Background: Chagas disease (CD), caused by the parasite Trypanosoma cruzi, is a serious public health concern in Latin America. Nifurtimox and benznidazole (BZ), the only two drugs currently approved for the treatment of CD, have very low efficacies in the chronic phase of the disease and several toxic side effects. Trypanosoma cruzi strains that are naturally resistant to both drugs have been reported. We performed a comparative transcriptomic analysis of wild-type and BZ-resistant T. cruzi populations using high-throughput RNA sequencing to elucidate the metabolic pathways related to clinical drug resistance and identify promising molecular targets for the development of new drugs for treating CD. Methods: All complementary DNA (cDNA) libraries were constructed from the epimastigote forms of each line, sequenced and analysed using the Prinseq and Trimmomatic tools for the quality analysis, STAR as the aligner for mapping the reads against the reference genome (T. cruzi Dm28c—2018), the Bioconductor package EdgeR for statistical analysis of differential expression and the Python-based library GOATools for the functional enrichment analysis. Results: The analytical pipeline with an adjusted P-value of < 0.05 and fold-change > 1.5 identified 1819 transcripts that were differentially expressed (DE) between wild-type and BZ-resistant T. cruzi populations. Of these, 1522 (83.7%) presented functional annotations and 297 (16.2%) were assigned as hypothetical proteins. In total, 1067 transcripts were upregulated and 752 were downregulated in the BZ-resistant T. cruzi population. Functional enrichment analysis of the DE transcripts identified 10 and 111 functional categories enriched for the up- and downregulated transcripts, respectively. Through functional analysis we identified several biological processes potentially associated with the BZ-resistant phenotype: cellular amino acid metabolic processes, translation, proteolysis, protein phosphorylation, RNA modification, DNA repair, generation of precursor metabolites and energy, oxidation–reduction processes, protein folding, purine nucleotide metabolic processes and lipid biosynthetic processes. Conclusions: The transcriptomic profile of T. cruzi revealed a robust set of genes from different metabolic pathways associated with the BZ-resistant phenotype, proving that T. cruzi resistance mechanisms are multifactorial and complex. Biological processes associated with parasite drug resistance include antioxidant defenses and RNA processing. The identified transcripts, such as ascorbate peroxidase (APX) and iron superoxide dismutase (Fe-SOD), provide important information on the resistant phenotype. These DE transcripts can be further evaluated as molecular targets for new drugs against CD. Graphical

Background: One of the major challenges to leishmaniasis treatment is the emergence of parasites resistant to antimony. To study differentially expressed genes associated with drug resistance, we performed a comparative transcriptomic analysis between wild-type and potassium antimonyl tartrate (Sb^III)-resistant Leishmania infantum lines using high-throughput RNA sequencing. Methods: All the cDNA libraries were constructed from promastigote forms of each line, sequenced and analyzed using STAR for mapping the reads against the reference genome (L. infantum JPCM5) and DESeq2 for differential expression statistical analyses. All the genes were functionally annotated using sequence similarity search. Results: The analytical pipeline considering an adjusted p-value < 0.05 and fold change > 2.0 identified 933 transcripts differentially expressed (DE) between wild-type and Sb^III-resistant L. infantum lines. Out of 933 DE transcripts, 504 presented functional annotation and 429 were assigned as hypothetical proteins. A total of 837 transcripts were upregulated and 96 were downregulated in the Sb^III-resistant L. infantum line. Using this DE dataset, the proteins were further grouped in functional classes according to the gene ontology database. The functional enrichment analysis for biological processes showed that the upregulated transcripts in the Sb^III-resistant line are associated with protein phosphorylation, microtubule-based movement, ubiquitination, host–parasite interaction, cellular process and other categories. The downregulated transcripts in the Sb^III-resistant line are assigned in the GO categories: ribonucleoprotein complex, ribosome biogenesis, rRNA processing, nucleosome assembly and translation. Conclusions: The transcriptomic profile of L. infantum showed a robust set of genes from different metabolic pathways associated with the antimony resistance phenotype in this parasite. Our results address the complex and multifactorial antimony resistance mechanisms in Leishmania, identifying several candidate genes that may be further evaluated as molecular targets for chemotherapy of leishmaniasis. Graphical

Many vaccine candidates against visceral leishmaniasis (VL) have been proposed; however, to date, none of them have been efficacious for the human or canine disease. On this basis, the design of leishmaniasis vaccines has been constantly changing, and the use of approaches to select specific epitopes seems to be crucial in this scenario. The ability to predict T cell-specific epitopes makes immunoinformatics an even more necessary approach, as in VL an efficient immune response against the parasite is triggered by T lymphocytes in response to Leishmania spp. immunogenic antigens. Moreover, the success of vaccines depends on the capacity to generate long-lasting memory and polyfunctional cells that are able to eliminate the parasite. In this sense, our study used a combination of different approaches to develop potential chimera candidate vaccines against VL. The first point was to identify the most immunogenic epitopes of Leishmania infantum proteins and construct chimeras composed of Major histocompatibility complex (MHC) class I and II epitopes. For this, we used immunoinformatics features. Following this, we validated these chimeras in a murine model in a thorough memory study and multifunctionality of T cells that contribute to a better elucidation of the immunological protective mechanisms of polyepitope vaccines (chimera A and B) using multicolor flow cytometry. Our results showed that in silico-designed chimeras can elicit polyfunctional T cells producing T helper (Th)1 cytokines, a strong immune response against Leishmania antigen, and the generation of central and effector memory T cells in the spleen cells of vaccinated animals that was able to reduce the parasite burden in this organ. These findings contribute two potential candidate vaccines against VL that can be used in further studies, and help in this complex field of vaccine development against this challenging parasite.

Reverse vaccinology or immunoinformatics is a computational methodology which integrates data from in silico epitope prediction, associated to other important information as, for example, the predicted subcellular location of the proteins used in the design of the context of vaccine development. This approach has the potential to search for new targets for vaccine development in the predicted proteome of pathogenic organisms. To date, there is no effective vaccine employed in vaccination campaigns against visceral leishmaniasis (VL). For the first time, herein, an in silico, in vitro, and in vivo peptide screening was performed, and immunogenic peptides were selected to constitute VL peptide-based vaccines. Firstly, the screening of in silico potential peptides using dogs naturally infected by L. infantum was conducted and the peptides with the best performance were selected. The mentioned peptides were used to compose Cockt-1 (cocktail 1) and Cockt-2 (cocktail 2) in combination with saponin as the adjuvant. Therefore, tests for immunogenicity, polyfunctional T-cells, and the ability to induce central and effector memory in T-lymphocytes capacity in reducing the parasite load on the spleen for Cockt-1 and Cockt-2 were performed. Among the vaccines under study, Cockt-1 showed the best results, eliciting CD4⁺ and CD8⁺ polyfunctional T-cells, with a reduction in spleen parasitism that correlates to the generation of T CD4⁺ central memory and T CD8⁺ effector memory cells. In this way, our findings corroborate the use of immunoinformatics as a tool for the development of future vaccines against VL.

Therapeutic cancer vaccines have been considered in recent decades as important immunotherapeutic strategies capable of leading to tumor regression. In the development of these vaccines, the identification of neoepitopes plays a critical role, and different computational methods have been proposed and employed to direct and accelerate this process. In this context, this review identified and systematically analyzed the most recent studies published in the literature on the computational prediction of epitopes for the development of therapeutic vaccines, outlining critical steps, along with the associated program’s strengths and limitations. A scoping review was conducted following the PRISMA extension (PRISMA-ScR). Searches were performed in databases (Scopus, PubMed, Web of Science, Science Direct) using the keywords: neoepitope, epitope, vaccine, prediction, algorithm, cancer, and tumor. Forty-nine articles published from 2012 to 2024 were synthesized and analyzed. Most of the identified studies focus on the prediction of epitopes with an affinity for MHC I molecules in solid tumors, such as lung carcinoma. Predicting epitopes with class II MHC affinity has been relatively underexplored. Besides neoepitope prediction from high-throughput sequencing data, additional steps were identified, such as the prioritization of neoepitopes and validation. Mutect2 is the most used tool for variant calling, while NetMHCpan is favored for neoepitope prediction. Artificial/convolutional neural networks are the preferred methods for neoepitope prediction. For prioritizing immunogenic epitopes, the random forest algorithm is the most used for classification. The performance values related to the computational models for the prediction and prioritization of neoepitopes are high; however, a large part of the studies still use microbiome databases for training. The in vitro/in vivo validations of the predicted neoepitopes were verified in 55% of the analyzed studies. Clinical trials that led to successful tumor remission were identified, highlighting that this immunotherapeutic approach can benefit these patients. Integrating high-throughput sequencing, sophisticated bioinformatics tools, and rigorous validation methods through in vitro/in vivo assays as well as clinical trials, the tumor neoepitope-based vaccine approach holds promise for developing personalized therapeutic vaccines that target specific tumor cancers.