The protozoan parasite Trypanosoma cruzi is the etiologic agent of Chagas disease, a highly debilitating human pathology that affects millions of people in the Americas. The sequencing of this parasite's genome reveals that trans-sialidase/trans-sialidase-like (TcS), a polymorphic protein family known to be involved in several aspects of T. cruzi biology, is the largest T. cruzi gene family, encoding more than 1,400 genes. Despite the fact that four TcS groups are well characterized and only one of the groups contains active trans-sialidases, all members of the family are annotated in the T. cruzi genome database as trans-sialidase. After performing sequence clustering analysis with all TcS complete genes, we identified four additional groups, demonstrating that the TcS family is even more heterogeneous than previously thought. Interestingly, members of distinct TcS groups show distinctive patterns of chromosome localization. Members of the TcSgroupII, which harbor proteins involved in host cell attachment/invasion, are preferentially located in subtelomeric regions, whereas members of the largest and new TcSgroupV have internal chromosomal locations. Real-time RT-PCR confirms the expression of genes derived from new groups and shows that the pattern of expression is not similar within and between groups. We also performed B-cell epitope prediction on the family and constructed a TcS specific peptide array, which was screened with sera from T. cruzi-infected mice. We demonstrated that all seven groups represented in the array are antigenic. A highly reactive peptide occurs in sixty TcS proteins including members of two new groups and may contribute to the known cross-reactivity of T. cruzi epitopes during infection. Taken together, our results contribute to a better understanding of the real complexity of the TcS family and open new avenues for investigating novel roles of this family during T. cruzi infection.

Background Trypanosoma cruzi is the etiological agent of Chagas disease, a debilitating illness that affects millions of people in the Americas. A major finding of the T. cruzi genome project was the discovery of a novel multigene family composed of approximately 1,300 genes that encode mucin-associated surface proteins (MASPs). The high level of polymorphism of the MASP family associated with its localization at the surface of infective forms of the parasite suggests that MASP participates in host–parasite interactions. We speculate that the large repertoire of MASP sequences may contribute to the ability of T. cruzi to infect several host cell types and/or participate in host immune evasion mechanisms.MethodsBy sequencing seven cDNA libraries, we analyzed the MASP expression profile in trypomastigotes derived from distinct host cells and after sequential passages in acutely infected mice. Additionally, to investigate the MASP antigenic profile, we performed B-cell epitope prediction on MASP proteins and designed a MASP-specific peptide array with 110 putative epitopes, which was screened with sera from acutely infected mice.Findings and ConclusionsWe observed differential expression of a few MASP genes between trypomastigotes derived from epithelial and myoblast cell lines. The more pronounced MASP expression changes were observed between bloodstream and tissue-culture trypomastigotes and between bloodstream forms from sequential passages in acutely infected mice. Moreover, we demonstrated that different MASP members were expressed during the acute T. cruzi infection and constitute parasite antigens that are recognized by IgG and IgM antibodies. We also found that distinct MASP peptides could trigger different antibody responses and that the antibody level against a given peptide may vary after sequential passages in mice. We speculate that changes in the large repertoire of MASP antigenic peptides during an infection may contribute to the evasion of host immune responses during the acute phase of Chagas disease.

Identification of novel antigens is essential for developing new diagnostic tests and vaccines. We used DIGE to compare protein expression in amastigote and promastigote forms of Leishmania chagasi. Nine hundred amastigote and promastigote spots were visualized. Five amastigote-specific, 25 promastigote-specific, and 10 proteins shared by the two parasite stages were identified. Furthermore, 41 proteins were identified in the Western blot employing 2-DE and sera from infected dogs. From these proteins, 3 and 38 were reactive with IgM and total IgG, respectively. The proteins recognized by total IgG presented different patterns in terms of their recognition by IgG1 and/or IgG2 isotypes. All the proteins selected by Western blot were mapped for B-cell epitopes. One hundred and eighty peptides were submitted to SPOT synthesis and immunoassay. A total of 25 peptides were shown of interest for serodiagnosis to visceral leishmaniasis. In addition, all proteins identified in this study were mapped for T cell epitopes by using the NetCTL software, and candidates for vaccine development were selected. Therefore, a large-scale screening of L. chagasi proteome was performed to identify new B and T cell epitopes with potential use for developing diagnostic tests and vaccines.

Background: Although the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment. Methods: We performed a gene expression microarray meta-analysis of the tumor against normal tissues in order to identify differentially expressed genes (DEG) shared among all datasets, named core-genes (CG). We confirmed the CG protein expression in pancreatic tissue through The Human Protein Atlas. It was selected five genes with the highest area under the curve (AUC) among these proteins with expression confirmed in the tumor group to train an artificial neural network (ANN) to classify samples. Results: This microarray included 461 tumor and 187 normal samples. We identified a CG composed of 40 genes, 39 upregulated, and one downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that fourteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our ANN, we selected the best genes (AHNAK2, KRT19, LAMB3, LAMC2, and S100P) to classify the samples based on AUC using mRNA expression. The network classified tumor samples with an f1-score of 0.83 for the normal samples and 0.88 for the PDAC samples, with an average of 0.86. The PDAC-ANN could classify the test samples with a sensitivity of 87.6 and specificity of 83.1. Conclusion: The gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on RNA expression. This software can be useful in the PDAC diagnosis.

Background: Although the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment. Methods:We performed a gene expression microarray meta-analysis of the tumor against healthy tissues in order to identify differentially expressed genes shared among all datasets, named core-genes (CG). We confirmed the pancreatic expressed proteins of the CG through The Human Protein Atlas. The five most expressed proteins in the tumor group were selected to train an artificial neural network to classify samples. 2 Results: This microarray included 110 tumor and 77 healthy samples. We identified a CG composed of 60 genes, 58 upregulated and two downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that thirteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our artificial neural network, we used the five most expressed genes (KRT19, LAMC2, MELK, MET, TOP2A). The artificial neural network model (PDAC-ANN) classified the train samples with sensitivity of 0.95, specificity of 0.9, and f1-score of 0.93. The PDAC-ANN could classify the test samples with a sensitivity of 0.97, specificity of 0.88, and f1-score 0.94. Conclusion:The gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on mRNA expression. This software can be useful in the PDAC diagnosis.1 5 migration, and metastasis. The PDAC-ANN trained using gene expression information could classify the samples in normal and PDAC with an f1-score of 0.94 and sensitivity = 0.97. The PDAC-ANN tool can only be used when the gene expression information from KRT19, LAMC2, MELK, MET, and TOP2A are available, in addition to min-max gene expression values rescaling. The PDAC-ANN is a free tool (Additional file 4) that can support in the pancreatic ductal adenocarcinoma diagnosis.

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