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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.

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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.

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The proteins that have structural disorder exemplify a class of proteins which is part of a new frontier in structural biology that demands a new understanding of the paradigm of structure/function correlations. In order to address the location, relative distances and the functional/structural correlation between disordered and conserved domains, consensus disordered predictions were mapped together with CDD domains in Leishmania braziliensis M2904, Leishmania infantum JPCM5, Trypanosoma cruzi CL-Brener Esmeraldo-like, Trypanosoma cruzi Dm28c, Trypanosoma cruzi Sylvio X10, Blechomonas ayalai B08-376 and Paratrypanosoma confusum CUL13 predicted proteomes. Our results depicts the role of protein disorder in key aspects of parasites biology highlighting: a) statistical significant association between genome structural location of protein disordered consensus stretches and functional domains; b) that disordered protein stretches appear in greater percentage at upstream or downstream position of the predicted domain; c) a possible role of structural disorder in several gene expression, control points that includes but are not limited to: i) protein folding; ii) protein transport and degradation; and iii) protein modification. In addition, for values of protein with disorder content greater than 40%, a small percentage of protein binding sites in IDPs/IDRs, a higher hypothetical protein annotation frequency was observed than expected by chance and trypanosomatid multigene families linked with virulence are rich in protein with disorder content. SIGNIFICANCE: T. cruzi and Leishmania spp are the etiological agents of Chagas disease and leishmaniasis, respectively. Currently, no vaccine or effective drug treatment is available against these neglected diseases and the knowledge about the post-transcriptional and post-translational mechanisms of these organisms, which are key for this scenario, remain scarce. This study depicts the potential impact of the proximity between protein structural disorder and functional domains in the post-transcriptional regulation of pathogenic versus human non-pathogenic trypanosomatids. Our results revealed a significant statistical relationship between the genome structural locations of these two variables and disordered regions appearing more frequently at upstream or downstream positions of the CDD locus domain. This flexibility feature would maintain structural accessibility of functional sites for post-translational modifications, shedding light into this important aspect of parasite biology. This hypothesis is corroborated by the functional enrichment analysis of disordered proteins subset that highlight the involvement of this class of proteins in protein folding, protein transport and degradation and protein modification. Furthermore, our results pointed out: a) the impact of protein disorder in the process of genome annotation (proteins tend to be annotated as hypothetical when the disorder content reaches ~40%); b) that trypanosomatid multigenic families linked with virulence have a key protein disorder content.

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We describe the optimization of a simplified sample preparation method which permits rapid and direct detection of SARS-CoV-2 RNA within saliva using reverse-transcription loop-mediated isothermal amplification (RT-LAMP). Treatment of saliva samples prior to RT-LAMP by dilution 1:1 in Mucolyse, followed by dilution (within the range of 1:5 to 1:40) in 10% (w/v) Chelex(C) 100 Resin and a 98{degrees}C heat step for 2 minutes enabled detection of SARS-CoV-2 RNA in all positive saliva samples tested, with no amplification detected in pooled negative saliva. The time to positivity for which SARS- CoV-2 RNA was detected in these positive saliva samples was proportional to the real-time reverse- transcriptase PCR cycle threshold (CT), with SARS-CoV-2 RNA detected in as little as 05:43 (CT 21.08), 07:59 (CT 24.47) and 08:35 (CT 25.27) minutes, respectively. The highest CT where direct RT-LAMP detected SARS-CoV-2 RNA was 31.39 corresponding to a 1:40 dilution of a positive saliva sample with a starting CT of 25.27. When RT-LAMP was performed on pools of SARS-CoV-2 negative saliva samples spiked with whole inactivated SARS-CoV-2 virus, RNA was detected at dilutions spanning 1:5 to 1:160 representing CTs spanning 22.49-26.43. Here we describe a simple but critical rapid sample preparation method which can be used up front of RT-LAMP to permit direct detection of SARS-CoV- 2 within saliva samples. Saliva is a sample which can be collected non-invasively without the use of highly skilled staff and critically can be obtained from both health care and home settings. Critically, this approach overcomes both the requirement and validation of different swabs and the global bottleneck observed in obtaining RNA extraction robots and reagents to enable molecular testing by PCR. Such testing opens the possibility of public health approaches for effective intervention to control the COVID-19 pandemic through regular SARS-CoV-2 testing at a population scale, combined with isolation and contact tracing for positive cases.