A computational pipeline for diagnostic biomarker discovery in the human pathogen Trypanosoma cruzi

CARMONA S, SARTOR P, LEGUIZAMON S, CAMPETELLA O, AGÜERO F

Montevideo, Uruguay

Conferencia; ISCB Latin America; 2010

International Society for Computational Biology (ISCB)

A computational pipeline for diagnostic biomarker discovery in the human pathogen Trypanosoma cruziSantiago Carmona1, Paula Sartor2, Susana Leguizamón2, Oscar Campetella1 and Fernán Agüero11 Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina2 Departamento de Microbiología, Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires, ArgentinaBackgroundThe protozoan parasite Trypanosoma cruzi is the causative agent of Chagas' disease, endemic in 18 countries in Central and South America, affecting at least 16 million individuals and causing at least 21,000deaths annually. Transmission also occurs in non-endemic countries by way of blood transfusion and organ transplantation. Diagnosis of American trypanosomiasis is based on the detection of antibodies directed against T. cruzi antigens. Poor standardization of reagents causes variation in reproducibility and reliability of diagnostic tests. Furthermore cross-reactivity occurs with antibodies elicited by other pathogens, mainly Leishmania. Our goal is to mine the genomic sequences of T. cruzi to identify new peptidic diagnostic biomarkers.MethodsA bioinformatic strategy was adopted to prioritize peptidic antigens with low cross-reactivity in the genome of T. cruzi. A computational pipeline was developed to assess a set of molecular properties on each protein from the reference T. cruzi genome, such as the most likely subcellular localization or predicted expression level (by mass spec. evidence, number of gene copies and synonymous codon usage bias). At a higher resolution, a set of local properties that change over the length of the protein were also evaluated, such as repetitive motifs, trans-membrane spans, glycosylation sites, polymorphisms (conserved vs. divergent regions), predicted B-cell epitopes, sequence similarity against human proteins and Leishmania, and disorder (structured vs. natively unfolded regions) (Figure 1). A scoring function based on these properties was used to rank each of the ~10 million 12-residue overlapping peptides in which the 22,000 T. cruzi proteins can be virtually fragmented. Experimental validation of predicted epitopes was performed with peptide microarrays, screened using pooled sera from human chagasic patients and controls.ResultsThe genome-wide prioritization uncovered more than 300 promising biomarker candidates. The 200 highest-scoring peptides were selected for immunological validation, along with 40 peptides derived from previously validated B-cell epitopes and an additional set of 40 low-scoring peptides for discovery rate assessment. Preliminary analysis of the microarray images revealed that ~25% (49/200) of the candidate peptides reacted specifically against the positive sera pools assayed. Noteworthy, the reactivity observed for previously validated peptides was ~50% (17/40), while none of the low-scoring peptides reacted.ConclusionThe developed bioinformatic approach proved to be successful, leading from a genome-wide prioritization to the identification of novel peptidic antigens. Moreover, the algorithm may be used to prioritize peptidicbiomarkers in other sequenced pathogen species.AcknowledgementsThis work was funded by the “Special Programme for Research and Training in Tropical Diseases (UNICEF/UNDP/WorldBank/WHO)”, and by the University of San Martín (PROGF7/1). Santiago Carmona is supported by a research training fellowshipfrom the University of San Martín.