Candida albicans - ACS Publications - American Chemical Society

Article pubs.acs.org/jpr

Serum Antibody Signature Directed against Candida albicans Hsp90 and Enolase Detects Invasive Candidiasis in Non-Neutropenic Patients Aida Pitarch,* César Nombela, and Concha Gil Department of Microbiology II, Faculty of Pharmacy, Complutense University of Madrid and Ramón y Cajal Institute of Health Research (IRYCIS), Plaza Ramón y Cajal s/n, 28040 Madrid, Spain S Supporting Information *

ABSTRACT: Invasive candidiasis (IC) adds significantly to the morbidity and mortality of non-neutropenic patients if not diagnosed and treated early. To uncover serologic biomarkers that alone or in combination could reliably detect IC in this population, IgG antibody−reactivity profiles to the Candida albicans intracellular proteome were examined by serological proteome analysis (SERPA) and data mining procedures in a training set of 24 non-neutropenic patients. Despite the high interindividual molecular heterogeneity, unsupervised clustering analyses revealed that serum 22-IgG antibody−reactivity patterns differentiated IC from non-IC patients. Univariate analyses further highlighted that 15 out of the 22 SERPAidentified IgG antibodies could be useful candidate IC biomarkers. The diagnostic performance of one of these candidates (anti-Hsp90 IgG antibodies) was validated using an ELISA prototype in a test set of 59 non-neutropenic patients. We then formulated an IC discriminator based on the combined immunoproteomic fingerprints of this and another SERPA-detected and previously validated IC biomarker (anti-Eno1 IgG antibodies) in the training set. Its consistency was substantiated using their ELISA prototypes in the test set. Receiver-operatingcharacteristic curve analyses showed that this two-biomarker signature accurately identified IC in non-neutropenic patients and provided better IC diagnostic accuracy than the individual biomarkers alone. We conclude that this serum IgG antibody signature directed against C. albicans Hsp90 and Eno1, if confirmed prospectively, may be useful for IC diagnosis in non-neutropenic patients. KEYWORDS: diagnosis, biomarkers, immunoproteomics, immunome, invasive candidiasis, hsp90, enolase, serological proteome analysis, antibody response, sensitivity and specificity

■

INTRODUCTION In the light of the crucial role of neutrophils in host defense mechanisms against invasive candidiasis (IC), neutropenia was reported early as an important risk factor for this life-threatening mycosis caused by Candida spp. (commonly Candida albicans, a member of the normal human microbiome).1,2 Nevertheless, over the past few decades, the incidence of IC in neutropenic patients has substantially reduced due to the widespread use of prophylactic, preemptive, and early empirical antifungal therapy strategies in this high-risk group.3,4 In contrast, the optimal management of IC in non-neutropenic patients remains a clinical challenge.5−7 This often results in delays in the initiation of adequate antifungal treatment, with the subsequent adverse clinical outcomes (increased morbidity and mortality) in this heterogeneous population of patients at risk of developing IC, which mainly includes critically ill, postsurgical, trauma, diabetic, and cancer patients.8,9 Early detection of IC could improve its clinical management in such patients.2,8 However, IC is difficult to diagnose at an early © XXXX American Chemical Society

stage because of the low specificity of its clinical signs and symptoms and the limited diagnostic value of its two current gold standards, at least as far as sensitivity (blood cultures) and unfeasibility (tissue biopsies) are concerned.8,10 Furthermore, the alternative (nonculture and noninvasive) diagnostic tests that have been developed to assess individual biomarkers in bodily fluids (anti-Candida antibodies or Candida polysaccharides, proteins, nucleic acids, or metabolites) have not proven to be sufficiently early, sensitive, or specific on their own, nor have they been standardized and validated for routine clinical practice.10−13 Taking into account the complexity and diversity of host and fungal responses underlying IC pathogenesis and the inadequate accuracy of single biomarkers, it has been suggested in recent years that combinations of multiple biomarkers or risk factors Special Issue: Proteomics of Human Diseases: Pathogenesis, Diagnosis, Prognosis, and Treatment Received: June 30, 2014

A

dx.doi.org/10.1021/pr500681x | J. Proteome Res. XXXX, XXX, XXX−XXX

Journal of Proteome Research

Article

was designed following current guidelines of reporting in diagnostic test research.40,41

could overcome the Achilles’ heel of individual molecular or clinical indicators, respectively, and be the key to a prompt and reliable diagnosis of IC.10,13−20 Among the currently available methodologies for biomarker discovery on a large scale, serological proteome analysis (SERPA), together with data mining procedures, has been demonstrated to be an efficient platform to examine the antibody response to a wide array of immunogenic proteins (referred to as the immunome) of a (micro)organism and to identify antibody biomarkers and signatures in serum with clinical and therapeutic potential for cancers, autoimmune disorders, allergies, and infectious diseases.15,21−28 Antibody signature is a newly coined term to designate the molecular fingerprint of antibodies produced against a particular disease state.15,29 Because these immunological signatures contain information about multiple serologic biomarkers, their elucidation in health and disease conditions may be useful in differentiating physiological from pathological processes and providing a rationale for the future design of noninvasive diagnostic tests.30 We previously showed the value of SERPA to evaluate antiCandida IgG antibody response in IC patients as well as to discover individual IC diagnostic/prognostic biomarkers and an IC prognostic predictor based on a reduced panel of biomarkers.15,31−37 In this work, we have extended our research to gain further insight into the serologic response to the C. albicans intracellular proteome (an important source of serodominant antigens)35−38 in non-neutropenic patients and to identify candidate IgG antibody biomarkers that alone, or in combination, can be useful for IC diagnosis in this population. We then developed an ELISA prototype to validate the diagnostic value of one of the most promising SERPA-identified candidate IC biomarkers in a different set of non-neutropenic patients. We further investigated whether the combined molecular fingerprints of this and another SERPA-identified and previously validated IC biomarker (a serum 2-IgG antibody signature) could offer better diagnostic accuracy than the individual biomarkers alone and be used to detect IC in non-neutropenic patients.

■

SERPA

Preparation of C. albicans Protoplast Lysates. Protoplast lysates of a clinical C. albicans isolate (strain SC5314) were used as a source of intracellular antigens and prepared as described.30,31 Briefly, yeast cells were grown in YPD medium (1% Difco yeast extract, 2% peptone, and 2% D-glucose) and incubated in a pretreatment buffer (10 mM Tris-HCl, pH 9.0, 5 mM EDTA, and 1% 2-mercaptoethanol) for 30 min and then in a 1 M sorbitol solution containing 30 μg/mL glusulase (Du Pont, Boston, MA) until more than 90% protoplasts were obtained. After washing with 1 M sorbitol, protoplast cells were resuspended in lysis buffer (50 mM Tris-HCl, pH 7.5, 1 mM EDTA, 150 mM NaCl, 1 mM DTT, 0.5 mM PMSF, and 5 μg/mL each of leupeptin, pepstatin, and antipain (Sigma, St. Louis, MO)) and lysed by vortexing. The clarified supernatant was kept at −80 °C. Protein concentration was estimated using the Bradford assay (Bio-Rad, Hercules, CA). Two-Dimensional Polyacrylamide Gel Electrophoresis (2-DE). Proteins from C. albicans protoplast lysates were separated by 2-DE as reported,30,35 with slight modifications. In brief, protein samples (350 μg) were incubated in a rehydration buffer [7 M urea, 2 M thiourea, 2% CHAPS, 65 mM DTE, 0.5% immobilized pH gradient (IPG) buffer pH 3−10 (GE Healthcare, Buckinghamshire, U.K.), and 0.002% bromophenol blue] for 30 min. Proteins were absorbed onto IPG strips (pH 3−10 nonlinear; 18 cm; GE Healthcare) at 15 °C for 16 h and then focused on an IEF system (IPGphor; GE Healthcare) at 15 °C using a stepwise increasing voltage (500 V for 1 h, 500 to 2000 V for 1 h, and 8000 V for 9.5 h). After equilibrating the IPG strips, isoelectric focused proteins were separated by SDS-PAGE using homogeneous gels (10% T, 1.6% C) and an electrophoresis chamber (Protean II xi cell; Bio-Rad), and then visualized by colloidal Coomassie brilliant blue or silver staining. Two-Dimensional Western Blot Analysis. The 2-DEseparated proteins were transferred to nitrocellulose membranes (HyBond ECL; GE Healthcare) by electroblotting. Serum specimens from the training set were individually tested at a 1:100 dilution by Western blotting for IgG antibodies to proteins onto the 2-D blots and evaluated in two independent assays as reported.30,35 The reactivity level of each IgG antibody in each serum was calculated as the integrated optical density of the area of its corresponding immunoreactive protein spots after background subtraction and normalization analyses using the ImageMaster 2D Platinum software v.5.0 (GE Healthcare) and expressed as arbitrary units (AU). Protein spots detected with sera from at least three training patients were identified using our reference 2-D map of C. albicans immunogenic proteins35 (which were characterized previously by MS35 and MS/MS32,35 analyses). This map is also available on our COMPLUYEAST2DPAGE database.42,43

EXPERIMENTAL PROCEDURES

Study Population and Serum Specimens

Serum samples from 83 non-neutropenic patients, which comprised 35 IC patients and 48 non-IC patients (with no clinical and microbiological evidence of IC), were collected on the day of culture sampling at the Salamanca Clinic Hospital (Spain), following a standard clinical procedure and a single-gate design.37,39 All patients were enrolled according to protocols approved by the Ethics Committee of Clinical Research from Salamanca Clinical Hospital after informed consent had been obtained. Patients were considered to be non-neutropenic if they had an absolute neutrophil count ≥500 cells/mm3. Patients with an absolute neutrophil count 65 years

10 (83.3) 2 (16.7) 62.6 ± 16.0 7 (58.3) 5 (41.7)

hematological malignancye solid tumorf nonmalignant diseases respiratory dysfunctiong gastrointestinal pathologyh othersi

3 (25.0) 5 (41.7) 4 (33.3) 2 (16.7) 2 (16.7) 0 (0.0)

iatrogenic risk factors broad-spectrum antibiotics immunosuppressive therapyj central venous catheters parenteral nutrition abdominal or thoracic surgery hematopoietic transplantation other risk factors underlying malignancy intensive care unit (ICU) stay neutropeniak acute renal failure deathl discharge

10 (83.3) 2 (16.7) 58.1 ± 17.2 7 (58.3) 5 (41.7) Comorbiditiesd 3 (25.0) 5 (41.7) 4 (33.3) 2 (16.7) 2 (16.7) 0 (0.0) Risk Factors for IC

8 (66.7) 4 (33.3) 4 (33.3) 2 (16.7) 2 (16.7) 1 (8.3)

7 (58.3) 5 (41.7) 6 (50.0) 3 (25.0) 3 (25.0) 0 (0.0)

15 (65.2) 5 (21.7) 9 (39.1) 11 (47.8) 9 (39.1) 0 (0.0)

16 (44.4) 9 (25.0) 10 (27.8) 10 (27.8) 6 (16.7) 2 (5.6)

8 (66.7) 2 (16.7) 0 (0.0) 3 (25.0)

8 (66.7) 6 (50.0) 0 (0.0) 1 (8.3) Outcome of Hospital Stay 2 (16.7) 10 (83.3)

9 (39.1) 10 (43.5) 0 (0.0) 1 (4.3)

17 (47.2) 11 (30.6) 0 (0.0) 2 (5.6)

7 (30.4) 16 (69.6)

2 (5.6)m 34 (94.4)m

3 (25.0) 9 (75.0)

a

No significant differences were observed for the comparisons with the training IC patient group. bNo significant differences were found for the comparisons with the test IC patient group, except for outcome of hospital stay (see footnote m). cNo significant differences were noticed for the comparisons with the training non-IC patient group. dOnly the primary condition is given. eIncludes the following diseases: leukemia, lymphoma, myelodysplasia, and multiple myeloma. fIncludes the following diseases: bronchopulmonary neoplasm, pancreas/colon adenocarcinomas, and bladder neoplasm. gIncludes the following diseases: pneumonia, chronic obstructive pulmonary disease, and respiratory distress syndrome. hIncludes the following diseases: cholecystitis, angiocholitis, pancreatitis, peritonitis, and hepatitis. iIncludes the following diseases: multiple trauma, acute renal insufficiency, and diabetes mellitus. jIncludes systemic corticosteroids (≥7.5 mg prednisone per day or equivalent), immunosuppressive or cytotoxic drugs, or total-body irradiation. All patients with systemic corticosteroid therapy received 1−3 mg prednisone per kilogram per day for >15 days. k Neutropenia was defined as an absolute neutrophil count 0.05 for these study groups; in particular, p = 1.0 and 0.9 for patients receiving or not receiving broad-spectrum antibiotics and immunosuppressive therapy; Figure 3C, center and right, respectively). PCA further highlighted that the individual variances of 22-IgG antibody− reactivity profiles differed between IC and non-IC patients (p = 0.007; Figure 3C, left) but did not show significant differences between groups of patients categorized by sex, age, underlying disease or predisposing factors for IC listed in Table 1 (p > 0.05 for these study groups; in particular, p = 0.8 and 0.9 for patients receiving or not receiving broad-spectrum antibiotics and immunosuppressive therapy; Figure 3C, center and right, F



Article

Table 2. Seroprevalence of IgG Antibodies to the C. albicans Intracellular Proteome As Determined by SERPA in NonNeutropenic Patients from the Training Set serum IgG antibodies to the specified C. albicans protein no. (%) of seropositive patients

main C. albicans immunogenic proteinsa

nameb

functionb

CGD accession number

Mrc

pIc

IC patients (n = 12)

non-IC patients (n = 12)

I. Cell Rescue, Defense, and Virulence a. Molecular Chaperones Hsp90e 90 kDa heat shock protein CAL0003079 82 4.48 6 (50) 0 (0) Ssb1e heat shock protein of the CAL0001367 66 4.86−5.03 5 (42) 0 (0) HSP70 family Ssc1 heat shock protein of the CAL0001828 68 4.81−4.96 6 (50) 1 (8) HSP70 family Msi3/Sse1 heat shock protein of the CAL0001523 80 5.13−5.18 4 (33) 0 (0) HSP70 family b. Detoxification, Stress Response, and Methylglyoxal Pathway-Related Proteins Glx3/Ipf17186e glutathione-independent CAL0004002 27 4.32−4.40 6 (50) 0 (0) glyoxalase Grp2 methylglyoxal reductase CAL0000895 37 5.95 5 (42) 0 (0) II. Carbon Compound and Carbohydrate Metabolism a. Glycolysis/Gluconeogenesis Pathway-Related Enzymes Fba1e fructose-bisphosphate CAL0003619 39 6.00 6 (50) 1 (8) aldolase Tpi1 triose-phosphate CAL0004861 26 5.59−5.85 4 (33) 1 (8) isomerase e,h glyceraldehyde-3CAL0005657 35 7.00−7.38 5 (42) 1 (8) Tdh3/Gap1 phosphate dehydrogenase Pgk1e phosphoglycerate kinase CAL0000415 45−46 5.62−6.12 10 (83) 3 (25) Eno1e enolase CAL0004953 48−49 5.20−5.65 12 (100) 5 (42) Cdc19e pyruvate kinase CAL0005977 59 6.64−6.89 6 (50) 0 (0) b. Fermentation Pathway-Related Enzymes Pdc11e pyruvate decarboxylase CAL0005202 60−63 5.00−5.22 7 (58) 6 (50) Adh1e alcohol dehydrogenase CAL0003176 44−45 5.56−5.83 7 (58) 2 (17) c. Glyoxylate and Tricarboxylic Acid Cycles-Related Enzymes Aco1 aconitase CAL0001406 84 5.82−5.90 4 (33) 1 (8) Mdh1e mitochondrial malate CAL0005697 35 5.56 5 (42) 0 (0) dehydrogenase d. Pentose Phosphate Pathway-Related Enzymesh Tkl1e transketolase CAL0002508 73 5.54−5.59 3 (25) 1 (8) III. Amino Acid Metabolism and S-Adenosylmethionine Cycle Met6e cobalamin-independent CAL0002475 84−85 5.39−5.55 8 (67) 1 (8) methionine synthase Sah1 S-adenosyl-LCAL0002855 50 5.37 3 (25) 1 (8) homocysteine hydrolase IV. Nucleotide, Nucleoside, and Nucleobase Metabolism Imh3 inosine monophosphate CAL0000509 57 6.67 4 (33) 0 (0) dehydrogenase V. Phosphate-Containing Compound Metabolism Ipp1 inorganic pyrophosphatase CAL0006016 36 4.78 3 (25) 0 (0) VI. Protein Synthesis and G-Protein-Coupled Receptor Signaling Pathway Asc1/Bel1 40S ribosomal subunit CAL0000124 31 6.07 4 (33) 3 (25) similar to G-β subunits

% difference (95% CI) in seropositivity rate (IC vs non-IC patients)d

50 (17 to 83)f,g 42 (9 to 74)f,g 42 (5 to 78)g 33 (2 to 65)g

50 (17 to 83)f,g 42 (9 to 74)f,g

42 (5 to 78)g 25 (−10 to 60) 33 (−3 to 69)g 58 (23 to 94)f,g 58 (26 to 91)f,g 50 (17 to 83)f,g 8 (−36 to 52) 42 (3 to 80)g 25 (−10 to 60) 42 (9 to 74)f,g

17 (−16 to 49) 58 (24 to 93)f,g 17 (−16 to 49)

33 (2 to 65)g

25 (−4 to 54) 8 (−32 to 48)

a

C. albicans immunogenic proteins were identified using our published reference 2-D map35 (also available on our COMPLUYEAST-2DPAGE database).42,43 These proteins were identified previously by peptide mass fingerprinting.35 Most identities were further characterized by MS/MS.32,35 b Protein names and functions are reported according to CGD and CandidaDB. When protein names differ between both databases, the first and second names correspond to those from CGD and CandidaDB, respectively. cExperimental Mr and pI values estimated by using the ImageMaster 2D Platinum software. dRepresents the percentage difference in seropositivity rate for IgG antibodies to the specified C. albicans protein (i.e., in frequency of recognition for such a protein) in IC patients minus that in non-IC patients. eAntigenic or immunogenic properties for recombinant forms of the specified C. albicans protein were described in previous studies.16−19,36,37,45,71−77 fSeropositivity rate for IgG antibodies to the specified C. albicans protein was significantly higher in IC patients than in non-IC patients (p < 0.05). gMedian seroreactivity level of IgG antibodies to the specified C. albicans protein was significantly higher in IC patients than in non-IC patients (p < 0.05). hTdh3/Gap1 also participates in pentose phosphate pathway. G



Article

Figure 2. SERPA-detected 22-IgG antibody-seropositivity patterns from non-neutropenic patients in the training set. (A) Distribution of 22-IgG antibody seropositivity rates in the training set. Horizontal lines denote medians, and vertical lines denote interquartile ranges. The cutoff threshold defined by ROC analysis is shown (0.21). The shaded area depicts non-neutropenic patients at low risk for IC. (B) Correlation between 22-IgG antibody seropositivity rates and seroreactivity levels in the training set. Patient diagnosis is color-coded as indicated. ρ, Spearman’s rho. (C) Unsupervised oneway HCA of the seropositivity rates for the 22 IgG antibodies in the training set. Dendrogram and heat map display clustering of serum IgG antibodies from non-neutropenic patients according to the similarities in their seropositivity rates across the sample groups (left panel). The seropositivity rates for each IgG antibody (oblongs) are color-coded, as described in the color bar. The main clusters of IgG antibodies (A−D) that exhibited different seropositivity patterns across the sample groups are color-coded as depicted. Clusters A, B, and C comprised IgG antibodies directed against C. albicans antigens that were recognized at high/very high, high, and medium frequencies, respectively, in acute IC (i.e., with sera from at least five IC patients from the training set). Cluster D grouped IgG antibodies directed against C. albicans antigens that were detected at low frequencies in acute IC. In particular, 13/14 and 2/8 IgG antibodies included in clusters A−C and D, respectively, were useful candidate biomarkers for IC (Table 2). Asterisks indicate IgG antibodies that differed in their seroprevalence between the IC and non-IC groups. Daggers show IgG antibodies that exhibited significant differences in their seroreactivity levels between both groups and that could be useful candidate biomarkers for IC diagnosis. Protein names refer to those in Table 2. I, IC patients; N, non-IC patients. Representative seropositive rates (center panels) and seroreactivity levels (right panels) for four of these IgG antibodies within each cluster (A−D) are illustrated. The percentage difference in seropositivity rate for each IgG antibody in IC patients minus that in non-IC patients is given (center panels; see Table 2 for further details). In the box-and-whisker plots (right panels), the boxes denote the interquartile ranges (25th to 75th percentiles), the horizontal thick lines portray the medians, the black squares depict the means, the whiskers extend to 1.5 times the interquartile range, the circles represent the outliers, and the asterisks correspond to the extreme values. H



Article

Figure 3. Unsupervised clustering analyses of SERPA-detected 22-IgG antibody−reactivity profiles from non-neutropenic patients in the training set. (A) Unsupervised two-way HCA of 22-IgG antibody−reactivity patterns in the training set. Dendrograms and heat map show clustering of serum specimens (columns; n = 24) and IgG antibodies (rows; n = 22) from non-neutropenic patients based on the similarities in the 22-IgG antibody− reactivity profiles of each serum and the reactivity patterns of each IgG antibody across all serum samples, respectively. Red or green oblongs display seroreactivity levels above or below, respectively, the median value (black oblongs). The two main IgG antibody−reactivity signatures (M and L) that segregated training specimens into two clusters according to IC diagnosis (I and N) are color-coded as shown. Protein names refer to those in Table 2. IC, IC patients; N, non-IC patients. (B) Main IgG antibody−reactivity signatures in the training set. Sample dendrograms are the same as in panel A, and heat maps are also color-coded as described in panel A. Average reactivity profiles of the IgG antibodies integrated into each serologic signature (clusters M and L) are shown for each training patient. (C) Unsupervised PCA of 22-IgG antibody−reactivity profiles from the training patients within a 3-D vector space. The percentages of variance explained by the first three principal components (PC1, PC2, and PC3) are indicated on their corresponding axes. Each circle represents the 22-IgG antibody−reactivity pattern of an individual sample. Specimens are color-coded as depicted. The color-shaded areas illustrate sample clustering. Asterisks and daggers indicate the degree of homology (relative similarity or overlapping) and homogeneity (relative variation) of 22-IgG antibody−reactivity profiles between the study groups and within each study group, respectively. I



Article

Figure 4. GO term enrichment and functional protein association network analyses of the 22 SERPA-identified C. albicans immunogenic proteins. (A) Enriched GO terms from the cellular component (CC; top left), biological process (BP; top right), and molecular function (MF; bottom) ontologies in the 22 C. albicans antigens. The squares indicate the ratio of the number of genes encoding the 22 antigens (input list) to the number of genes from the whole C. albicans genome in the specified GO term. The solid bars correspond to the negative natural logarithm of the p value (with Bonferroni correction) associated with the enrichment of the specified GO term in the input list of genes. Asterisk denotes that the FDR for GO term enrichment was different from zero (0.13−2.29%). (B) Network of predicted functional associations among the 22 antigens. The network nodes represent proteins and show their 3-D structures or structure models if these are available in the STRING database. Every color of the nodes corresponds to a distinct functional module (I−IV) in the PPI network. The edges portray pairwise interactions (predicted functional associations). The blue lines indicate that the mode of action of PPI is through binding. The thicker lines illustrate stronger associations. The dashed lines depict intermodule edges. The colorshaded areas show the antigens recognized by the 2-IgG antibody signature. Protein names refer to those in Table 2.

processes (such as glycolysis and gluconeogenesis, respectively) as well as pyruvate metabolic and oxidation−reduction processes. On the contrary, modules III and IV showed no bias toward any GO term but integrated IgG targets related to S-adenosylmethionine cycle and methylglyoxal pathway, respectively (Table 2).

into four functional modules (I−IV; Figure 4B). Modules I and II displayed higher connectivity of nodes in this PPI network and were enriched for common (response to host defenses) and specific ontologies. Module I comprised IgG targets involved in protein (re)folding, whereas module II included proteins associated with carbohydrate catabolic and biosynthetic J



Article

Figure 5. Analytical performance of the ELISA prototype for anti-Hsp90 IgG antibody quantification. (A) C. albicans rHsp90. After GST removal, purified rHsp90 was visualized on a 10% SDS-polyacrylamide gel stained with silver nitrate. Lane M, molecular mass standards; lane 1, rHsp90. (B) Reactivity of the purified rHsp90 with sera from IC and non-IC patients. Lanes 1 and 3, Ponceau S red-stained blots of rHsp90 (loading controls of lanes 2 and 4, respectively). Lanes 2 and 4, representative SDS-PAGE immunoblots of rHsp90 hybridized with serum samples from IC and non-IC patients, respectively. (A,B) Arrowheads show the relative position of rHsp90. (C) Calibration curve. The dose−response curve (absorbance vs concentration) was established by two-fold serial dilutions of the calibrator (a positive in-house reference serum with an arbitrary anti-Hsp90 IgG antibody concentration of 1000 RU/mL) and fit to a four-parameter logistic-regression model. Each point represents the mean absorbance (for the relative calibrator concentration) of seven different analytical immunoassays. The numbers above each point indicate the ratio of the signal at that relative calibrator concentration to the signal for the zero calibrator. (D) Imprecision profile. Interassay CVs at different concentrations of the calibrator assessed in duplicate on seven nonconsecutive days were fit to a quadratic-regression model. The shaded rectangle depicts the portion of the curve with an optimal interassay imprecision. (C,D) AU, arbitrary units; RU, reference units; Sy/x, standard deviation about the regression line; R2, goodness-of-fit statistic; LOD, limit of detection; LOQ, limit of quantification.

Validation of Anti-Hsp90 IgG Antibodies as an IC Biomarker in Non-Neutropenic Patients

Analytical Performance of the ELISA Prototype for Anti-Hsp90 IgG Antibody Quantification. To guarantee the reliability of results obtained using this ELISA prototype, we examined indicators of its analytical measurement range, precision, and accuracy after its empirical optimization and before assessing its clinical usefulness.47,48 The calibration curve and imprecision profile of this immunoassay revealed good log− linear response in the interval of 0.3−40 RU/mL (Figure 5C) and high reproducibility within this range (Figure 5D). Its limits of detection and quantification were 0.005 and 0.18 RU/mL, respectively. Acceptable intra- and interassay imprecision values were also observed using three serum samples with different concentrations of anti-Hsp90 IgG antibodies (5.7 and 12.1% at 0.7 RU/mL, 3.3 and 6.9% at 2.1 RU/mL, and 5.3 and 11.2% at 10.2 RU/mL, respectively). Assay linearity and analytical accuracy analyses were then carried out on six serum specimens with different anti-Hsp90 IgG antibody concentrations. These samples gave good signal linearity as a function of dilution (median dilution recovery, 102%; range, 88−113%). Likewise, adequate assay accuracy was demonstrated by the nearly complete analytical recovery of 0.5, 1.5, and 3.0 RU/mL anti-Hsp90 IgG antibodies exogenously added to these six specimens (median, 97%; range, 86−109%).

Anti-Hsp90 IgG antibodies were selected for initial validation studies of SERPA results because they were among the useful candidate IC biomarkers not detected in serum samples from non-IC patients (Table 2) and showed a significantly greater difference in seropositivity rate and seroreactivity level between IC and non-IC patients (subcluster C2 and cluster M in Figures 2C and 3A, respectively, and Table 2). To confirm and extend these observations, we set up an ELISA prototype for their quantification using a C. albicans rHsp90 as an immunodiagnostic reagent in another cohort of serum specimens from nonneutropenic patients at high risk for IC (test set; Table 1). This rHsp90, with an apparent molecular mass of 82 kDa, was successfully expressed in E. coli and purified by affinity chromatography (Figure 5A). Its identity was verified by MS (Figure S1 in the Supporting Information), and its antigenicity was evaluated by SDS-PAGE Western blot assays using four IC and non-IC sera (Figure 5B). This rHsp90 was specifically recognized by serum IgG antibodies elicited in acute IC, suggesting its suitability for prototype assay development (validation phase). K



Article

These recovery values highlighted that neither sample dilution nor possible interfering molecules in the sample matrix had an important effect on our prototype assay. Diagnostic Performance of the ELISA Prototype for Anti-Hsp90 IgG Antibody Quantification. Having shown the robust analytical performance of our ELISA prototype, we then focused on the evaluation of its diagnostic performance in the test set (23 IC and 36 non-IC patients; Table 1). Serum anti-Hsp90 IgG antibody concentrations were higher in IC patients than in non-IC patients (median, 1.6 vs 0.3 RU/mL; p < 0.001; Figure 6A). This ELISA prototype was able to reasonably discriminate IC from non-IC patients (ROC area, 0.77; 95% CI, 0.64−0.90; p < 0.001; Figure 6B). ROC analysis further indicated that IC was diagnosed in this set with 52% (95% CI, 39−65%) sensitivity, 97% (93−100%) specificity, 92% (86− 99%) positive predictive value (PPV), 76% (65−87%) negative predictive value (NPV), and 80% (69−90%) accuracy using a cutoff threshold of 1.2 RU/mL (Table 3). All of these results agreed well with SERPA data (Figure 2C and Tables 2 and 3). Multivariate logistic-regression models, adjusted for known predisposing factors for IC and other baseline variables (listed in Table 1), revealed that each unit increase in anti-Hsp90 IgG antibody concentration was associated with 7.9 times higher IC risk in the test set (95% CI, 2.1−29.2; p = 0.002). This association was stronger when patients were categorized according to the optimum diagnostic cutoff threshold defined by the ROC plot (multivariate-adjusted OR, 56.0; 95% CI, 5.9−528.0; p < 0.001). To further explore the relationship between anti-Hsp90 IgG antibody concentrations and IC risk in the test set, we stratified these concentrations into tertiles, based on their distribution in the non-IC group, and calculated the ORs for IC in patients with higher (second and third) concentration tertiles compared with those with the lowest (first) concentration tertile (Figure 6C). Although IC risk did not differ between the patients with lower (first and second) concentration tertiles (OR, 1.0; 95% CI, 0.2− 6.0; p = 1.0), it increased sharply in those with the highest (third) concentration tertile (OR, 5.7; 95% CI, 1.3−24.5; p = 0.02), suggesting a threshold effect (p = 0.01 for trend) rather than a continuous dose−response relationship between the anti-Hsp90 IgG antibody concentrations and IC risk. This trend remained significant after controlling for potential confounding factors (sex, age, comorbidities, and known risk factors for IC listed in Table 1) in the relationship between antibody concentrations and IC risk. Diagnostic Value of a Serum IgG Antibody Signature Directed against C. albicans Hsp90 and Eno1 in Non-Neutropenic Patients at High-Risk for IC

Figure 6. Diagnostic performance of the ELISA prototype for antiHsp90 IgG antibody quantification in non-neutropenic patients from the test set. (A) Distribution of serum anti-Hsp90 IgG antibody concentrations in the test set. Horizontal lines depict medians, and vertical lines depict interquartile ranges. The cutoff threshold for a positive result as defined by ROC analysis is indicated (1.2 RU/mL). The shaded area represents a seronegative status. (B) ROC curve for serum anti-Hsp90 IgG antibody concentrations in the test set. The diagnostic cutoff threshold is labeled. See Table 3 for details. (C) Risk of IC according to serum anti-Hsp90 IgG antibody concentrations in the test set. Serum anti-Hsp90 IgG antibody concentrations were stratified into tertiles relative to the distribution of values in the non-IC group. Samples in the lowest (first) antibody tertile served as the reference group (OR = 1.0). RU, reference units; AUC, area under the curve.

It is increasingly evident that no single biomarker is sufficiently sensitive and specific on its own for the early detection of some infectious diseases and that combinations of multiple biomarkers may be needed for a reliable diagnosis.49−51 Because anti-Hsp90 IgG antibodies showed high PPV but moderate NPV for IC identification, we asked whether their diagnostic accuracy could be improved by combining them with other candidate IC biomarkers, like anti-Eno1 IgG antibodies with higher sensitivity, as gathered from SERPA results (Tables 2 and 3) and previous data.36,52 Comparison of IgG Antibodies to Hsp90 and Eno1 for IC Diagnosis. To test this possibility, we first evaluated the clinical concordance between IgG antibodies to Hsp90 and Eno1 for IC detection in the test set, as measured by our prototype

assay and a well-established ELISA prototype,36 respectively (Figure 7A). These validated biomarkers had fair agreement for L


Assay or model cutoff thresholds were defined by ROC plots (Figure 8C). These discrimination thresholds are given in AU or RU/mL for the single IC biomarkers evaluated by SERPA or ELISA (in the training or test sets), respectively, whereas they are dimensionless for the 2-IgG antibody signature. bValue ranging from 0.7 to 0.8 represents reasonable discrimination, while a value over 0.8 indicates good discrimination.46 cp = 0.04 as compared with an area of 0.5 for a useless test. dp < 0.001 as compared with an area of 0.5 for a useless test. ep = 0.001 as compared with an area of 0.5 for a useless test.

0.93 (0.84−1.00)d 0.95 (0.87−1.00)d 0.86 (0.76−0.97)d 0.88 (0.75−1.00)e 0.77 (0.64−0.90)d 0.75 (0.55−0.95)c

IC prediction at their cutoff thresholds (κ = 0.35; p = 0.006), an important indication that they discriminate IC from non-IC patients (p < 0.001 for both assays) in a distinct manner. In fact, although 33 (92%) out of the 36 non-IC patients were seronegative for IgG antibodies to Hsp90 and Eno1, only 8 (35%) out of the 23 IC patients were seropositive for both of them using these thresholds. We then estimated the ORs for IC with increasing (low, medium and high) anti-Eno1 IgG antibody concentrations, combined with two different (low-medium and high) concentrations of anti-Hsp90 IgG antibodies, as quantified by their prototype assays in the test set (Figure 7B). At each anti-Eno1 IgG antibody concentration (low, medium, and high), patients with high anti-Hsp90 IgG antibody concentrations had greater IC risk than those with low-medium concentrations. In contrast, anti-Eno1 IgG antibodies predicted IC in both groups of patients in a different way. A threshold effect between anti-Eno1 IgG antibody concentrations and IC risk was observed in patients with low-medium anti-Hsp90 IgG antibody concentrations, while a surprising graded, positive dose−response relationship was observed in those with high anti-Hsp90 IgG antibody concentrations (p = 0.001 for trend). Adjustment for sex, age, and underlying diseases did not affect these distinct patterns nor did further control for the putative confounding effects of established IC risk factors (listed in Table 1). The Hosmer− Lemeshow test indicated a good fit of this interaction model to the data (χ2 = 4.7; five degrees of freedom; p = 0.5). PPI network analysis revealed that Hsp90 and Eno1 did not interact with each other and clustered into distinct modules in the network of predicted functional associations among the 22 identified antigens (I and II, respectively; Figure 4B). GO term enrichment analysis of these two IgG-targets further highlighted that the cell surface/wall and protein binding ontologies were only over-represented. Creation of a 2-IgG Antibody Signature-Based Discriminator for IC. Having proven that these serologic biomarkers differently detected IC in non-neutropenic patients, we built a diagnostic model (discriminator) for IC based on their combined molecular fingerprints to determine whether this could provide better IC diagnostic accuracy than the individual biomarkers alone. For its construction, reactivity profiles of IgG antibodies to Hsp90 and Eno1, as evaluated by SERPA in the training set, were modeled using supervised stepwise multivariate discriminant analysis. The 2-IgG antibody signature scores derived from this model were higher in IC patients than in nonIC patients (median, 0.6 vs −0.9; p < 0.001; Figure 8A, left). A score equal to or above −0.4 was predictive of an elevated risk of IC and properly classified 21 out of the 24 non-neutropenic patients from the training set (Figure 8B, left), resulting in 75% (95% CI, 58−92%) sensitivity, 100% specificity, 100% PPV, 80% (64−96%) NPV, and 88% (74−100%) accuracy for IC detection (Table 3). In addition to a good discriminative power for the 2-IgG antibody signature in this set of patients (ROC area, 0.95; 95% CI, 0.87−1.00; p < 0.001), it was, also, expectedly more accurate at differentiating IC from non-IC patients than antiHsp90 IgG antibodies alone (c-statistic, 0.95 vs 0.75; Z = 2.6; p = 0.01; Table 3 and Figure 8C, left). Validation of the 2-IgG Antibody Signature-Based Discriminator for IC. To validate the robustness of this SERPAbased diagnostic model for IC, a similar 2-IgG antibody signature score was formulated using supervised stepwise multivariate discriminant analysis of serum concentrations of IgG antibodies to Hsp90 and Eno1, as measured by their prototype assays in the

a

28.17 (16.70−39.65) 0.22 (0.12−0.33) 126.00 (41.32−210.68) ∞ 0.25 (0.07−0.42) ∞ 12.52 (4.08−20.97) 0.32 (0.20−0.44) 38.86 (26.42−51.29) 18.78 (8.82−28.75) 0.49 (0.36−0.62) 38.18 (25.78−50.58) ∞ 0.50 (0.30−0.70) ∞

8.00 (6.40−9.60) 0.36 (0.17−0.56) 22.00 (5.43−38.57)

78 (68−89) 97 (93−100) 95 (89−100) 88 (79−96) 90 (82−98) 75 (58−92) 100 100 80 (64−96) 88 (74−100) 70 (58−81) 94 (89−100) 89 (81−97) 83 (73−93) 85 (76−94) 67 (48−86) 92 (81−100) 89 (76−100) 73 (56−91) 79 (63−95) 52 (39−65) 97 (93−100) 92 (86−99) 76 (65−87) 80 (69−90) 50 (30−70) 100 100 67 (48−86) 75 (58−92)

test set (n = 59)

≥−0.1 ≥−0.4

training set (n = 24) test set (n = 59)

≥1.6 ≥39.5

training set (n = 24) test set (n = 59)

≥1.2 ≥3.0

training set (n = 24) test operating characteristics

Article

assay/model cutoffa percentage (95% CI) sensitivity specificity PPV NPV accuracy ratio (95% CI) positive likelihood ratio negative likelihood ratio diagnostic odds ratio mean (95% CI) ROC areab

2-IgG antibody signature anti-Eno1 IgG antibodies anti-Hsp90 IgG antibodies

Table 3. Test Operating Characteristics of IgG Antibodies to Hsp90 and Eno1 Alone and in Combination (2-IgG Antibody Signature) For IC Diagnosis As Measured by SERPA or ELISA in the Training or Test Sets, Respectively


M



Article

Figure 7. Evaluation of the interaction between serum IgG antibodies to Hsp90 and Eno1 for IC prediction in non-neutropenic patients from the test set. (A) Comparison of prediction strengths of serum concentrations of IgG antibodies to Hsp90 and Eno1 in the test set. A scatter plot is shown in the bottom left panel. The blue- and pink-shaded areas display clinical concordance in the detection of non-neutropenic patients at low-risk and high-risk, respectively, for IC between both biomarkers at their cutoff thresholds. Patient diagnosis is color-coded as indicated. The horizontal and vertical lines denote the diagnostic cutoff thresholds of IgG antibodies to Hsp90 and Eno1, respectively, for IC as defined by ROC plots. See Table 3 for details. κ, Cohen’s kappa. The histograms depict the distributions of concentrations in the test set, whereas the box-and-whisker plots stratify them on the basis of the two study groups (top left and bottom right panels). The boxes represent the interquartile ranges (25th to 75th percentiles), the horizontal thick lines show the medians, the black squares portray the means, the whiskers extend to 1.5 times the interquartile range, the circles indicate the outliers, and the asterisk illustrates the extreme values. (B) Risk of IC according to serum anti-Eno1 and anti-Hsp90 IgG antibody concentrations in the test set. IgG antibody concentrations were categorized as low (equal to or below the 50th percentile), medium (from above the 50th percentile to the 90th percentile), or high (above the 90th percentile) relative to values in the non-IC group. Non-neutropenic patients with low-medium anti-Hsp90 and low anti-Eno1 IgG antibody concentrations served as the reference group (OR = 1.0).

above −0.1 was associated with a significant increase in IC risk and correctly classified 53 out of the 59 non-neutropenic patients from the test set (Figure 8B, right), leading to 78% (95% CI,

test set. In agreement with SERPA data, IC patients had higher scores than non-IC patients (median, 0.2 vs −0.6; p < 0.001; Figure 8A, right). A 2-IgG antibody signature score equal to or N



Article

Figure 8. Diagnostic performance of the 2-IgG antibody signature assessed by SERPA or ELISA in non-neutropenic patients from the training or test sets, respectively. (A) Distribution of the 2-IgG antibody signature scores determined by SERPA or ELISA in the training (left) or test (right) sets, respectively. Horizontal lines represent medians, and vertical lines represent interquartile ranges. The cutoff thresholds for a positive result according to ROC plots in the training and test sets are indicated (−0.4 and −0.1, respectively). The shaded areas display non-neutropenic patients at low-risk for IC. (B) Probabilities of having or not having IC for each patient in the training (left) or test (right) sets predicted by the 2-IgG antibody signature-based diagnostic model as evaluated by SERPA or ELISA, respectively. Each circle depicts an individual serum specimen. Patient diagnosis is color-coded as shown. The blue- and pink-shaded areas display an elevated model-predicted probability of being a patient at low-risk and high-risk, respectively, for IC. The best probability thresholds to discriminate between predicted IC and non-IC patients are labeled (0.35−0.34 and 0.65−0.66). Asterisks indicate misclassified samples. Planes portray the diagnostic cutoff thresholds of the 2-IgG antibody signature scores as estimated by SERPA or ELISA in the training (left) or test (right) sets, respectively. (C) ROC curves for serum IgG antibodies to Hsp90 and Eno1 alone and in combination (2-IgG antibody signature) examined by SERPA or ELISA in the training (left) or test (right) sets, respectively. The cutoff thresholds of the different assays and models are shown. See Table 3 for details.

68−89%) sensitivity, 97% (93−100%) specificity, 95% (89− 100%) PPV, 88% (79−96%) NPV, and 90% (82−98%) true

predictive accuracy for IC identification (Table 3). This 2-IgG antibody signature yielded higher sensitivity for IC diagnosis O



Article

characterization of these proteins as C. albicans antigens rather than experimental artifacts or false identity assignments.78 However, further validation assays that use recombinant forms of the remaining identified proteins (also described previously as C. albicans antigens using different SERPA strategies)37,79,80 should be carried out in future research to independently confirm their antigenic and immunogenic properties and thus their identity. It is also worth emphasizing that this was not an exhaustive study of the C. albicans immunome characterization. Although the intracellular proteome of C. albicans is an important and easy-to-explore source of serodominant and serodiagnostic antigens,35−38 other subsets of its proteome may also reveal additional immunogenic proteins.15,17 Therefore, our approach should be extended to other C. albicans subproteomes (preferably during in vivo growth in each stage of the infectious process) using a larger set of serum samples to attain a more comprehensive view of the C. albicans immunome and the complexity of the antibody responses during IC development and progression in non-neutropenic patients.

than anti-Hsp90 IgG antibodies alone at their cutoff thresholds (78% vs 52%; p = 0.03). Its ability to discriminate IC from non-IC patients (ROC area, 0.93; 95% CI, 0.84−1.00; p < 0.001) was also higher than IgG antibodies to Hsp90 or Eno1 alone (c-statistic, 0.93 versus 0.77 or 0.86; Z = 2.6 or 2.1; p = 0.01 or 0.04, respectively; Table 3 and Figure 8C, right). ROC analysis further demonstrated that the 2-IgG antibody signature was more accurate at detecting IC than traditional risk factors (listed in Table 1).

■

DISCUSSION

IgG Antibody Response to the C. albicans Intracellular Proteome in Non-Neutropenic Patients Is Heterogeneous, Selective, and Biased toward Ubiquitous and Multifunctional Proteins

We found that IgG antibody−reactivity profiles to the C. albicans intracellular proteome differed from one non-neutropenic patient to another. This heterogeneous antigen recognition, also noted in other infectious diseases,49,53−55 may be attributed to factors related to the host (e.g., immune status, comorbidity, clinical outcome, or demographic or risk factors), fungus (e.g., burden, strain, morphological form or growth phase), or infection (e.g., stage, site, or severity). This observation has major implications for immunodiagnostics because this diversity in antibody response necessitates the use of rational combinations of several serologic biomarkers to accurately detect IC in a clinical setting (see below). Despite the high heterogeneity, these IgG antibody responses consistently focused on a small subset of the C. albicans intracellular proteome enriched for ubiquitous (extracellular, cell surface/wall, plasma membrane, and cytosolic) and multifunctional (moonlighting) proteins. The serodominant nature of these internal antigens could arise from their release to the extracellular medium after cell lysis or nonclassical secretion,48,56,57 cell surface-association,58−60 inducible nature under different conditions related to IC pathogenesis,61−65 and multiple or moonlighting roles during IC establishment and progression (adhesion to host cells, invasion of host tissues, biofilm formation, morphological transition, drug resistance, adaptive stress response, and metabolic flexibility, among others).59,66−69 PPI network and GO term enrichment analyses of the immunological signature associated with IC unveiled that the identified IgG antibody repertoires showed a further bias toward C. albicans proteins that interact within known or predicted complexes or functional modules and that are involved in carbohydrate catabolic and biosynthetic processes (e.g., glycolysis and gluconeogenesis, respectively), pyruvate metabolism, starvation response, oxidation−reduction processes, and protein (re)folding. Because these proteins are synthesized during in vivo infection, as gathered from their immunogenic nature, these biological processes could partially reflect underlying physiological status in the intracellular compartment (before their release to the cell surface or extracellular medium) during host− pathogen interaction. Accordingly, these data offer some insight into the metabolic pathways possibly required for adaptation of C. albicans to distinct host microniches (with differing carbohydrate levels and nutrient availability)62,63,70 during IC development in non-neutropenic patients. Consistent evidence that recombinant forms of about twothirds of the SERPA-identified proteins can bind serum antibodies elicited during candidiasis or induce protective antibody responses during experimental C. albicans infection (Table 2)16−19,36,37,45,71−77 provides additional support for the

Serum 22-IgG Antibody−Reactivity Signature May Discriminate IC from Non-IC Patients

Unsupervised clustering analyses highlighted that IC patients mounted more heterogeneous and robust IgG antibody responses to 22 C. albicans antigens than non-IC patients and that this 22-IgG antibody−reactivity signature discriminated IC from non-IC patients. In accordance with earlier reports in other high-risk populations,15,17,35,36 these results support an adaptive immune response to C. albicans during invasive infection different from the humoral immunity during commensal sensitization. Univariate analyses further indicated that 15 out of the 22 signature IgG antibodies, which were mainly directed against C. albicans antigens recognized at very high, high, and medium frequencies in acute IC, could be useful candidate IC biomarkers in non-neutropenic patients. These findings are important for translational research. However, the translation of candidate biomarkers for clinical use is challenging and often limited by bottlenecks in the proteomic biomarker pipeline (such as validation and clinical assay development).47,48 As a first step toward clinical implementation, one of these SERPA-identified candidates (anti-Hsp90 IgG antibodies) was transferred onto a more universal platform appropriate for clinical deployment (an ELISA prototype) and validated, both analytically and clinically, in a different set of non-neutropenic patients (see below). Elevated Serum Concentrations of Anti-Hsp90 IgG Antibodies May Be Useful for Ruling in IC in Non-Neutropenic Patients

Although previous studies have shown the usefulness of antibodies to C. albicans Hsp90 for IC diagnosis,18,81 to our knowledge, no quantitative validation has been carried out so far. In these works, a 47 kDa breakdown product, a conserved epitope, or the C-terminal fragment (rather than the full-length sequence) of Hsp90 was used as an immunodiagnostic reagent. These differences, along with the different study populations or clinical settings, might in part account for the relatively better diagnostic accuracy of our quantitative (observer-independent) assay, compared with those obtained previously. Our prototype assay revealed that anti-Hsp90 IgG antibodies at elevated concentrations (≥1.2 RU/mL) reliably ruled in IC in non-neutropenic patients, but lower concentrations did not rule out this diagnosis. If validated in larger patient cohorts, an important practical implication of this finding is that elevated P



Article

Eno1 are complementary, rather than redundant, for IC identification and that the observed differences between both biomarkers for IC diagnosis and risk prediction could be synergistic in our model. This additional discriminative power could stem from the combined detection of ubiquitous IgG-targets (located both in the cytoplasm and at the surface/wall of C. albicans cells and in the host bloodstream)58,59,82,92 that span differing states of IC pathogenesis and show no known or predicted functional associations between them (Figure 4B). In particular, Hsp90 is required for morphogenesis, biofilm dispersion, environmental stress response, drug resistance, and apoptosis,67,70,83,84,93,94 whereas Eno1 is involved in adhesion to host cells, invasion of host cells and tissue barriers, biofilm formation, and metabolic adaptation.66,68−70 Given that Hsp90 and Eno1 may contribute to the pathogenic potential of C. albicans in a different way,66−68,70 it is biologically plausible that the combined molecular fingerprints of their elicited antibodies might offer a more coherent snapshot of the differing, highly dynamic, heterogeneous, and complex states of host−pathogen interaction in acute IC than the individual fingerprints alone and therefore bring to light additional and nonredundant information for IC diagnosis.

concentrations of these antibodies might aid clinicians to make a more rational therapeutic decision in patients at high-risk for IC. This major IgG antibody response to Hsp90 in acute IC may be ascribed, as previously mentioned, to its extracellular (host bloodstream) and cell surface location,57,58,82 great abundance in IC,64,82 and diverse contributions to pathogenicity (morphological switching, biofilm formation, and adaptive stress response).67,70,83,84 These attributes could allow Hsp90 to become more rapidly available for the host immune system in an early stage of IC. In contrast, the reduced, or even absent, IgG antibody response to Hsp90 in non-IC patients suggests that this molecular chaperone could be less abundant during commensal growth82 and, therefore, less visible for immune recognition in these patients. Such a minor IgG antibody response to Hsp90 also raises the possibility that presentation of this ubiquitous, evolutionarily conserved and immunogenic protein to the mucosal immune system during commensal colonization might favor the induction of tolerance (rarely complete), in lieu of immune activation, in an attempt to avoid the development of deleterious autoimmune responses.85 Several factors may explain the false-negative results of our prototype assay. Because antibody response to Hsp90 confers protection against IC,77,86,87 this could be absent or reduced in IC patients with fatal outcomes, as noted in this and other studies,35,37,81 or delayed in acute IC and only triggered at an early/mid convalescent stage of IC.17,18,35 Furthermore, excess circulating Hsp90 during IC82 might neutralize or mask its elicited antibodies by creating immune complexes88 or result in an unresponsive or tolerance state.89 It is thus conceivable that the diagnostic accuracy of this IC biomarker may be improved by its regular serological monitoring or combination with more sensitive IC biomarkers, as explained later.

Serum IgG Antibody Signature Directed against Hsp90 and Eno1 May Serve as a Basis for the Rational Design of IC Immunodiagnostics in Non-Neutropenic Patients

ROC analyses showed that the serum signature of IgG antibodies to Hsp90 and Eno1 provided better discrimination between IC and non-IC patients than the individual IgG antibodies alone in a different set of non-neutropenic patients using an independent assay. This finding validates this 2-IgG antibody signature as a reliable IC discriminator with potential for application in the clinical setting and further reinforces the growing consensus that biomarker combinations are necessary to achieve a more accurate diagnosis of IC.10,14−19 However, prior to its implementation in clinical practice, our IC diagnostic signature must be refined and validated in future prospective, larger-scale, multicenter studies using multiplexed assays. If confirmed independently, nonneutropenic patients with a high-risk signature could benefit from antifungal therapy, while those with a low-risk signature might avoid, with a reasonable probability, overtreatment with unnecessary, highly nephrotoxic, and costly antifungal agents. This antibody signature could further provide a rationale for future studies focused on improving diagnostic tests for IC.

Serum Anti-Hsp90 IgG Antibody Concentrations Show a Threshold Effect on the IC Risk in Non-Neutropenic Patients

Another remarkable finding was the threshold effect, as opposed to a graded, positive dose−response relationship between antiHsp90 IgG antibody concentrations and IC risk. Although the immunological mechanism for this effect remains to be elucidated, this threshold (above which the IC risk became significant) might reflect the ability of the host immune system to actively discriminate dangerous pathogens from harmless commensals and respond more robustly to the former to defend itself. 90,91 Accordingly, the host immune system could preferentially induce anti-Hsp90 IgG antibody concentrations above this threshold (associated with the IC risk) to provide an effective protective barrier against IC37,81,87 or below it (not related to the odds of IC) to limit C. albicans colonization (biofilm dispersion and adaptation to the varying stresses of the distinct host microenvironments) during the permanent host− pathogen interplay in the commensal state.17,67,84 Pending further confirmation in larger prospective patient populations, this threshold effect does not rule out a potential graded dose− response relationship at the higher range of anti-Hsp90 IgG antibody concentrations, where these antibodies are strongly associated with protection against IC.37,81

■

CONCLUSIONS Our proteomic biomarker pipeline has demonstrated that the combined molecular fingerprints of serum IgG antibodies to C. albicans Hsp90 and Eno1 are useful for diagnosing IC in nonneutropenic patients. Future studies should be aimed at refining and validating this 2-IgG antibody signature in prospective, larger, multicenter cohorts of non-neutropenic patients.

■

ASSOCIATED CONTENT

S Supporting Information *

Summary of peptide mass fingerprint data from the C. albicans rHsp90. This material is available free of charge via the Internet at http://pubs.acs.org.

Serum Concentrations of IgG Antibodies to Hsp90 and Eno1 May Provide Complementary Information for Diagnosing IC in Non-Neutropenic Patients

■

Our IC discriminator revealed that anti-Hsp90 IgG antibody concentrations added diagnostic value beyond that obtained from anti-Eno1 IgG antibody concentrations in non-neutropenic patients. This result suggests that IgG antibodies to Hsp90 and

AUTHOR INFORMATION

Corresponding Author

*Phone: +34-91-394-1755. Fax: +34-91-394-1745. E-mail: [email protected]. Q



Article

Notes

(10) Ellepola, A. N.; Morrison, C. J. Laboratory diagnosis of invasive candidiasis. J. Microbiol. 2005, 43 (Spec No), 65−84. (11) Ahmad, S.; Khan, Z. Invasive candidiasis: a review of nonculturebased laboratory diagnostic methods. Indian J. Med. Microbiol 2012, 30 (3), 264−269. (12) Schuetz, A. N. Invasive fungal infections: biomarkers and molecular approaches to diagnosis. Clin Lab Med. 2013, 33 (3), 505− 525. (13) Eggimann, P.; Bille, J.; Marchetti, O. Diagnosis of invasive candidiasis in the ICU. Ann. Intensive Care 2011, 1 (37), 10.1186/21105820137. (14) Pazos, C.; Moragues, M. D.; Quindos, G.; Ponton, J.; del Palacio, A. Diagnostic potential of (1,3)-beta-D-glucan and anti-Candida albicans germ tube antibodies for the diagnosis and therapeutic monitoring of invasive candidiasis in neutropenic adult patients. Rev. Iberoam. Micol. 2006, 23 (4), 209−215. (15) Pitarch, A.; Jimenez, A.; Nombela, C.; Gil, C. Decoding serological response to Candida cell wall immunome into novel diagnostic, prognostic, and therapeutic candidates for systemic candidiasis by proteomic and bioinformatic analyses. Mol. Cell. Proteomics 2006, 5 (1), 79−96. (16) Clancy, C. J.; Nguyen, M. L.; Cheng, S.; Huang, H.; Fan, G.; Jaber, R. A.; Wingard, J. R.; Cline, C.; Nguyen, M. H. Immunoglobulin G responses to a panel of Candida albicans antigens as accurate and early markers for the presence of systemic candidiasis. J. Clin. Microbiol. 2008, 46 (5), 1647−1654. (17) Mochon, A. B.; Jin, Y.; Kayala, M. A.; Wingard, J. R.; Clancy, C. J.; Nguyen, M. H.; Felgner, P.; Baldi, P.; Liu, H. Serological profiling of a Candida albicans protein microarray reveals permanent host-pathogen interplay and stage-specific responses during candidemia. PLoS Pathog. 2010, 6 (3), e1000827. (18) Quanping, S.; Yanyan, H.; Yicun, W.; Zhigang, J.; Yuling, G.; Li, W. The use of hybrid phage displaying antigen epitope and recombinant protein in the diagnosis of systemic Candida albicans infection in rabbits and cancer patients. Diagn. Microbiol. Infect. Dis. 2010, 68 (4), 382−389. (19) Li, F. Q.; Ma, C. F.; Shi, L. N.; Lu, J. F.; Wang, Y.; Huang, M.; Kong, Q. Q. Diagnostic value of immunoglobulin G antibodies against Candida enolase and fructose-bisphosphate aldolase for candidemia. BMC Infect. Dis. 2013, 13, 253. (20) Leon, C.; Ruiz-Santana, S.; Saavedra, P.; Galvan, B.; Blanco, A.; Castro, C.; Balasini, C.; Utande-Vazquez, A.; Gonzalez de Molina, F. J.; Blasco-Navalproto, M. A.; Lopez, M. J.; Charles, P. E.; Martin, E.; Hernandez-Viera, M. A. Usefulness of the ″Candida score″ for discriminating between Candida colonization and invasive candidiasis in non-neutropenic critically ill patients: a prospective multicenter study. Crit. Care Med. 2009, 37 (5), 1624−1633. (21) Zeindl-Eberhart, E.; Haraida, S.; Liebmann, S.; Jungblut, P. R.; Lamer, S.; Mayer, D.; Jager, G.; Chung, S.; Rabes, H. M. Detection and identification of tumor-associated protein variants in human hepatocellular carcinomas. Hepatology 2004, 39 (2), 540−549. (22) Lopez-Arias, E.; Aguilar-Lemarroy, A.; Felipe Jave-Suarez, L.; Morgan-Villela, G.; Mariscal-Ramirez, I.; Martinez-Velazquez, M.; Alvarez, A. H.; Gutierrez-Ortega, A.; Hernandez-Gutierrez, R. Alpha 1-antitrypsin: a novel tumor-associated antigen identified in patients with early-stage breast cancer. Electrophoresis 2012, 33 (14), 2130− 2137. (23) Katsumata, Y.; Kawaguchi, Y.; Baba, S.; Hattori, S.; Tahara, K.; Ito, K.; Iwasaki, T.; Yamaguchi, N.; Oyama, M.; Kozuka-Hata, H.; Hattori, H.; Nagata, K.; Yamanaka, H.; Hara, M. Identification of three new autoantibodies associated with systemic lupus erythematosus using two proteomic approaches. Mol. Cell Proteomics 2011, 10 (6), M110.005330. (24) Peng, B.; Huang, X.; Nakayasu, E. S.; Petersen, J. R.; Qiu, S.; Almeida, I. C.; Zhang, J. Y. Using immunoproteomics to identify alphaenolase as an autoantigen in liver fibrosis. J. Proteome Res. 2013, 12 (4), 1789−1796. (25) Luengo, O.; Molla, R.; Gamez, C.; Cardona, V.; Lopez, E.; Sastre, B.; Waisel, Y.; Belmonte, J.; Cadahia, A.; Lahoz, C.; del Pozo, V. Allergenicity and cross-reactivity of Senecio pollen: identification of

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS We are indebted to A. Jiménez (from Salamanca Clinic Hospital, Spain) for supplying human serum specimens and to the patients recruited in this study. We also thank M. Martínez-Gomariz and M. D. Gutiérrez (from the Proteomic Facility, Complutense University and Scientific Park Foundation of Madrid, a member of the ProteoRed-Health Institute Carlos III (ISCIII) Network, Spain) for their technical assistance in the rHsp90 identification. This work was supported by grants from the Community of Madrid (S2010/BMD-2414 PROMPT-CM); the Ministry of Economy and Competitiveness (BIO-2012-31767); the Marie Curie Initial Training Networks (FP7-PEOPLE-2013-ITN ImResFun); the National Plan of I+D+i 2008-2011 and ISCIII, Spanish Network for Research in Infectious Diseases (REIPI RD12/0015/0004) cofinanced by European Development Regional Fund “A way to achieve Europe” ERDF; Ramón Areces Foundation; and the MSD Special Chair in Genomics and Proteomics, Spain. C.N. is the director of this Chair.

■

ABBREVIATIONS AU, arbitrary units; CGD, Candida Genome Database; CI, confidence interval; Eno1, enolase; GST, glutathione-S-transferase; GO, gene ontology; FDR, false discovery rate; Hsp90, 90 kDa heat shock protein; IC, invasive candidiasis; IPG, immobilized pH gradient; NPV, negative predictive value; OR, odds ratio; PPI, protein−protein interaction; PPV, positive predictive value; ROC, receiver-operating-characteristic; RU, reference units

■

REFERENCES

(1) Romani, L.; Bistoni, F.; Puccetti, P. Initiation of T-helper cell immunity to Candida albicans by IL-12: the role of neutrophils. Chem. Immunol. 1997, 68, 110−135. (2) Eggimann, P.; Garbino, J.; Pittet, D. Epidemiology of Candida species infections in critically ill non-immunosuppressed patients. Lancet Infect. Dis. 2003, 3 (11), 685−702. (3) Prentice, H. G.; Kibbler, C. C.; Prentice, A. G. Towards a targeted, risk-based, antifungal strategy in neutropenic patients. Br. J. Haematol. 2000, 110 (2), 273−284. (4) Pechlivanoglou, P.; Le, H. H.; Daenen, S.; Snowden, J. A.; Postma, M. J. Mixed treatment comparison of prophylaxis against invasive fungal infections in neutropenic patients receiving therapy for haematological malignancies: a systematic review. J. Antimicrob. Chemother. 2014, 69 (1), 1−11. (5) Glockner, A.; Cornely, O. A. Practical considerations on current guidelines for the management of non-neutropenic adult patients with candidaemia. Mycoses 2013, 56 (1), 11−20. (6) Zaragoza, R.; Llinares, P.; Maseda, E.; Ferrer, R.; Rodríguez, A. EPICO project. Development of educational recommendations using the DELPHI technique on invasive candidiasis in non- neutropenic critically ill adult patients. Rev. Esp. Anestesiol. Reanim. 2013, 60 (7), e1− e18. (7) Deshpande, A.; Gaur, S.; Bal, A. M. Candidaemia in the nonneutropenic patient: a critique of the guidelines. Int. J. Antimicrob. Agents 2013, 42 (4), 294−300. (8) Pfaller, M. A.; Diekema, D. J. Epidemiology of invasive candidiasis: a persistent public health problem. Clin. Microbiol. Rev. 2007, 20 (1), 133−163. (9) Garey, K. W.; Rege, M.; Pai, M. P.; Mingo, D. E.; Suda, K. J.; Turpin, R. S.; Bearden, D. T. Time to initiation of fluconazole therapy impacts mortality in patients with candidemia: a multi-institutional study. Clin. Infect. Dis. 2006, 43 (1), 25−31. R



Article

novel allergens using the immunoproteomics approach. Clin. Exp. Allergy 2008, 38 (6), 1048−1060. (26) Pitarch, A.; Nombela, C.; Gil, C. Proteomics, a new challenge for clinical microbiology. Enferm. Infecc. Microbiol. Clin. 2010, 28 (8), 489− 491. (27) Dea-Ayuela, M. A.; Ubeira, F. M.; Pitarch, A.; Gil, C.; MartinezFernandez, A. R.; Bolas, F. A comparison of antigenic peptides in muscle larvae of several Trichinella species by two-dimensional western-blot analysis with monoclonal antibodies. Parasite 2001, 8 (2 Suppl), S117− S119. (28) Krah, A.; Schmidt, F.; Becher, D.; Schmid, M.; Albrecht, D.; Rack, A.; Buttner, K.; Jungblut, P. R. Analysis of automatically generated peptide mass fingerprints of cellular proteins and antigens from Helicobacter pylori 26695 separated by two-dimensional electrophoresis. Mol. Cell. Proteomics 2003, 2 (12), 1271−1283. (29) Wang, X.; Yu, J.; Sreekumar, A.; Varambally, S.; Shen, R.; Giacherio, D.; Mehra, R.; Montie, J. E.; Pienta, K. J.; Sanda, M. G.; Kantoff, P. W.; Rubin, M. A.; Wei, J. T.; Ghosh, D.; Chinnaiyan, A. M. Autoantibody signatures in prostate cancer. N. Engl. J. Med. 2005, 353 (12), 1224−1235. (30) Pitarch, A.; Nombela, C.; Gil, C. Proteomic profiling of serologic response to Candida albicans during host-commensal and hostpathogen interactions. Methods Mol. Biol. 2009, 470, 369−411. (31) Pitarch, A.; Pardo, M.; Jimenez, A.; Pla, J.; Gil, C.; Sanchez, M.; Nombela, C. Two-dimensional gel electrophoresis as analytical tool for identifying Candida albicans immunogenic proteins. Electrophoresis 1999, 20 (4−5), 1001−1010. (32) Pardo, M.; Ward, M.; Pitarch, A.; Sanchez, M.; Nombela, C.; Blackstock, W.; Gil, C. Cross-species identification of novel Candida albicans immunogenic proteins by combination of two-dimensional polyacrylamide gel electrophoresis and mass spectrometry. Electrophoresis 2000, 21 (13), 2651−2659. (33) Pitarch, A.; Sanchez, M.; Nombela, C.; Gil, C. Analysis of the Candida albicans proteome. I. Strategies and applications. J. Chromatogr., B 2003, 787 (1), 101−128. (34) Pitarch, A.; Nombela, C.; Gil, C. Contributions of proteomics to diagnosis, treatment, and prevention of candidiasis. Methods Biochem. Anal. 2006, 49, 331−361. (35) Pitarch, A.; Abian, J.; Carrascal, M.; Sanchez, M.; Nombela, C.; Gil, C. Proteomics-based identification of novel Candida albicans antigens for diagnosis of systemic candidiasis in patients with underlying hematological malignancies. Proteomics 2004, 4 (10), 3084−3106. (36) Pitarch, A.; Jimenez, A.; Nombela, C.; Gil, C. Serological proteome analysis to identify systemic candidiasis patients in the intensive care unit: Analytical, diagnostic and prognostic validation of anti-Candida enolase antibodies on quantitative clinical platforms. Proteomics Clin. Appl. 2008, 2 (4), 596−618. (37) Pitarch, A.; Nombela, C.; Gil, C. Prediction of the clinical outcome in invasive candidiasis patients based on molecular fingerprints of five anti-Candida antibodies in serum. Mol. Cell. Proteomics 2011, 10 (1), M1110.004010. (38) Pitarch, A.; Diez-Orejas, R.; Molero, G.; Pardo, M.; Sanchez, M.; Gil, C.; Nombela, C. Analysis of the serologic response to systemic Candida albicans infection in a murine model. Proteomics 2001, 1 (4), 550−559. (39) Rutjes, A. W.; Reitsma, J. B.; Vandenbroucke, J. P.; Glas, A. S.; Bossuyt, P. M. Case-control and two-gate designs in diagnostic accuracy studies. Clin. Chem. 2005, 51 (8), 1335−1341. (40) Mischak, H.; Apweiler, R.; Banks, R. E.; Conaway, M.; Coon, J.; Dominiczak, A.; Ehrich, J. H.; Fliser, D.; Girolami, M.; Hermjakob, H.; Hochstrasser, D.; Jankowski, J.; Julian, B. A.; Kolch, W.; Massy, Z. A.; Neusuess, C.; Novak, J.; Peter, K.; Rossing, K.; Schanstra, J.; Semmes, O. J.; Theodorescu, D.; Thongboonkerd, V.; Weissinger, E. M.; Van Eyk, J. E.; Yamamoto, T. Clinical proteomics: A need to define the field and to begin to set adequate standards. Proteomics Clin. Appl. 2007, 1 (2), 148− 156. (41) Bruns, D. E.; Huth, E. J.; Magid, E.; Young, D. S. Toward a checklist for reporting of studies of diagnostic accuracy of medical tests. Clin. Chem. 2000, 46 (7), 893−895.

(42) Pitarch, A.; Sanchez, M.; Nombela, C.; Gil, C. Analysis of the Candida albicans proteome. II. Protein information technology on the Net (update 2002). J. Chromatogr., B 2003, 787 (1), 129−148. (43) Pitarch, A.; Molero, G.; Moteoliva, L.; Thomas, D. P.; LópezRibot, J. L.; Nombela, C.; Gil, C. Proteomics in Candida Species. In Candida: Comparative and Functional Genomics; d’Enfert, C, Hube, B, Eds.; Caister Academic Press: Norfolk, U.K., 2007; pp 169−194. (44) Pitarch, A.; Nombela, C.; Gil, C. Identification of the Candida albicans immunome during systemic infection by mass spectrometry. Methods Mol. Biol. 2009, 470, 187−235. (45) Pitarch, A.; Nombela, C.; Gil, C. Reliability of antibodies to Candida methionine synthase for diagnosis, prognosis and risk stratification in systemic candidiasis: A generic strategy for the prototype development phase of proteomic markers. Proteomics Clin. Appl. 2007, 1 (10), 1221−1242. (46) Hanley, J. A.; McNeil, B. J. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983, 148 (3), 839−843. (47) Rifai, N.; Gillette, M. A.; Carr, S. A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 2006, 24 (8), 971−983. (48) Pitarch, A.; Nombela, C.; Gil, C. The Candida Immunome As a Mine for Clinical Biomarker Development for Invasive Candidiasis: From Biomarker Discovery to Assay Validation. In Pathogenic Fungi: Insights in Molecular Biology; San-Blas, G, Calderone, R, Eds.; Caister Academic Press: Norfolk, U.K., 2008; pp 103−142. (49) Vigil, A.; Chen, C.; Jain, A.; Nakajima-Sasaki, R.; Jasinskas, A.; Pablo, J.; Hendrix, L. R.; Samuel, J. E.; Felgner, P. L. Profiling the humoral immune response of acute and chronic Q fever by protein microarray. Mol. Cell. Proteomics 2011, 10 (10), M110.006304. (50) Tiberti, N.; Hainard, A.; Lejon, V.; Robin, X.; Ngoyi, D. M.; Turck, N.; Matovu, E.; Enyaru, J.; Ndung’u, J. M.; Scherl, A.; Dayon, L.; Sanchez, J.-C. Discovery and verification of osteopontin and beta-2microglobulin as promising markers for staging human African trypanosomiasis. Mol. Cell. Proteomics 2010, 9 (12), 2783−2795. (51) Lin, Y.-F.; Chen, C.-Y.; Tsai, M.-H.; Wu, M.-S.; Wang, Y.-C.; Chuang, E. Y.; Lin, J.-T.; Yang, P.-C.; Chow, L.-P. Duodenal ulcerrelated antigens from Helicobacter pylori. Mol. Cell. Proteomics 2007, 6 (6), 1018−1026. (52) Lain, A.; Moragues, M. D.; Ruiz, J. C.; Mendoza, J.; Camacho, A.; Del Palacio, A.; Ponton, J. Evaluation of a novel enzyme-linked immunosorbent assay to detect immunoglobulin G antibody to enolase for serodiagnosis of invasive candidiasis. Clin. Vaccine Immunol. 2007, 14 (3), 318−319. (53) Lyashchenko, K.; Colangeli, R.; Houde, M.; Al Jahdali, H.; Menzies, D.; Gennaro, M. L. Heterogeneous antibody responses in tuberculosis. Infect. Immun. 1998, 66 (8), 3936−3940. (54) Liang, L.; Tan, X.; Juarez, S.; Villaverde, H.; Pablo, J.; NakajimaSasaki, R.; Gotuzzo, E.; Saito, M.; Hermanson, G.; Molina, D.; Felgner, S.; Morrow, W. J.; Liang, X.; Gilman, R. H.; Davies, D. H.; Tsolis, R. M.; Vinetz, J. M.; Felgner, P. L. Systems biology approach predicts antibody signature associated with Brucella melitensis infection in humans. J. Proteome Res. 2011, 10 (10), 4813−4824. (55) Krah, A.; Miehlke, S.; Pleissner, K.-P.; Zimny-Arndt, U.; Kirsch, C.; Lehn, N.; Meyer, T. F.; Jungblut, P. R.; Aebischer, T. Identification of candidate antigens for serologic detection of Helicobacter pylori-infected patients with gastric carcinoma. Int. J. Cancer 2004, 108 (3), 456−463. (56) Nombela, C.; Gil, C.; Chaffin, W. L. Non-conventional protein secretion in yeast. Trends Microbiol. 2006, 14 (1), 15−21. (57) Sorgo, A. G.; Heilmann, C. J.; Dekker, H. L.; Bekker, M.; Brul, S.; de Koster, C. G.; de Koning, L. J.; Klis, F. M. Effects of fluconazole on the secretome, the wall proteome, and wall integrity of the clinical fungus Candida albicans. Eukaryotic Cell 2011, 10 (8), 1071−1081. (58) Pitarch, A.; Sanchez, M.; Nombela, C.; Gil, C. Sequential fractionation and two-dimensional gel analysis unravels the complexity of the dimorphic fungus Candida albicans cell wall proteome. Mol. Cell. Proteomics 2002, 1 (12), 967−982. (59) Chaffin, W. L. Candida albicans cell wall proteins. Microbiol. Mol. Biol. Rev. 2008, 72 (3), 495−544. S



Article

(60) Pitarch, A.; Nombela, C.; Gil, C. Cell wall fractionation for yeast and fungal proteomics. Methods Mol. Biol. 2008, 425, 217−239. (61) Kusch, H.; Engelmann, S.; Bode, R.; Albrecht, D.; Morschhauser, J.; Hecker, M. A proteomic view of Candida albicans yeast cell metabolism in exponential and stationary growth phases. Int. J. Med. Microbiol. 2008, 298 (3−4), 291−318. (62) Fradin, C.; De Groot, P.; MacCallum, D.; Schaller, M.; Klis, F.; Odds, F. C.; Hube, B. Granulocytes govern the transcriptional response, morphology and proliferation of Candida albicans in human blood. Mol. Microbiol. 2005, 56 (2), 397−415. (63) Fradin, C.; Kretschmar, M.; Nichterlein, T.; Gaillardin, C.; d’Enfert, C.; Hube, B. Stage-specific gene expression of Candida albicans in human blood. Mol. Microbiol. 2003, 47 (6), 1523−1543. (64) Wilson, D.; Thewes, S.; Zakikhany, K.; Fradin, C.; Albrecht, A.; Almeida, R.; Brunke, S.; Grosse, K.; Martin, R.; Mayer, F.; Leonhardt, I.; Schild, L.; Seider, K.; Skibbe, M.; Slesiona, S.; Waechtler, B.; Jacobsen, I.; Hube, B. Identifying infection-associated genes of Candida albicans in the postgenomic era. FEMS Yeast Res. 2009, 9 (5), 688−700. (65) Monteoliva, L.; Martinez-Lopez, R.; Pitarch, A.; Hernaez, M. L.; Serna, A.; Nombela, C.; Albar, J. P.; Gil, C. Quantitative proteome and acidic subproteome profiling of Candida albicans yeast-to-hypha transition. J. Proteome. Res. 2011, 10 (2), 502−517. (66) Silva, R. C.; Padovan, A. C.; Pimenta, D. C.; Ferreira, R. C.; da Silva, C. V.; Briones, M. R. Extracellular enolase of Candida albicans is involved in colonization of mammalian intestinal epithelium. Front. Cell. Infect. Microbiol. 2014, 4, 66. (67) O’Meara, T. R.; Cowen, L. E. Hsp90-dependent regulatory circuitry controlling temperature-dependent fungal development and virulence. Cell Microbiol. 2014, 16 (4), 473−481. (68) Jong, A. Y.; Chen, S. H.; Stins, M. F.; Kim, K. S.; Tuan, T. L.; Huang, S. H. Binding of Candida albicans enolase to plasmin(ogen) results in enhanced invasion of human brain microvascular endothelial cells. J. Med. Microbiol. 2003, 52 (Pt 8), 615−622. (69) Pitarch, A.; Nombela, C.; Gil, C. Candida albicans biology and pathogenicity: insights from proteomics. Methods Biochem. Anal. 2006, 49, 285−330. (70) Mayer, F. L.; Wilson, D.; Hube, B. Candida albicans pathogenicity mechanisms. Virulence 2013, 4 (2), 119−128. (71) Shibasaki, S.; Aoki, W.; Nomura, T.; Karasaki, M.; Sewaki, T.; Ueda, M. Evaluation of Mdh1 protein as an antigenic candidate for a vaccine against candidiasis. Biocontrol Sci. 2014, 19 (1), 51−55. (72) Peterson, P.; Perheentupa, J.; Krohn, K. J. Detection of candidal antigens in autoimmune polyglandular syndrome type I. Clin. Diagn. Lab. Immunol. 1996, 3 (3), 290−294. (73) Chou, H.; Tam, M. F.; Chang, C. Y.; Lai, H. Y.; Huang, M. H.; Chou, C. T.; Lee, S. S.; Shen, H. D. Characterization of a novel Candida albicans 29 kDa IgE-binding protein - purification, cDNA isolation and heterologous expression of Cand a 3. Allergy 2003, 58 (11), 1157−1164. (74) Xin, H.; Dziadek, S.; Bundle, D. R.; Cutler, J. E. Synthetic glycopeptide vaccines combining beta-mannan and peptide epitopes induce protection against candidiasis. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (36), 13526−13531. (75) Xin, H.; Cutler, J. E. Vaccine and monoclonal antibody that enhance mouse resistance to candidiasis. Clin. Vaccine Immunol. 2011, 18 (10), 1656−1667. (76) Li, W.; Hu, X.; Zhang, X.; Ge, Y.; Zhao, S.; Hu, Y.; Ashman, R. B. Immunisation with the glycolytic enzyme enolase confers effective protection against Candida albicans infection in mice. Vaccine 2011, 29 (33), 5526−5533. (77) Raska, M.; Belakova, J.; Wudattu, N. K.; Kafkova, L.; Ruzickova, K.; Sebestova, M.; Kolar, Z.; Weigl, E. Comparison of protective effect of protein and DNA vaccines Hsp90 in murine model of systemic candidiasis. Folia Microbiol. 2005, 50 (1), 77−82. (78) Yang, Y.; Thannhauser, T. W.; Li, L.; Zhang, S. Development of an integrated approach for evaluation of 2-D gel image analysis: Impact of multiple proteins in single spots on comparative proteomics in conventional 2-D gel/MALDI workflow. Electrophoresis 2007, 28 (12), 2080−2094.

(79) Thomas, D. P.; Viudes, A.; Monteagudo, C.; Lazzell, A. L.; Saville, S. P.; Lopez-Ribot, J. L. A proteomic-based approach for the identification of Candida albicans protein components present in a subunit vaccine that protects against disseminated candidiasis. Proteomics 2006, 6 (22), 6033−6041. (80) Martinez-Lopez, R.; Nombela, C.; Diez-Orejas, R.; Monteoliva, L.; Gil, C. Immunoproteomic analysis of the protective response obtained from vaccination with Candida albicans ecm33 cell wall mutant in mice. Proteomics 2008, 8 (13), 2651−2664. (81) Matthews, R. C.; Burnie, J. P.; Tabaqchali, S. Isolation of immunodominant antigens from sera of patients with systemic candidiasis and characterization of serological response to Candida albicans. J. Clin. Microbiol. 1987, 25 (2), 230−237. (82) Matthews, R.; Burnie, J. Diagnosis of systemic candidiasis by an enzyme-linked dot immunobinding assay for a circulating immunodominant 47-kilodalton antigen. J. Clin. Microbiol. 1988, 26 (3), 459− 463. (83) Shapiro, R. S.; Uppuluri, P.; Zaas, A. K.; Collins, C.; Senn, H.; Perfect, J. R.; Heitman, J.; Cowen, L. E. Hsp90 orchestrates temperature-dependent Candida albicans morphogenesis via Ras1PKA signaling. Curr. Biol. 2009, 19 (8), 621−629. (84) Robbins, N.; Uppuluri, P.; Nett, J.; Rajendran, R.; Ramage, G.; Lopez-Ribot, J. L.; Andes, D.; Cowen, L. E. Hsp90 governs dispersion and drug resistance of fungal biofilms. PLoS Pathog. 2011, 7 (9), e1002257. (85) Zugel, U.; Kaufmann, S. H. Role of heat shock proteins in protection from and pathogenesis of infectious diseases. Clin. Microbiol. Rev. 1999, 12 (1), 19−39. (86) Pachl, J.; Svoboda, P.; Jacobs, F.; Vandewoude, K.; van der Hoven, B.; Spronk, P.; Masterson, G.; Malbrain, M.; Aoun, M.; Garbino, J.; Takala, J.; Drgona, L.; Burnie, J.; Matthews, R. A randomized, blinded, multicenter trial of lipid-associated amphotericin B alone versus in combination with an antibody-based inhibitor of heat shock protein 90 in patients with invasive candidiasis. Clin. Infect. Dis. 2006, 42 (10), 1404−1413. (87) Karwa, R.; Wargo, K. A. Efungumab: a novel agent in the treatment of invasive candidiasis. Ann. Pharmacother. 2009, 43 (11), 1818−1823. (88) Burges, G.; Holley, H. P.; Virella, G. Circulating immune complexes in patients with Candida albicans infections. Clin. Exp. Immunol. 1983, 53 (1), 165−174. (89) Raska, M.; Belakova, J.; Krupka, M.; Weigl, E. Candidiasis - do we need to fight or to tolerate the Candida fungus? Folia Microbiol. 2007, 52 (3), 297−312. (90) Jacobsen, I. D.; Wilson, D.; Wachtler, B.; Brunke, S.; Naglik, J. R.; Hube, B. Candida albicans dimorphism as a therapeutic target. Expert Rev. Anti-Infect. Ther. 2012, 10 (1), 85−93. (91) Hayman, L.; Steffen, M. J.; Stevens, J.; Badger, E.; Tempro, P.; Fuller, B.; McGuire, A.; Al-Sabbagh, M.; Thomas, M. V.; Ebersole, J. L. Smoking and periodontal disease: discrimination of antibody responses to pathogenic and commensal oral bacteria. Clin. Exp. Immunol. 2011, 164 (1), 118−126. (92) Walsh, T. J.; Hathorn, J. W.; Sobel, J. D.; Merz, W. G.; Sanchez, V.; Maret, S. M.; Buckley, H. R.; Pfaller, M. A.; Schaufele, R.; Sliva, C.; Navarro, E.; Lecciones, J.; Chandrasekar, P.; Lee, J.; Pizzo, P. A. Detection of circulating Candida enolase by immunoassay in patients with cancer and invasive candidiasis. N Engl J. Med. 1991, 324 (15), 1026−1031. (93) Dai, B.; Wang, Y.; Li, D.; Xu, Y.; Liang, R.; Zhao, L.; Cao, Y.; Jia, J.; Jiang, Y. Hsp90 is involved in apoptosis of Candida albicans by regulating the calcineurin-caspase apoptotic pathway. PLoS One 2012, 7 (9), e45109. (94) Singh, S. D.; Robbins, N.; Zaas, A. K.; Schell, W. A.; Perfect, J. R.; Cowen, L. E. Hsp90 governs echinocandin resistance in the pathogenic yeast Candida albicans via calcineurin. PLoS Pathog. 2009, 5 (7), e1000532.

T


Candida albicans - ACS Publications - American Chemical Society

Recommend Documents