Identification of novel diagnostic serum biomarkers for Chagas' disease in asymptomatic subjects by mass spectrometric profiling

Abstract

More than 10 million people are thought to be infected with Trypanosoma cruzi, primarily in the Americas. The clinical manifestations of Chagas' disease (CD) are variable, but most subjects remain asymptomatic for decades. Only 15 to 30% eventually develop terminal complications. All current diagnostic tests have limitations. New approaches are needed for blood bank screening as well as for improved diagnosis and prognosis. Sera from subjects with asymptomatic CD (n = 131) were compared to those from uninfected controls (n = 164) and subjects with other parasitic diseases (n = 140), using protein array mass spectrometry. To identify biomarkers associated with CD, sera were fractionated by anion-exchange chromatography and bound to two commercial ProteinChip array chemistries: WCX2 and IMAC3. Multiple candidate biomarkers were found in CD sera (3 to 75.4 kDa). Algorithms employing 3 to 5 of these biomarkers achieved up to 100% sensitivity and 98% specificity for CD. The biomarkers most useful for diagnosis were identified and validated. These included MIP1 alpha, C3a anaphylatoxin, and unusually truncated forms of fibronectin, apolipoprotein A1 (ApoA1), and C3. An antipeptide antiserum against the 28.9-kDa C terminus of the fibronectin fragment achieved good specificity (90%) for CD in a Western blot format. We identified full-length ApoA1 (28.1 kDa), the major structural and functional protein component of high-density lipoprotein (HDL), as an important negative biomarker for CD, and relatively little full-length ApoA1 was detected in CD sera. This work provides proof of principle that both platform-dependent (i.e., mass spectrometry-based) and platform-independent (i.e., Western blot) tests can be generated using high-throughput mass profiling.

FIG. 1.

Biomarker pattern software based on CART analysis was used to generate candidate diagnostic algorithms. The CART procedure seeks to minimize a cost function that balances prediction errors in either sense (e.g., false-positive or false-negative results) and the total number of biomarkers used. Equal weight is given to false-positive and false-negative results. The relative importance of each peak in any given algorithm is measured by the order in which it is selected in the decision tree and the number of correct predictions credited to it. An example of decision tree classification in the training data set, using CD and control (C) (VHC, CHC, and OPD) samples, is shown. The numbers in the root node (top box), the descendant nodes (lower boxes), and the terminal nodes (ovals) represent the biomarker peak mass (M [kDa]), the peak intensity criterion (I), and the class (CD and C), respectively. In this algorithm, the intensities of the 8.1-, 9.3-, 13.6-, 24.7-, and 28.9-kDa biomarkers establish the splitting rules. Cases that follow the rule are placed in the left daughter node (yes), and samples that do not follow the rule go to the right (no) daughter. For example, the first splitting-rule question asks the following: “Does the peak at 13.6 kDa have an intensity of ≤2.05?”

FIG. 2.

SELDI-TOF MS identification of the 9.3-kDa candidate biomarker. (A) Representative spectral view of IMAC spectra of the Chagas' disease sera and the parasite controls (Toxoplasma, African sleeping sickness, Babesia, Leishmania, and malaria). The 9.3-kDa protein, detected in Chagas' disease but not other diseases, is shown. (B) The 9.3-kDa protein was purified from serum samples by anion-exchange chromatography followed by reverse-phase chromatography and, finally, 16% Tricine SDS-PAGE. Coomassie blue-stained bands were extracted and reprofiled on NP20 arrays. A band corresponding to the 9.3-kDa protein was digested in solution with trypsin, and the digest was analyzed by tandem mass spectrometry. Eight peptides were identified as components of a C-terminal fragment of ApoA1. (C) Amino acid sequence of human ApoA1. The amino acid sequence encompassed by the peptides identified by tandem mass spectrometry is highlighted in bold. The calculated molecular mass for the highlighted sequence is 9,306.59 Da. (D) Immunocapture of C-terminal fragments from serum samples, using rabbit polyclonal antibody against ApoA1.

FIG. 2.

SELDI-TOF MS identification of the 9.3-kDa candidate biomarker. (A) Representative spectral view of IMAC spectra of the Chagas' disease sera and the parasite controls (Toxoplasma, African sleeping sickness, Babesia, Leishmania, and malaria). The 9.3-kDa protein, detected in Chagas' disease but not other diseases, is shown. (B) The 9.3-kDa protein was purified from serum samples by anion-exchange chromatography followed by reverse-phase chromatography and, finally, 16% Tricine SDS-PAGE. Coomassie blue-stained bands were extracted and reprofiled on NP20 arrays. A band corresponding to the 9.3-kDa protein was digested in solution with trypsin, and the digest was analyzed by tandem mass spectrometry. Eight peptides were identified as components of a C-terminal fragment of ApoA1. (C) Amino acid sequence of human ApoA1. The amino acid sequence encompassed by the peptides identified by tandem mass spectrometry is highlighted in bold. The calculated molecular mass for the highlighted sequence is 9,306.59 Da. (D) Immunocapture of C-terminal fragments from serum samples, using rabbit polyclonal antibody against ApoA1.

FIG. 3.

SELDI-TOF MS identification of 28.9-kDa candidate biomarker. (A) The 28.9-kDa protein was enriched from serum samples by anion-exchange chromatography (flowthrough) and protein A chromatography (flowthrough), concentrated/desalted using YM30 filtration, and finally purified by 12% Bis-Tris SDS-PAGE. Coomassie blue-stained bands were extracted and reprofiled on NP20 arrays. A band corresponding to the 28.9-kDa protein was digested in solution with trypsin, and the digest was analyzed by tandem mass spectrometry. Seven peptides were identified as components of the N-terminal fragment of fibronectin. (B) Sequence of the first 300 amino acids of human fibronectin. The full-length sequence of fibronectin, consisting of 2,355 amino acids, is not shown. The fragments identified by MS/MS are highlighted in bold. Importantly, one fragment with m/z 1,707.78 Da had a nontryptic cut at the C terminus, indicating that this was the C-terminal end of the 28.9-kDa protein. The position of the peptide used to generate the rabbit antiserum is underlined. The calculated molecular mass of the sequence from the N terminus to Val258, with the N-terminal Gln modified to pyrrolidone carboxylic acid and with nine disulfide bonds, is 28,931 Da. The putative C-terminal Val258 is bold and underlined.

FIG. 4.

Western blot analysis of sera from patients with CD or other parasitic diseases, probed with anti-CttFbn peptide sera. (A) Analysis of 28 CD-negative control sera shows that the C-terminally truncated fragment of Fbn (28.9 kDa) was detected in only 3/28 sera (11%). (B) Analysis of 28 CD-positive sera shows that the 28.9-kDa band was detected in 22/28 sera (79%). Fbn, control recombinant Fbn fragment; CD+, pool of 8 positive Chagasic sera; CD−, pooled Canadian negative sera. Lanes in which the 28.9-kDa band was present are marked by asterisks. (C) Analysis of pools of sera from patients with fasciolosis (F), schistosomiasis (S), malaria (M), leishmaniasis (L), African trypanosomiasis (A), or Chagas' disease (CD+) or from Canadian negative controls (CD−). The antipeptide serum detected a band near 28.9 kDa only in the CD⁺ serum pool.