Towards High-throughput Immunomics for Infectious Diseases: Use of Next-generation Peptide Microarrays for Rapid Discovery and Mapping of Antigenic Determinants
2015; Elsevier BV; Volume: 14; Issue: 7 Linguagem: Inglês
10.1074/mcp.m114.045906
ISSN1535-9484
AutoresSantiago J. Carmona, Morten Nielsen, Claus Schafer‐Nielsen, Juan Mucci, Jaime Altcheh, Virginia Balouz, Valeria Tekiel, Alberto C.C. Frasch, Oscar Campetella, Carlos A. Buscaglia, Fernán Agiero,
Tópico(s)Monoclonal and Polyclonal Antibodies Research
ResumoComplete characterization of antibody specificities associated to natural infections is expected to provide a rich source of serologic biomarkers with potential applications in molecular diagnosis, follow-up of chemotherapeutic treatments, and prioritization of targets for vaccine development. Here, we developed a highly-multiplexed platform based on next-generation high-density peptide microarrays to map these specificities in Chagas Disease, an exemplar of a human infectious disease caused by the protozoan Trypanosoma cruzi. We designed a high-density peptide microarray containing more than 175,000 overlapping 15mer peptides derived from T. cruzi proteins. Peptides were synthesized in situ on microarray slides, spanning the complete length of 457 parasite proteins with fully overlapped 15mers (1 residue shift). Screening of these slides with antibodies purified from infected patients and healthy donors demonstrated both a high technical reproducibility as well as epitope mapping consistency when compared with earlier low-throughput technologies. Using a conservative signal threshold to classify positive (reactive) peptides we identified 2,031 disease-specific peptides and 97 novel parasite antigens, effectively doubling the number of known antigens and providing a 10-fold increase in the number of fine mapped antigenic determinants for this disease. Finally, further analysis of the chip data showed that optimizing the amount of sequence overlap of displayed peptides can increase the protein space covered in a single chip by at least ∼threefold without sacrificing sensitivity. In conclusion, we show the power of high-density peptide chips for the discovery of pathogen-specific linear B-cell epitopes from clinical samples, thus setting the stage for high-throughput biomarker discovery screenings and proteome-wide studies of immune responses against pathogens. Complete characterization of antibody specificities associated to natural infections is expected to provide a rich source of serologic biomarkers with potential applications in molecular diagnosis, follow-up of chemotherapeutic treatments, and prioritization of targets for vaccine development. Here, we developed a highly-multiplexed platform based on next-generation high-density peptide microarrays to map these specificities in Chagas Disease, an exemplar of a human infectious disease caused by the protozoan Trypanosoma cruzi. We designed a high-density peptide microarray containing more than 175,000 overlapping 15mer peptides derived from T. cruzi proteins. Peptides were synthesized in situ on microarray slides, spanning the complete length of 457 parasite proteins with fully overlapped 15mers (1 residue shift). Screening of these slides with antibodies purified from infected patients and healthy donors demonstrated both a high technical reproducibility as well as epitope mapping consistency when compared with earlier low-throughput technologies. Using a conservative signal threshold to classify positive (reactive) peptides we identified 2,031 disease-specific peptides and 97 novel parasite antigens, effectively doubling the number of known antigens and providing a 10-fold increase in the number of fine mapped antigenic determinants for this disease. Finally, further analysis of the chip data showed that optimizing the amount of sequence overlap of displayed peptides can increase the protein space covered in a single chip by at least ∼threefold without sacrificing sensitivity. In conclusion, we show the power of high-density peptide chips for the discovery of pathogen-specific linear B-cell epitopes from clinical samples, thus setting the stage for high-throughput biomarker discovery screenings and proteome-wide studies of immune responses against pathogens. Detailed knowledge of antigens and epitopes recognized in the context of naturally acquired human infections has important implications for our understanding of immune system responses against pathogens, and of the immunopathogenesis of infectious diseases. This knowledge is also important for practical clinical applications such as the development of improved vaccines, intervention strategies, and diagnostics. In the last decades, significant progress has been made in the discovery of antigens and epitopes thanks to a number of methodologies such as cDNA expression libraries (1.Beghetto E. Gargano N. 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Ponomarenko J. Zhu Z. Tamang D. Wang P. Greenbaum J. Lundegaard C. Sette A. Lund O. Bourne P.E. Nielsen M. Peters B. Immune epitope database analysis resource.Nucleic Acids Res. 2012; 40: W525-W530Crossref PubMed Scopus (323) Google Scholar) currently contains an average of only 10 antigens with mapped B-cell epitopes recognized from naturally acquired human infections for bacterial or eukaryotic pathogens. The reasons for this are many, but can be largely attributed to different limitations in the mentioned screening technologies. Heterologous expression of cDNA libraries has been used to guide antigen discovery, but mapping of epitopes most often lags behind as it is a much more costly exercise. Similarly, combinatorial peptide libraries greatly facilitate the identification of peptides that are specifically recognized by antibodies, but these peptides have sequences that can greatly differ from those of the native epitopes (they are mimotopes), thus making it difficult to identify the original antigens. As a result, we currently have only limited detailed information on the fine specificities of the antibody response against complex pathogens. The number of tools for studying immune responses has recently expanded with the inclusion of peptide and protein microarrays, which have been used to identify pathogen-specific antigens and linear epitopes (6.Sundaresh S. Randall A. Unal B. Petersen J.M. Belisle J.T. Hartley M.G. Duffield M. Titball R.W. Davies D.H. Felgner P.L. Baldi P. From protein microarrays to diagnostic antigen discovery: a study of the pathogen Francisella tularensis.Bioinformatics. 2007; 23: i508-i518Crossref PubMed Scopus (76) Google Scholar, 7.Gaseitsiwe S. 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In contrast, peptide arrays can provide exquisite detail of epitope localization, but until now had other limitations mostly associated with their reduced capacity, preventing the complete scanning of large numbers of candidate proteins. Recent advances in computerized photolithography and photochemistry have led to the development of a novel high-density peptide microarray technology, where individual peptides can be synthesized in situ on a glass slide at high densities (14.Fodor S.P. Read J.L. Pirrung M.C. Stryer L. Lu A.T. Solas D. Light-directed, spatially addressable parallel chemical synthesis.Science. 1991; 251: 767-773Crossref PubMed Scopus (2437) Google Scholar, 15.Pellois J.P. Zhou X. Srivannavit O. Zhou T. Gulari E. Gao X. Individually addressable parallel peptide synthesis on microchips.Nat. Biotechnol. 2002; 20: 922-926Crossref PubMed Scopus (159) Google Scholar, 16.Buus S. Rockberg J. Forsström B. Nilsson P. Uhlen M. Schafer-Nielsen C. 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Forsström B. Nilsson P. Uhlen M. Schafer-Nielsen C. High-resolution mapping of linear antibody epitopes using ultrahigh-density peptide microarrays.Mol. Cell. Proteomics. 2012; 11: 1790-1800Abstract Full Text Full Text PDF PubMed Scopus (123) Google Scholar, 17.Forsström B. Axnäs B.B. Stengele K.-P. Bihler J. Albert T.J. Richmond T.A. Hu F.J. Nilsson P. Hudson E.P. Rockberg J. Uhlen M. Proteome-wide epitope mapping of antibodies using ultra-dense peptide arrays.Mol. Cell. Proteomics. 2014; 13: 1585-1597Abstract Full Text Full Text PDF PubMed Scopus (90) Google Scholar, 18.Hansen L.B. Buus S. Schafer-Nielsen C. Identification and mapping of linear antibody epitopes in human serum albumin using high-density peptide arrays.PLoS One. 2013; 8: e68902Crossref PubMed Scopus (35) Google Scholar). Using these high-density peptide arrays, we here describe the first large-scale study of fine antibody specificities associated with Chagas Disease, which is an exemplar of a chronic human infectious disease. Chagas Disease, caused by the protozoan Trypanosoma cruzi, is an endemic disease of the Americas, affecting ∼8 million people (19.Rassi Jr., A. Rassi A. Marin-Neto J.A. Chagas disease.Lancet. 2010; 375: 1388-1402Abstract Full Text Full Text PDF PubMed Scopus (1665) Google Scholar). The parasite invades and replicates within host cells, and briefly enters the bloodstream to reach other target tissues. Initially, the disease goes through an acute stage, characterized by patent parasitaemia and the appearance of antibodies against acute-phase antigens, such as SAPA (20.Affranchino J.L. Ibañez C.F. Luquetti A.O. Rassi A. Reyes M.B. Macina R.A. Aslund L. Pettersson U. Frasch A.C. Identification of a Trypanosoma cruzi antigen that is shed during the acute phase of Chagas' disease.Mol. 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Almeida I.C. Buscaglia C.A. Evaluation of a recombinant Trypanosoma cruzi mucin-like antigen for serodiagnosis of Chagas' disease.Clin. Vaccine Immunol. 2011; 18: 1850-1855Crossref PubMed Scopus (38) Google Scholar) remains the standard for diagnosis of Chagas Disease. In this work, we screened high-density microarray slides containing peptides derived from T. cruzi proteins with mixtures of immunoglobulins purified directly from blood samples of Chagas Disease patients. This led to the identification of novel antigens and the simultaneous mapping of their linear B-cell epitopes, thus demonstrating the capacity and performance of this platform for studying antibody specificities associated with human infectious diseases. A total of 457 T. cruzi protein sequences annotated in the CL-Brener genome (version Jan. 2012) (23.Aslett M. Aurrecoechea C. Berriman M. Brestelli J. Brunk B.P. Carrington M. Depledge D.P. Fischer S. Gajria B. Gao X. Gardner M.J. Gingle A. Grant G. Harb O.S. Heiges M. Hertz-Fowler C. Houston R. Innamorato F. Iodice J. Kissinger J.C. Kraemer E. Li W. Logan F.J. Miller J.A. Mitra S. Myler P.J. Nayak V. Pennington C. Phan I. Pinney D.F. Ramasamy G. Rogers M.B. Roos D.S. Ross C. Sivam D. Smith D.F. Srinivasamoorthy G. Stoeckert Jr., C.J. Subramanian S. Thibodeau R. Tivey A. Treatman C. Velarde G. Wang H. TriTrypDB: a functional genomic resource for the Trypanosomatidae.Nucleic Acids Res. 2010; 38: D457-D462Crossref PubMed Scopus (645) Google Scholar) were selected for inclusion in the microarray (according to the groups defined in Table I). Proteins in Group 1 were randomly sampled from the proteome. If a sampled protein was a putative ortholog of a previously picked protein (as defined by the OrthoMCL algorithm (24.Chen F. Mackey A.J. Vermunt J.K. Roos D.S. Assessing performance of orthology detection strategies applied to eukaryotic genomes.PLoS One. 2007; 2: e383Crossref PubMed Scopus (291) Google Scholar)), that protein was skipped. Hence, the final protein set for this group contained no homologous proteins. Group 2 contained T. cruzi proteins without previous serology evidence, selected based on shared features with known antigens such as evidence of expression, subcellular localization, presence of tandem repeats, disordered regions and B-cell epitope predictions, using an integrative method developed in our group (25.Carmona S.J. Sartor P.A. Leguizamón M.S. Campetella O.E. Agiero F. Diagnostic peptide discovery: prioritization of pathogen diagnostic markers using multiple features.PLoS One. 2012; 7: e50748Crossref PubMed Scopus (27) Google Scholar). Homologous proteins were removed from this group, as described for Group 1. Group 3 is composed of selected members of the MASP (mucin-associated surface proteins) family. This is a multigene family composed of ∼1300 genes, with a high level of polymorphisms (26.Ackermann A.A. Carmona S.J. Agiero F. TcSNP: a database of genetic variation in Trypanosoma cruzi.Nucleic Acids Res. 2009; 37: D544-D549Crossref PubMed Scopus (16) Google Scholar, 27.Ackermann A.A. Panunzi L.G. Cosentino R.O. Sánchez D.O. Agiero F. A genomic scale map of genetic diversity in Trypanosoma cruzi.BMC Genomics. 2012; 13: 736Crossref PubMed Scopus (15) Google Scholar), localized at the surface of infective forms of the parasite. This protein family has earlier been proposed to participate in host-parasite interactions (28.Bartholomeu D.C. Cerqueira G.C. Leão A.C.A. DaRocha W.D. Pais F.S. Macedo C. Djikeng A. Teixeira S.M.R. El-Sayed N.M. Genomic organization and expression profile of the mucin-associated surface protein (masp) family of the human pathogen Trypanosoma cruzi.Nucleic Acids Res. 2009; 37: 3407-3417Crossref PubMed Scopus (90) Google Scholar). The six MASP subgroups (29.Dos Santos S. Freitas L.M. Lobo F.P. Rodrigues-Luiz G.F. de Oliveira Mendes T.A. Oliveira A.C.S. Andrade L.O. Chiari E. Gazzinelli R.T. Teixeira S.M.R. Fujiwara R.T. Bartholomeu D.C. The MASP family of Trypanosoma cruzi: changes in gene expression and antigenic profile during the acute phase of experimental infection.PLoS Negl. Trop. Dis. 2012; 6: e1779Crossref PubMed Scopus (45) Google Scholar) were represented in the group. Group 4 is composed of 68 proteins with previous evidence of seroreactivity in T. cruzi infected humans (these were obtained from the IEDB (5.Kim Y. Ponomarenko J. Zhu Z. Tamang D. Wang P. Greenbaum J. Lundegaard C. Sette A. Lund O. Bourne P.E. Nielsen M. Peters B. Immune epitope database analysis resource.Nucleic Acids Res. 2012; 40: W525-W530Crossref PubMed Scopus (323) Google Scholar) or manually curated from the literature), more information in supplemental Table S3. Protein Group 5 is composed by 54 neo-proteins of random protein sequences. These random sequences have the same mono- and di-peptide distribution as found in the T. cruzi proteome. All unique 15-mer derived from protein sequences in these five groups were included in the array in a single copy, except for a random subset of peptides, which were replicated for technical variability assessment (6549 peptides in two or more copies, 992 in five or more copies). Peptide fields were distributed in 12 microarray sectors of ∼18,700 peptides each. Peptides from the same protein were synthesized at randomized positions within a single sector. The chip contains additional 2386 unique peptides corresponding to eight proteins not accounted within the protein groups defined above (and of which 2 proteins resulted seropositive), including members of the T. cruzi TASV protein family (30.García E.A. Ziliani M. Agiero F. Bernabó G. Sánchez D.O. Tekiel V. TcTASV: a novel protein family in Trypanosoma cruzi identified from a subtractive trypomastigote cDNA library.PLoS Negl Trop Dis. 2010; 4Crossref Scopus (17) Google Scholar, 31.Bernabó G. Levy G. Ziliani M. Caeiro L.D. Sánchez D.O. Tekiel V. TcTASV-C, a protein family in Trypanosoma cruzi that is predominantly trypomastigote-stage specific and secreted to the medium.PLoS One. 2010; 8: e71192Crossref Scopus (19) Google Scholar) and 21 epitopes of high prevalence in healthy humans (not associated to Chagas disease, identified in the IEDB with a search for linear B-cell epitopes from any source with sero-prevalence > = 50%, assayed in at least 20 subjects). The sequences of these 532 proteins (457 T. cruzi proteins, 54 neo-proteins and the additional 21 short peptidic epitopes of high prevalence in healthy humans) are available in supplemental Table S4.Table IChagas-specific positivity rates in different peptide sets. The table shows the total number of distinct peptides assayed in each group; the number of positive peptides (above cutoff of 3) in the averaged signal (combining data from eight peptide-chip assays); the average number of positive peptides in a single experiment (single chip/single sample above cut-off of 7); and the number of peptides that were positive in at least 1 out of the 4 sera samples (pools) tested. Percentages are given in parentheses. Group 5 of 24,000 randomly generated peptides (negative control) had no positives in any caseProtein SetDescriptionNo. of PeptidesPositive PeptidesAverage signal in all chipsIn single chipGroup 1Proteins picked randomly from the proteome38,66450 (0.13%)23.5 ± 17.2Group 2Proteins ranked with a bioinformatics method37,773317 (0.84%)133.25 ± 38.54Group 3Proteins from MASP family65,808797 (1.21%)399.75 ± 219.3Group 4Proteins with previous seroreactivity evidence32,372848 (2.62%)484.75 ± 76.3 Open table in a new tab Synthesis slides (Nexterion P) for the photolithographic synthesis of peptide arrays were purchased from Schott AG, Germany and derivatized with a 2% w/v linear copolymer of N,N′-dimethylacrylamide and aminoethyl methacrylate (both from Sigma-Aldrich) mixed in a 20:1 w/w ratio before polymerization for 2 h at room temperature in freshly degassed 0.1 m sodium borate buffer, pH 8 containing 0.025% v/v TEMED and 0.1% w/v ammonium persulfate (18.Hansen L.B. Buus S. Schafer-Nielsen C. Identification and mapping of linear antibody epitopes in human serum albumin using high-density peptide arrays.PLoS One. 2013; 8: e68902Crossref PubMed Scopus (35) Google Scholar). Each peptide field was composed of 2 × 2 mirrors controlled by the Digital Micro-mirror Device, and with a single-mirror border separation within fields. This setup allows high-throughput while maintaining a high field resolution. The spacer polypeptide DAPAD was added to all peptides at their C-terminal. The FLAG peptide DYKDDDDKK extended c-terminally with a linker peptide (GAPAGAP) was included in the microarray in 852 copies for peptide synthesis quality control and as corner (reference positions) markers. Synthesis of the arrays was performed as described previously (18.Hansen L.B. Buus S. Schafer-Nielsen C. Identification and mapping of linear antibody epitopes in human serum albumin using high-density peptide arrays.PLoS One. 2013; 8: e68902Crossref PubMed Scopus (35) Google Scholar). T. cruzi–infected human sera samples used in this study were obtained from the Laboratorio de Enfermedad de Chagas, Hospital de Niños "Dr. Ricardo Gutierrez" (Buenos Aires, Argentina). All procedures were approved by the institutional review board of this institution. Written informed consent was obtained from all individuals, and all samples were decoded and de-identified before they were provided for research purposes. Sera were collected from clotted blood obtained by venipuncture and analyzed for T. cruzi-specific antibodies by commercially available kits: enzyme-linked immunosorbent assay (ELISA) using total parasite homogenate (Wiener lab, Argentina) and indirect hemagglutination (Polychaco, Buenos Aires, Argentina). The negative panel was composed of 10 samples from healthy, non-infected individuals that gave negative results in the mentioned tests. Using these 10 samples, four different T. cruzi seronegative sera pools were prepared by taking five random samples (3 μl per serum) in each case, and labeled them A_neg, B_neg, C_neg and D_neg. In the case of Chagas-positive samples, we selected nine samples from patients that had no clinical symptoms, and were classified in the Chagas chronic indeterminate stage (19.Rassi Jr., A. Rassi A. Marin-Neto J.A. Chagas disease.Lancet. 2010; 375: 1388-1402Abstract Full Text Full Text PDF PubMed Scopus (1665) Google Scholar). Using these nine samples, four different T. cruzi seropositive sera pools were prepared by taking five random samples (3 μl per serum) in each case, and labeled them A, B, C, and D. We used the smallest pool size, which would cover most of the high prevalence antibody specificities. For this, we estimated that all epitopes of a prevalence of 50% or higher will be present at least in 1 individual serum with a probability higher than 95%. Briefly, for an epitope of prevalence x in the infected population, the probability of randomly taking n subjects without specific antibodies, assuming independence is (1 - x)n. Then, we calculated the minimum x, such that (1 - x)n < 0.05 (i.e. that the probability of not sampling an epitope of prevalence x% or higher in n subjects is less than 0.05). Performing this calculation, we find n = 5, i.e. (1 - 0.5)5 = 0. IgGs were purified from 15 μl sera pools using Melon Gel IgG spin purification kit (Thermo Scientific), following the manufacturer's protocol. Purified IgG samples were checked in 12% SDS-PAGE gels. IgG concentration was estimated after staining by Coomassie Brilliant Blue, by comparison against a standard curve made by electrophoresing different quantities of purified bovine γ-globulin (IgG, 150 kDa, BioRad Laboratories, Hercules, CA). Microarray slides were incubated essentially as described in (16.Buus S. Rockberg J. Forsström B. Nilsson P. Uhlen M. Schafer-Nielsen C. High-resolution mapping of linear antibody epitopes using ultrahigh-density peptide microarrays.Mol. Cell. Proteomics. 2012; 11: 1790-1800Abstract Full Text Full Text PDF PubMed Scopus (123) Google Scholar) with a few modifications to accommodate sequential incubations. Briefly, slides were incubated at room temperature overnight with 1 ml of purified pooled IgGs, diluted to 20 μg/ml in 0.15 m Tris/Acetate pH 8.0, 0.1% v/v Tween20. After washing with the incubation buffer, slides were incubated for 2 h with secondary antibody (Cy3 goat anti-human IgG, Abcam Cat. No. Ab97170) at 1 μg/ml. After a second washing step with incubation buffer, followed by air-drying of the slides in a nitrogen jet, the peptide array slides were scanned and recorded with an Innoscan 900, Cambridge, MA laser scanner (INNOPSYS, Carbonne, France) at 1 μm resolution, with an excitation wavelength of 532 nm. The images recorded with the laser scanner were analyzed using the PepArray analysis program (Schafer-N, Copenhagen Denmark). Auxiliary "marker" peptides with sequence DYKDDDKKGAPAGAP containing the FLAG epitope tag, were used for positioning of the grid and to quantify spots' intensities. For all microarray experiments described, the same procedure was performed first with the negative sample (from healthy subjects), and then sequentially with the positive sample (from infected patients) in the same conditions. Therefore two readouts were obtained per slide: the negative sample data and the cumulative signal of negative and positive sample data. A total of 8 microarray chips were assayed, labeled A1 (Sample A_neg followed by Sample A, Replicate 1), A2 (Sample A_neg followed by Sample A, Replicate 2), A3 (Sample A_neg followed by Sample A, Replicate 3), B1 (Sample B_neg followed by Sample B, Replicate 1), B2 (Sample B_neg followed by Sample B, Replicate 2), C1 (Sample C_neg followed by Sample C, Replicate 1), C2 (Sample C_neg followed by Sample C, Replicate 2), and D1 (Sample D_neg followed by Sample D, Replicate 1). Raw intensity data sets for each chip and sample are available in public databases (See Data availability Section). In previous successful applications of HD-peptide microarrays (16.Buus S. Rockberg J. Forsström B. Nilsson P. Uhlen M. Schafer-Nielsen C. High-resolution mapping of linear antibody epitopes using ultrahigh-density peptide microarrays.Mol. Cell. Proteomics. 2012; 11: 1790-1800Abstract Full Text Full Text PDF PubMed Scopus (123) Google Scholar, 18.Hansen L.B. Buus S. Schafer-Nielsen C. Identification and mapping of linear antibody epitopes in human serum albumin using high-density peptide arrays.PLoS One. 2013; 8: e68902Crossref PubMed Scopus (35) Google Scholar, 32.Andreatta M. Schafer-Nielsen C. Lund O. Buus S. Nielsen M. NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data.PLoS One. 2011; 6: e26781Crossref PubMed Scopus (48) Google Scholar, 33.Roder G. Geironson L. Darabi A. Harndahl M. Schafer-nielsen C. Skjødt K. Buus S. Paulsson K. The outermost N-terminal region of tapasin facilitates folding of major histocompatibility complex class I.Eur. J. Immunol. 2009; 39: 2682-2694Crossref PubMed Scopus (13) Google Scholar), minimal data normalization was performed. However, unlike previous work, here we analyzed a complex antibody sample and compared relative intensities across different experiments. This demanded further normalization. A first normalization step was implemented, aimed at equalizing two se
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