, Lys) in positions 55-56, 69, and 84-85 of the amino acid
sequence of the ß-chain (Figure 1). We found a clear correlation between
the total charge on protein variants and epidemiologic odds ratios conferring
susceptibility to either BeS or CBD. The MEP contours in Figures 7 and 8 illustrate
the distribution of charge in the three significant regions in proximity to
the binding groove. Hence, HLA-DPA1*0103/B1*1701, which carries the most
negative charge, also appears to confer the greatest susceptibility for BeS
and CBD. We appreciate that in all cases of HLA-DPB1 alleles coding for
glutamic acid in position 69, the 95% confidence intervals overlap. This stems
from the lack of sufficient power of the studies, both independently and together,
to answer the question of allele-specific risk hierarchy. However, given the
consistency of the different studies, the data strongly suggest that such a
hierarchy exists.
Originally, we examined data reported by Wang et al. (1999) and were able,
to a limited extent, to account for homozygosity. The analyses presented here
do not account for the impact of homozygosity versus heterozygosity. Moreover,
we are unaware of the impact of any potential allele misclassification, which
is suggested by the growing numbers of haplotypes reported since 1999. Regarding
the ranking of alleles with respect to BeS (Table 1), we considered the possibility
of bias, potentially introduced by the data reported by Saltini et al. (2001),
as, unlike those of Wang et al. (2001) and Rossman et al. (2002), these data
did not show an association between inheritance of HLA-DPB1*E69 and BeS.
However, elimination of the BeS data reported by Saltini et al. (2001) from
our analyses did not change the overall ranking.
All of the reports described were case-control studies, and we remain
ignorant of many facets of the natural history of CBD, especially as it pertains
to the progression from BeS to disease. In addition, diagnosis of BeS remains
problematic. It may be that a relatively high OR associated with CBD but a relatively
low allele-specific OR associated with BeS reflects a greater propensity for
progression from BeS to CBD (e.g., as may be the case for HLA-DPB1*1001).
As Figure 5 illustrates, there appears to be a log-linear relationship between
ORs and charge on the protein. Although an improved set of data may alter the
fit, it is noteworthy that such a relationship exists, as it suggests that this
kind of analysis may be useful for eventually predicting disease susceptibility
for new allotypes for which epidemiologic data are lacking or unavailable.
As far as the molecular mechanism of CBD is concerned, a previous study has
indicated that Be2+ may bind in the vicinity of Glu ß69, perhaps
directly in the peptide-binding groove (Amicosante et al. 2001). In this connection
the log-linear dependence of the odds ratios (essentially the probability expressed
in the units of background) on the charge is curious because it resembles the
Boltzmann distribution in a two-site charge-charge interaction model when
all other parameters are fixed. At present, we are unaware whether it is a coincidence
or fact reflecting a yet unknown peculiar mode of ion binding. This puzzle may
be resolved in future studies, which will shed additional light on the molecular
mechanisms of metal-dependent immunomodulation.
Conclusion
Our results complement recent findings, which implicate positions ß55,
ß56, ß69, ß84, and ß85 of HLA-DPB1 in susceptibility
to CBD. This approach may prove useful in predicting risk associated with previously
unknown alleles and may help lead to the elucidation of mechanistic issues associated
with CBD. Further studies will be directed at identifying the beryllium binding
site on the HLA-DP protein. |
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Last Updated: November 7, 2003 |
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