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Previous Article | Next Article 
Infection and Immunity, November 2006, p. 6458-6466, Vol. 74, No. 11
0019-9567/06/$08.00+0 doi:10.1128/IAI.00041-06
Copyright © 2006, American Society for Microbiology. All Rights Reserved.
Use of Protein Microarrays To Define the Humoral Immune Response in Leprosy Patients and Identification of Disease-State-Specific Antigenic Profiles
,
Nathan A. Groathouse,1
Amol Amin,1
Maria Angela M. Marques,1
John S. Spencer,1
Robert Gelber,3
Dennis L. Knudson,2
John T. Belisle,1
Patrick J. Brennan,1 and
Richard A. Slayden1*
Department
of Microbiology, Immunology, and Pathology,1
Department of Bioagricultural
Science and Pest Management, Colorado State
University, Fort Collins, Colorado 80523-1682,2
Leonard Wood
Memorial Leprosy Research Center, Cebu,
Philippines3
Received 9 January 2006/
Returned for modification 28 February 2006/
Accepted 3 September 2006
 |
ABSTRACT
|
|---|
Although
the global prevalence of leprosy has decreased over the last few
decades due to an effective multidrug regimen, large numbers of new
cases are still being reported, raising questions as to the ability to
identify patients likely to spread disease and the effects of
chemotherapy on the overall incidence of leprosy. This can partially be
attributed to the lack of diagnostic markers for different clinical
states of the disease and the consequent implementation of
differential, optimal drug therapeutic strategies. Accordingly,
comparative bioinformatics and Mycobacterium leprae protein
microarrays were applied to investigate whether leprosy patients with
different clinical forms of the disease can be categorized based on
differential humoral immune response patterns. Evaluation of sera from
20 clinically diagnosed leprosy patients using native protein and
recombinant protein microarrays revealed unique disease-specific,
humoral reactivity patterns. Statistical analysis of the serological
patterns yielded distinct groups that correlated with phenolic
glycolipid I reactivity and clinical diagnosis, thus demonstrating that
leprosy patients, including those diagnosed with the paucibacillary,
tuberculoid form of disease, can be classified based on humoral
reactivity to a subset of M. leprae protein antigens produced
in recombinant
form.
|
INTRODUCTION
|
|---|
Global leprosy disease prevalence has been drastically
reduced, due largely to a World Health
Organization-sponsoredmultidrug therapy elimination campaign
(42). Incidence, as
estimated by new case detection, however, remains high. Moreover,
disease management and prevention in this new era of lowered prevalence
have been hindered by the absence of tools that allow the objective
diagnosis of disease and disease states, therefore providing a guide to
preventative therapy and overall disease management. The identification
of specific informative diagnostic antigens is one of the most
difficult aspects in developing new diagnostic tools, and this is
particularly true with leprosy, because there is a paucity of
information involving the roles of many of the expressed proteins or
the metabolic state of the organism throughout infection and disease
progression.
The availability of the complete genome sequence and
annotated coding capacity of Mycobacterium leprae provides a
wealth of information that can be exploited for diagnostic purposes
(4,
18). Of course,
prospective antigens that may be relevant to disease diagnosis must
then be validated experimentally. The major protein antigens of M.
leprae were identified through subcellular fractionation of
armadillo-derived M. leprae whole cells
(16,
17,
21,
22,
27,
33,
34,
37). Recombinant forms of
some of the more significant native proteins were subsequently created
and tested (22,
27,
37). Recently, several
groups have also used a postgenomic approach to discover new antigens
for leprosy diagnosis (1,
2,
28,
36,
37). These studies all
exploited genomic sequence for the identification of M.
leprae-specific proteins or peptides that may be suitable for
serodiagnosis of different disease states of leprosy. While many of
these studies described novel antigens that show marked humoral and
cellular immunogenicity, none employed protein-based
microarrays.
The presence of antibodies follows an initial
infection and precedes disease manifestations, allowing targeted
chemoprophylaxis during the typical long incubation period (
5
years) of leprosy. Similar to the diagnosis of tuberculosis, where
early detection of exposure and prompt chemoprophylaxis prevent the
progression of disease, household contacts of multibacilliary (MB)
leprosy patients and exposed individuals would also benefit from early
detection (10). Indeed,
studies have shown that contacts of MB leprosy patients have an
increased risk of developing leprosy themselves
(41). It has also been
found that contacts who have an antibody response to the M.
leprae-specific phenolic glycolipid (phenolic glycolipid I
[PGL-I]) have a much greater chance to develop clinical leprosy than
those without an antibody response
(3,
7,
19,
23). Yet almost half of
those who have antibodies to PGL-I never develop leprosy, and half of
those who develop leprosy never have PGL-I antibody. Thus, additional
alternative markers have the promise of producing a predictive
serodiagnostic tool.
Protein-based microarrays provide
a consistent platform for studying humoral immune responses of a
diverse group of patients to a wide variety of antigens for various
infectious diseases in a high-throughput fashion
(6,
9). In the present work,
the humoral immune response patterns of sera from patients clinically
diagnosed with tuberculoid or lepromatous forms of leprosy
(30) were evaluated with
protein microarrays to define protein profiles reflective of specific
disease states. The arrays were constructed either with proteins
isolated from the cell wall and membrane of M. leprae or with
a subset of recombinant proteins that are unique to M. leprae
or have significant selectivity to M. leprae, according to
stringent bioinformatics analysis. The results indicate that screening
disease-state sera against protein-based microarrays can discern
reactive antigens and patterns that are specific to disease
classification. This work provides a foundation for the identification
of novel diagnostic antigens relevant to the various clinical forms of
leprosy, particularly
tuberculoid.
|
MATERIALS AND METHODS
|
|---|
M. leprae patient serum samples.
Ten each of
paucibacilliary (PB) and MB leprosy patients were diagnosed by clinical
and histopathological criteria at the Leonard Wood Memorial Center for
Leprosy Research, Cebu, Philippines. Leprosy was classified based on
the Ridley-Jopling scheme by bacterial, histological, and clinical
observation (30) carried
out by experienced leprologists and a leprosy pathologist; no nerve
biopsies were performed on the patients in this study. All sera were
collected at the time of initial diagnosis before any antimicrobial
therapy. Individuals clinically diagnosed with the lepromatous (LL) or
borderline lepromatous (BL) forms of leprosy (samples L1 to L26) had an
enzyme-linked immunosorbent assay (ELISA) value (optical density at 490
nm [OD490]) against PGL-I of M.
leprae (15) of 2.35
± 0.28 and a mean bacterial index (BI) of 4.03 ± 0.62.
Individuals clinically diagnosed with the tuberculoid (TT) or
borderline tuberculoid (BT) forms of leprosy (samples T51 to T60) had
an ELISA PGL-I value (OD490) of 0.80 ± 0.36 and a
mean BI of 0.48 ± 0.50. Details of the treatment of patients
and clinical outcomes are presented in Table S1 in the supplemental
material. Naive individuals from a site to which leprosy is not endemic
(Colorado) provided control sera with an ELISA PGL-I value
(OD490) of 0.29 ±
0.03.
Isolation and purification of M. leprae subcellular fractions.
Approximately 200 mg of
M. leprae whole cells were purified from armadillo spleens and
livers according to the Draper 3/77 protocol
(33). Subcellular
fractionation of M. leprae whole cells was achieved by sonic
disruption (MSE Soniprep 150, MSE-Sonyo; Integrated Services, Palisades
Park, NJ) for 30 cycles (60-s bursts followed by 60 s of
cooling) in buffer consisting of 10 mM NH4HCO3
and 1 mM phenylmethylsulfonyl fluoride. The whole-cell sonicate was
digested with 10 mg/ml of DNase and RNase for 1 h at
37°C (11). The
pellet resulting from centrifugation at 27,000 x g for
30 min provided the cell wall fraction, and the supernatant from this
step was recentrifuged at 100,000 x g for 2
h, yielding a second pellet of cytoplasmic membrane.
Final
separation of cell wall and cytoplasmic membrane-associated proteins
was achieved by electrophoresis on a preparative 10% sodium dodecyl
sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel performed
under reducing conditions
(20). On completion of
electrophoresis, the gel was soaked in 20 mM
NH4HCO3 for 30 min, followed by electrophoretic
elution of the proteins using a Bio-Rad whole-gel elutor (Bio-Rad,
Hercules, CA) at 250 mA for 2 h. The resulting protein
fractions were frozen and lyophilized, resuspended in a 400-µl
volume of sterile endotoxin-free water, and analyzed for content and
purity by SDS-PAGE and silver staining
(24). A periodic acid
step was also incorporated to gauge the presence of or ensure the
absence of lipoarabinomannan
(40).
ELISA and Western blotting.
High-affinity polystyrene microtiter
plates (Immulon 4 HBX plates; Dynax, Alexandria, VA) were coated with
protein antigens overnight at 4°C at concentrations ranging
from 50 to 250 ng in 50 µl of phosphate-buffered saline (PBS)
per well for purified antigens, up to 4 µg per well for
membrane and cell wall fractions, and 0.5 to 2 µg per well for
size-fractionated protein antigens. Wells were blocked with PBS
containing 1% bovine serum albumin (Intergen Co., Purchase, NY) and
0.05% Tween 80 (Sigma Chemical Co., St. Louis, MO) for 1 h at
room temperature. Polyclonal mouse sera and monoclonal antibodies were
incubated at optimal dilutions in blocking buffer, as previously
described (37). Unbound
antibody was removed with PBS containing 0.05% Tween 80 without bovine
serum albumin, and the secondary antibody conjugated to alkaline
phosphatase (Sigma) was added and incubated for 2 h. Alkaline
phosphatase activity was detected by the addition of a
p-nitrophenylphosphate substrate. Western blots
were prepared by transferring antigens run on SDS-PAGE (10% or 15%
polyacrylamide gels) to a nitrocellulose membrane
(39) and incubated with
an antigen-specific primary antibody
(37). Antibodies against
M. leprae antigens were generated as described elsewhere
(12,
16,
17,
22,
27,
34,
37); these are available
from the Leprosy Research Support and Maintenance of an Armadillo
Colony Post-Genome Era, Part I: Leprosy Research Support Contract (N01
AI-25469) at Colorado State University
(5).
Comparative genomic and bioinformatics analysis.
A global in
silico identification of
targets-CROSS_MATCH (GISIT-cm)-approach was
used to identify proteins that might be potential targets for further
study. The GISIT-cm approach identifies unique proteins by comparing
the M. leprae genome (GenBank entry NC_002677.fna)
against other bacterial genomes. This was performed with the
Mycobacterium tuberculosis genome (GenBank entry
NC_000962.fna) by dividing it into 491 10-kb
fragments, where each fragment contained a 1 kb-overlap
with the previous fragment, using SPLITTER (EMBOSS package)
(29). The data
set of M. tuberculosis overlapping sequences was then used as
the source for the masking sequence against the M. leprae
genome using CROSS_MATCH (version 0.990329), which used a
restricted Smith-Waterman
(35) algorithm
(13). CROSS_MATCH
was run with a min-match value of 12 and a min-score value of 20,
resulting in a masked M. leprae genome file where the
sequences similar to those of M. tuberculosis were identified.
ARTEMIS was then used to identify the open reading frames (ORFs) that
were masked and to produce a masked data set of M. leprae
ORFs. The M. leprae data set was opened in ARTEMIS
(31), ORFs were selected,
and a separate feature table of the 1,605 selected ORFs was
prepared; this selection did not contain pseudogenes. Shell
script and PERL scripts that read the FASTA
(26) formatted file of
masked proteins were written, producing a new file of proteins where
each protein did not contain more than a specified percentage of
cross-identity in the amino acid sequence. A value of 50% was used as
the cutoff in this study. Uniqueness of identified ORFs to M.
leprae was confirmed by BLASTN and BLASTP analysis against GenBank
entries. The complete list of proteins identified using the GISIT-cm
approach is in Table S2 in the supplemental
material.
Production of recombinant proteins.
Relevant genes
were PCR amplified from M. leprae genomic DNA using Vent PFU
DNA polymerase (Sigma). Primers for each gene were engineered to
introduce NdeI and HindIII restriction enzyme sites into the 5'
and 3' ends of the amplicon to facilitate direct cloning into
the expression vector pET28(+) (EMD Biosciences, Inc., San
Diego, CA). Each recombinant clone was verified by DNA sequencing.
Recombinant protein production was achieved by introduction of the
expression plasmid into Escherichia coli strain BL21(DE3)
(Invitrogen Corp., Carlsbad, CA) by transformation and induction using
the T7 polymerase with 0.3 mM
isopropyl-ß-D-thiogalactopyranoside. Recombinant
proteins were released from E. coli by sonic disruption in
buffer (Tris-HCl, pH 8.0, 2 µg/ml aprotinin, 1 µg/ml
leupeptin, 1 µg/ml pepstatin, and 1 mM phenylmethylsulfonyl
fluoride). The bacterial lysate was cleared by centrifugation, and the
resulting supernatant was applied to an immobilized nickel-affinity
column. Purified recombinant proteins were recovered from the affinity
column with 50 mM imidazole and passed over a Detoxi-gel column (Pierce
Biotechnology, Inc., Rockford, IL) to remove any contaminating
endotoxin. Purity of recombinant proteins was assessed by SDS-PAGE,
followed by silver staining. The final protein concentration was
determined using the bicinchoninic assay (Pierce Biotechnology, Inc.,
Rockford, IL), and lipopolysaccharide contamination was evaluated by
the Limulus amoebocyte lysate assay (Cambrex Corp., East Rutherford,
N.J.).
Fabrication and immunoblotting of protein microarrays.
M.
leprae protein arrays were fabricated on glass slides with a 14
µM nitrocellulose film (FAST glass slides; Schleicher &
Schuell BioScience, Inc., Keene, N.H.) using a Versarray
Chipwriter Pro (Bio-Rad). Proteins fractions and buffer controls were
printed in triplicate at approximately 0.2-mg/ml concentrations.
Protein arrays were blocked for 1 h in protein array-blocking
buffer (Schleicher & Schuell BioScience, Inc., Keene, NH) and
incubated with serum (diluted 1:50) from patients or controls (primary
antibody) at room temperature for 2 h. Visualization of
primary antibody (Ab) (Sigma-Aldrich, St. Louis, MO) was achieved by
incubation with Cy5- or Cy3-conjugated antihuman secondary Abs and
scanning with a VersaArray ChipReader Pro (Bio-Rad). Fluorescence
intensities were quantified using Spotfinder software
(32,
38).
Array data analysis.
Fluorescence
intensities derived from each of the independent triplicate arrays were
averaged to represent the response of each patient's serum sample. The
resulting averaged intensities were then globally normalized for direct
comparisons. Fluorescence intensities for each protein spot resulting
from blotting with control serum were used to calculate the level of
fluorescence intensity relative to background reactivity for each
protein spot. The reactive index for each protein spot was calculated
as the number of standard deviations relative to the average
fluorescence intensity of all the spots. This statistical approach
allowed for identification of protein antigens that were found to have
significantly greater than average background reactivity. Hierarchal
clustering and self-organizing map (SOM) analysis was performed on the
entire data set (43); SOM
is an unsupervised neural network model that effectively categorizes
and clusters based on similarities in the antibody reactivity among
groups.
|
RESULTS
|
|---|
Analysis of the humoral immune response using native-based protein arrays.
Native protein arrays
were printed with protein fractions derived from the M. leprae
membrane and cell wall. These fractions were visualized by SDS-PAGE to
evaluate overall sample fractionation and protein distribution (Fig.
1). Although the molecular weight range of proteins in each fraction was
relatively narrow, previous quantitative analysis of two-dimensional
gel patterns revealed that each protein fraction used for array
fabrication contained multiple proteins
(21). To evaluate the
potential distribution of a single protein among different protein
fractions, Western blot analysis was performed (data not shown),
demonstrating that known protein antigens were electrophoretically
eluted into peak fractions with some overlap to adjacent
fractions.
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FIG. 1. SDS-PAGE
gel migration analysis of the M. leprae native protein
fractions used in the fabrication of the protein microarray.
(A) Cell wall protein fractions and (B) membrane
proteins fractions separated by electrophoretic elution and visualized
by silver
staining.
|
|
Native protein arrays were probed with serum obtained
from patients clinically diagnosed with lepromatous or tuberculoid
forms of leprosy (30).
Immunologically naive individuals lack reactivity against any of the
proteins on the array, but sera from individuals diagnosed with leprosy
had different reactivity patterns. The reactive index for each protein
antigen fraction on the arrays was calculated and subjected to analysis
to determine whether there were unique disease-specific patterns that
correlate to disease diagnosis (Fig.
2C) (see Table S3 in the supplemental material). SOM
analysis of the reactive index for each protein fraction assigned
patients into three groups based on the reactive patterns of their sera
(Fig. 2A). SOM group I was
predominately composed of patients that had been clinically diagnosed
with the lepromatous form of leprosy (SOM 0; n = 4/5).
SOM group III was entirely composed of patients clinically diagnosed
with the tuberculoid form of leprosy (SOM 2; n = 6/6).
The largest and most clinically diverse group was SOM group II, which
had three patients clinically diagnosed with the tuberculoid form of
leprosy (SOM 1; n = 3/9) and six patients clinically
diagnosed with the lepromatous form of leprosy (SOM 1; n
= 6/9). While there were some protein fractions recognized in
common by all patient sera, many of the protein fractions were uniquely
recognized by sera from patients assigned to a single SOM group (Fig.
3A to
C; Table
1). Specific reactivity patterns which
correlate with different clinical states of disease were
seen using protein microarrays and statistical
analysis.
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FIG. 2. Statistical
analysis of humoral response patterns derived from M. leprae
native protein microarrays. (A) Self-organizing
mapping and (B) hierarchal cluster analysis of reactive indices from patient
sera on native protein microarrays. (C) Map of serum reactivity
patterns for each native protein fraction. The reactive indices for
each protein were calculated and the statistical analysis performed as
described in Materials and
Methods.
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FIG. 3. Reactive
profiles of patient groups assigned by self-organizing mapping. SOM
group I (A), group II (B), and group III (C) as determined
from native microarray analysis. The reactive indices for each protein
were calculated as described in Materials and
Methods.
|
|
Hierarchal clustering analysis was also performed on
the data set. This statistical approach organized patients into two
rather than three major groups (Fig.
2B). The main difference
was that patients assigned to SOM groups I and II were combined, and
several smaller subdivisions, namely HC groups Ia to Ic, emerged.
Overall, this analysis organized the majority of the lepromatous
patients together (HC groups Ib and Ic; n = 9/10) with
a small group (HC group Ia) as statistical outliers with one
lepromatous and one tuberculoid patient. Importantly, the second major
group, revealed by hierarchal clustering analysis (HC group II),
contained the same patients as SOM group III, which was wholly
comprised of patients that were clinically diagnosed with the TT form
of leprosy.
Further examination of the analysis clearly revealed
a group of patients assigned to SOM group II or HC group Ib by SOM or
hierarchical clustering analysis, respectively, whose sera had similar
reactivity patterns despite clinical diagnosis and that were different
from other lepromatous and tuberculoid patients, favoring
classification in a more intermediary position within the
Ridley-Jopling clinical spectrum
(30). These observations
support the case for a borderline form of disease (BT, BB, or BL).
Statistical analyses supported the case for the existence of unique
patterns of serological reactivity to M. leprae protein
fractions for different clinical states of disease, thus substantiating
this approach of using M. leprae protein microarrays for the
identification of disease state-specific reactive patterns,
particularly for the tuberculoid from of disease.
The
complexity of the native protein fractions hindered precise
identification of all of the potentially reactive proteins within each
fraction by mass spectrometry, N-terminal sequencing, or Western
blotting. Since certain dominant protein antigens were known to be
present in the spotted native protein fractions as determined by
application of antigen-specific monoclonal antibodies, patient sera
reactive to these spots implicated reaction to these precise proteins
(see Table S4 in the supplemental material). Furthermore, sera reactive
to multiple fractions containing a common protein strongly implicated a
particular antigen as the immunodominant protein in those fractions.
However, this is not a definitive identification system. Accordingly,
recombinant protein-based arrays were applied towards more definitive
identification of antigenic
proteins.
Analysis of humoral immune response to selected recombinant proteins.
GISIT-cm was performed to
identify proteins unique to M. leprae compared to other
mycobacterial species. Of 1,605 M. leprae-encoded proteins,
214 were found to have 50% or greater selectivity to M. leprae
among contiguous protein sequence while 160 were considered 100% unique
(see Table S2 in the supplemental material). M. leprae-unique
proteins identified from our bioinformatic approach are in agreement
with those recently identified by others
(2). Notably, only 6 of
the 160 unique proteins were annotated to a putative function, whereas
the others were annotated as hypothetical proteins
(18). Using information
obtained through bioinformatic analyses and reactivity on native
protein arrays, 18 proteins were selected for recombinant production
and purification.
This set of recombinant proteins was evaluated
through microarray technology to evaluate and identify a subset of
proteins that can serve as leprosy-specific disease state antigens.
Upon screening with patient sera, the reactive index for each
recombinant protein was calculated and subjected to SOM analysis (Table
2). Similar to what was observed using
native arrays, antigenic proteins fell into three basic diagnostic
categories: those recognized by tuberculoid patients, those recognized
by lepromatous patients, and those recognized by a subset of
tuberculoid and lepromatous patients. The last group may represent
borderline forms of disease. Group I antigen I (Grp I Ag-1)
(ML0008) and Grp I Ag-2 (ML0957) were recognized only by sera of
patients clinically diagnosed with the lepromatous form of the disease.
Grp II Ag-1/Tuf (ML1877), Grp II Ag-2 (ML1829), Grp II Ag-3 (ML0126),
and Grp II Ag-4 (ML0396) were identified as being differentially
recognized by sera of patients thought to have an intermediate form of
leprosy based on statistical analysis of reactivity patterns using
protein microarrays. Grp III Ag-1 (ML1419) and Grp III Ag-2 (ML1057)
were recognized by sera from patients that were clinically diagnosed
with the tuberculoid form of disease. Interestingly, a recent study did
not find ML1057 to elicit significant gamma interferon (IFN-
)
production in leprosy patients
(2). Two recombinant
proteins were found to be recognized universally in this study for all
patients infected with leprosy. Accordingly, these proteins, universal
Ag-1 (ML1915) and universal Ag-2/CFP-10 (ML0050), are predictive
antigens diagnostic for exposure to and infection with M.
leprae. Others have reported immunoreactivity of sera of leprosy
patients to ML1877 and ML0050, substantiating the discovery of these
proteins as seroreactive antigens, and the use of ML0050 as a universal
antigen for exposure to M. leprae
(2,
28,
37). Taken together,
these results demonstrate that as few as 10 recombinant proteins can be
used to acquire meaningful information about a disease state and that
directed recombinant arrays can yield information equivalent to what
was provided by full native protein microarrays but with the added
benefit of using proteins with known
identity.
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TABLE 2. Dominant
protein fractions for each SOM
group identified by patient sera on
recombinant protein microarrays
|
|
Correlation of protein array classification and reactivity to PGL-I.
A current serodiagnostic test that is
able to identify patients with M. leprae infection is based on
M. leprae-specific PGL-I. This antigen has been shown to be a
marker for bacterial load, with antibody levels correlating with the
spectrum of disease (10,
14,
25). Accordingly, sera
from all the patients used in this study were evaluated by ELISA for
PGL-I seroreactivity (Table
3). Overall, patients diagnosed with the PB
form of disease had lower ELISA PGL-I values (OD490)
(0.80 ± 0.36) and patients diagnosed with the MB form
of disease had greater PGL-I values (2.35 ± 0.28), which is
partially concordant with immunoreactivity patterning (Fig.
4). As discussed previously, patients were categorized into three groups
based on serum reactivity. SOM group I consisted of PB patients, and
SOM group III consisted of MB patients, whereas SOM group II contained
both PB and MB patients. Although there was a general concordance
between the clinical diagnosis of the patients and the statistical
categorization, a different state of disease progression, perhaps
borderline forms of disease, is indicated by the statistical
elucidation of SOM group II. Importantly, patients grouped in SOM group
II had a mean PGL-I ELISA value (OD490) of 1.80 ±
0.76, which is a value that was in between the mean value for patients
clinically diagnosed with PB and patients clinically diagnosed with MB
forms of disease. The conclusion that individuals categorized in SOM
group II are indeed at a different stage of disease progression is
supported by this observation and is consistent with elevated
antibodies against PGL-I being associated with spectrum of disease and
relapse; in fact it has been suggested that PB leprosy patients with
elevated antibodies should be treated as MB leprosy patients
(10).
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FIG. 4. Statistical
analysis and categorization of disease state based on patient sera
reactivity derived from native and recombinant-based protein
microarrays. (A) The PGL-I ELISA reactivity correlated with
the SOM analysis of reactivity patterns from (B) native and
recombinant protein microarray analysis. The T series of patient sera
and L series of patient sera are described in Materials and Methods.
The shaded areas highlight the different disease states and statistical
grouping.
|
|
|
DISCUSSION
|
|---|
One
of the most challenging tasks in developing disease state-specific
serodiagnostics is the identification of discriminating antigens that
differentiate between exposure and clinical stage of
disease with high sensitivity and specificity. Screening sera from a
large number of patients diagnosed with various states of disease
against the entire leprosy proteome offers the potential for facile
identification of such selective antigens. However, the resources to
accomplish such an extensive enterprise with leprosy are not available.
Therefore, to utilize microarray technology for the identification of
novel diagnostic antigens, native proteins were obtained by subcellular
fractionation of M. leprae and selected proteins were
identified for recombinant antigen production based on bioinformatic
analyses. Specifically, for selection of recombinant proteins,
comparative analysis of the leprosy genome against those of closely
related organisms was performed to identify gene products that are
unique to M. leprae, with a consequent high degree of
serological specificity. Such an approach has been successfully used to
identify highly specific antigens for tuberculosis diagnostics
(8).
Currently
the serodiagnosis of leprosy has been largely confined to the presence
of immunoglobulin M antibodies to the M. leprae-specific
PGL-I. Though antibodies to PGL-I are present in more than 90% of
untreated MB lepromatous patients, only a limited number of patients at
the PB/tuberculoid end of the disease spectrum are reactive
(23,
25). Thus, the PB state,
with low levels of circulating specific antibodies, absence of
acid-fast bacilli, and clinical similarities to numerous other
granulomatous processes, is difficult to diagnose
(19). Adding to the
complication of leprosy diagnosis is the requirement for highly trained
clinicians that can differentiate clinical states of disease and
categorize patients within the disease spectrum. In the absence of such
experienced clinicians, diagnoses of each clinical form of leprosy is
subjective (14).
Depending on the categorization, the chemotherapeutic regimen varies: 6
months of multidrug therapy for tuberculoid patients compared to 12
months or more for lepromatous patients. Therefore, to enhance leprosy
diagnosis and treatment, particularly for tuberculoid patients, an
accurate diagnostic tool that provides a clear definition and a
benchmark for disease progression is desirable.
In an
attempt to improve diagnostics, multiple tests have been developed for
leprosy; however, they lack either specificity or sensitivity for the
detection of asymptomatic infections and disease progression. Recently
studies employing bioinformatics and experimental approaches to
evaluate individual M. leprae proteins or small sets of
proteins as potential serodiagnostic or T-cell antigens have been
performed (1,
2,
14,
28,
36). Reed and colleagues
(28) identified 14
recombinant M. leprae proteins that strongly react to sera of
LL patients, and two of these antigens (Ml0405 and Ml2331) demonstrated
the ability to detect BL patients and, in combination, enhanced
serological detection with PGL-I. Geluk et al.
(14) also evaluated a
relatively large number of recombinant M. leprae proteins for
reactivity to T cells. This work demonstrated five antigens (Ml0576,
Ml1989, Ml1990, Ml2283, and Ml2567) that induced significant
IFN- levels in PB leprosy patients, reactional leprosy
patients, and contacts but not in most MB patients or controls.
Recently, recombinant proteins (Ml0008, Ml0126, Ml1057, and Ml2567) and
58 peptides were tested by us for IFN- responses in peripheral
blood mononuclear cells from leprosy patients seeking epitopes that
would increase specificity
(36). The responses to
the four recombinant proteins gave higher levels of IFN-
production but less specificity than the peptides, with 35 of the
peptides giving high responses only in the case of PB and household
contacts. Another study evaluated the immunogenicity of 12 recombinant
proteins by measuring the reactivity of circulating antibody and
IFN- responses. Both humoral and cellular immunogenicity was
observed for two antigens (Ml0308 and Ml2498) for PB and MB patients
(2). It is interesting to
note that there is limited overlap between the M. leprae
proteins studied in previous work
(36) and the 18
recombinant proteins evaluated in this study. However, the
methods for selecting and screening of potential antigens in these
studies were dramatically different. Overall, none of these studies
identified unique antigens capable of distinguishing patients with PB
versus MB forms of disease.
In our current studies,
evaluation of serological reactivities for 20 patients clinically
defined with either the PB or MB form of disease led to the
identification of 10 proteins, allowing classification of patients into
3 categories. Sera from six PB patients uniquely recognized Ml0008 and
Ml0957, and sera from six MB patients uniquely recognized, Ml1419 and
Ml1057. Sera from the remaining PB and MB patients reacted with Ml1877,
Ml1829, Ml0126, and Ml0396, giving rise to a third category. All
patient sera had reactivity to Ml1915 and Ml0050, providing broad
controls, similar to previous studies discussed. Identification of
these 10 antigens based on serological activity established that a
limited number of antigens can be used to categorize patients into
groups consistent with clinical diagnosis based solely on nonsubjective
criteria.
An interesting finding in this study is the statistical
identification of a set of patients clinically diagnosed with either
the PB or MB form of disease with similar humoral reactivity profiles
(SOM group II). One possibility that may account for this is that these
patients, with different clinical diagnoses, intermediate PGL-I
reactivity, and different bacterial burdens, may be
progressing along the clinical spectrum of disease. In such a case, the
ability of these 10 antigens to distinguish true PB patients from those
progressing towards the MB form of disease would have significant
utility in leprosy control programs and in limiting the transmission of
M. leprae. It has been reported that PB patients with weak
PGL-I antibody responses are not associated with the spread of disease,
whereas PB leprosy patients with elevated antibody responses transmit
bacilli. Therefore, PB patients in this study that were categorized
into SOM group II based on seroreactivity and that have elevated PGL-I
reactivity might be progressing to the MB state. Fully realizing the
potential of the antigens described in this study will require a larger
cohort of patients and follow-up studies on disease
progression.
A second aspect of this work was the use
of complex subcellular protein fractions from an obligate intracellular
pathogen to fabricate microarrays seeking to define unique serological
reactivity profiles. While precise antigen identifications were not
made, the use of native protein microarrays proved useful for
discerning unique patterns in leprosy patients. Since the native
protein fractions were limiting, extensive antigen identification could
not be performed. Nevertheless, it was interesting to note that
regardless of whether native protein fractions or recombinant proteins
were used, patients sera grouped equally well based on disease state.
The data obtained with the native fractions also indicate that there
are potentially more diagnostic antigens to be discovered. Protein
array technology may not yet be applicable as a field diagnostic in
regions of endemicity. It is, however, a powerful tool for antigen
discovery and could be applied to other clinically relevant research
questions, including the identification of serodiagnostic antigens that
can be used to monitor the success or failure of
therapy.
|
ACKNOWLEDGMENTS
|
|---|
This work was supported by
NO1-AI25469 (P.J.B.), RO1-AI47197 (P.J.B.), RO1-AI055298 (R.A.S.), and
NO1-AI75320 (J.T.B.).
We gratefully acknowledge the enthusiastic
support of the clinical staff at Leonard Wood Memorial Leprosy Research
Center in Cebu, Philippines.
N.A.G. performed the screening on
patient sera, prepared figures for publication, and wrote the
manuscript with R.A.S. A.A. printed the protein arrays and
standardized the hybridization protocols with R.A.S. M.A.M.M.
performed the protein fractionation. J.S.S. performed the
ELISA and provided protein fractions. R.G. provided clinical support.
D.L.K. provided bioinformatic support. D.L.K., J.T.B., P.J.B., and
R.A.S. contributed to the design of the study, data interpretation, and
manuscript
preparation.
|
FOOTNOTES
|
|---|
* Corresponding author. Mailing address: Department of Microbiology, Immunology, and
Pathology, Colorado State University, Fort Collins, CO 80523-1682.
Phone: (970) 491-1925. Fax: (970) 491-1815. E-mail:
richard.slayden{at}colostate.edu. 
Published
ahead of print on 11 September 2006.
Editor: J. L. Flynn
Supplemental material for this article may be found at
http://iai.asm.org/.
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Infection and Immunity, November 2006, p. 6458-6466, Vol. 74, No. 11
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Abstract
Introduction
