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Background Trypanosoma evansi infects a large number of wild and domestic animals and causes a spoliative disease known as surra. It is mechanically transmitted, mainly by biting flies of the genera Tabanus and Stomoxys. The detection of T. evansi DNA in the feeding apparatus of Dichelacera alcicornis and Dichelacera januarii from South America is reported, to the best of our knowledge, for the first time. Methods Tabanids were collected weekly from February 2018 to February 2019 from two sites. The feeding apparatus was removed and DNA extraction, polymerase chain reaction and sequencing were performed. Results A 205-base pair fragment of the variant surface protein RoTat 1.2 gene, confirmed by DNA sequencing, was amplified from the feeding apparatus of D. alcicornis and D. januarii. Conclusions This is, to the best of our knowledge, the first record of T. evansi DNA in South American tabanids. Graphical abstract

Sísifo, figura da mitologia grega, evoca-nos duas distintas ideias: a sagacidade que ludibria os poderosos — por duas vezes enganou as forças do submundo e escapou à morte — e o sofrimento de um esforço que jamais finda. Condenado a uma pena exemplar por enganar as forças do inferno, Sísifo é submetido a um ordálio cíclico e sem fim. Contudo, fica uma questão: Teria a atuação docente, principalmente durante o isolamento social e suas aulas remotas, seu quê de pena sisífica? Ou existe no aparente castigo do filho de Éolo mais que as aparências sugerem? O presente artigo apresenta uma aproximação entre o mito de Sísifo e o fazer docente voltado ao atendimento às classes populares, revelando um drama sob a ótica do esperançar freireano, sob os auditores de diferentes dimensões do tempo: Chronos, Aión e Kairós. Tal drama surge no tempo aiônico, o tempo da infância, no tempo do fazer sempre de novo sem deixar pesar em vão o fardo da pedra. Pedra esta ressignificada no presente texto, repensada como um fazer sempre novo, mesmo que aparentemente repetitivo. Aludimos ao esforço sísifo de carregar a mesma pedra morro acima a prática docente pautada no ensinar e aprender que se confundem e se complementam em uma espera esperançosa e utópica imbricada no inconformismo e na fé da emancipação social.

Trypanosoma evansi is a parasite that is phylogenetically close to Trypanosoma brucei and is the causative agent of a disease known as surra. Surra is responsible for a high mortality rate in livestock and large economic losses in the Americas, Africa, and Asia. This work aimed to analyze in vitro secreted proteins from T. evansi and identify potential treatment and diagnostic biomarkers for surra diagnosis. Two groups were used. In one group the parasites were purified using a DEAE-Cellulose column and maintained in a secretion medium while in the other group the parasites were not purified. Each group was further divided to be maintained at either 37 °C or 27 °C. We identified 246 proteins through mass spectrometry and found that the temperature appears to modulate protein secretion. We found minimal variations in the protein pools from pure and non-purified sets. We observed an emphasis on proteins associated to vesicles, glycolysis, and cellular homeostasis through the enrichment of GO. Also, we found that most secretome proteins share homologous proteins with T. b. brucei, T. b. gambiense, T. vivax, T. equiperdum, and T. b. rhodesiense secretome but unique T. evansi epitopes with potential biomarkers for surra diagnosis were detected. SIGNIFICANCE: Trypanosoma evansi is a parasite of African origin that is phylogenetically close to Trypanosoma brucei. As with other trypanosomatids and blood parasites, its infection causes non-pathognomonic symptoms, which makes its diagnosis difficult. One great problem is the fact that no diagnostic test differentiates between Trypanosoma equiperdum and T. evansi, which is a problem in South America and Asia, and Africa. Thus, it is urgent to study the biochemistry of the parasite to discover proteins that can be used for differential diagnosis or be possible therapeutic targets. In addition, the study of the secretome can point out proteins that are used by the parasite in its interactions with the host, helping to understand the progression of the disease.

Assignment of gene function has been a crucial, laborious, and time-consuming step in genomics. Due to a variety of sequencing platforms that generates increasing amounts of data, manual annotation is no longer feasible. Thus, the need for an integrated, automated pipeline allowing the use of experimental data towards validation of in silico prediction of gene function is of utmost relevance. Here, we present a computational workflow named AnnotaPipeline that integrates distinct software and data types on a proteogenomic approach to annotate and validate predicted features in genomic sequences. Based on FASTA (i) nucleotide or (ii) protein sequences or (iii) structural annotation files (GFF3), users can input FASTQ RNA-seq data, MS/MS data from mzXML or similar formats, as the pipeline uses both transcriptomic and proteomic information to corroborate annotations and validate gene prediction, providing transcription and expression evidence for functional annotation. Reannotation of the available Arabidopsis thaliana, Caenorhabditis elegans, Candida albicans, Trypanosoma cruzi, and Trypanosoma rangeli genomes was performed using the AnnotaPipeline, resulting in a higher proportion of annotated proteins and a reduced proportion of hypothetical proteins when compared to the annotations publicly available for these organisms. AnnotaPipeline is a Unix-based pipeline developed using Python and is available at: https://github.com/bioinformatics-ufsc/AnnotaPipeline.

Selenoproteins have been described in all three domains of life and their function has been mainly associated with oxidative stress defense. Canonical elements required for selenoprotein production have been identified in members of the kinetoplastid group supporting the existence of a complete selenocysteine synthesis pathway in these organisms. Currently, nothing is known regarding the selenocysteine pathway in Trypanosoma evansi. In this study, we identified the expression of the elements selB, selC, selD, PSTK and selTRYP at the mRNA level in T. evansi. All translated proteins (selD, PSTK, selTRYP and selB) have the domains predicted and higher identity with Trypanosoma brucei. gambiense. The selenophosphate synthetase protein was localized in the cytoplasm. Our results support the existence of an active selenocysteine pathway in T. evansi.

Epitopes are portions of a protein that are recognized by antibodies. These small amino acid sequences represent a significant breakthrough in a branch of bioinformatics called immunoinformatics. Various software are available for linear B-cell epitope (BCE) prediction such as ABCPred, SVMTrip, EpiDope, and EpitopeVec; a well-known BCE predictor is BepiPred-2.0. However, despite the prediction, there are several essential steps, such as epitope assembly, evaluation, and searching for epitopes in other proteomes. Here, we present EpiBuilder ( https://epibuilder.sourceforge.io ), a user friendly software that assists in epitope assembly, classifying and searching using input results of BepiPred-2.0. EpiBuilder generates several output results from these data and supports a proteome-wide processing approach. In addition, this software provides the following features: Chou & Fasman beta-turn prediction, Emini surface accessibility prediction, Karplus and Schulz flexibility prediction, Kolaskar and Tongaonkar antigenicity, Parker hydrophilicity prediction, N-glycosylation domains, and hydropathy. These information generate a unique topology for each epitope, visually demonstrating its characteristics. The software can search the entire epitope sequence in various FASTA files, and it allows to use BLASTP to identify epitopes that eventually have sequence variations. As an EpiBuilder application, we developed a epitope dataset from the protozoan Trypanosoma brucei gambiense, the gram-positive bacterium Clostridioides difficile, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Genomic and proteomic advances in extremophile microorganism studies are increasingly demonstrating their ability to produce a variety of enzymes capable of converting biomass into bioenergy. Such microorganisms are found in environments with nutritional restrictions, anaerobic environments, high salinity, varying pH conditions and extreme natural environments such as hydrothermal vents, soda lakes, and Antarctic sediments. As extremophile microorganisms and their enzymes are found in widely disparate locations, they generate new possibilities and opportunities to explore biotechnological prospecting, including biofuels (biogas, hydrogen and ethanol) with an aim toward using multi-omics tools that shed light on biotechnological breakthroughs.

Trypanosoma evansi is the agent of "surra," a trypanosomosis endemic in many areas worldwide. Trypanosoma proteins released/secreted during infection are attractive biomarkers for disease detection and monitoring. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), we performed a comprehensive analysis of the serum proteome of mice infected with T.evansi and detected changes in the abundance of parasite and host serum proteins during infection. Following bioinformatics analysis, 30 T. evansi proteins were identified in the mice serum including known targets such as pyruvate kinase 1, β-tubulin, actin A, heat shock protein 70, and cyclophilin A. We also identified two exclusive VSG epitopes which are novel putative biomarker targets. In addition, upregulation of 31 mouse proteins, including chitinase-like protein 3 and monocyte differentiation antigen CD14, were observed. Identification of parasite-specific biomarkers in the host serum is critical for the development of reliable serological/ assays for differential diagnosis.

This paper describes the development of a new hybrid meta-heuristic of optimization based on a viral lifecycle, specifically the retroviruses (the nature's swiftest evolvers'), called Retroviral Iterative Genetic Algorithm (RIGA). This algorithm uses Genetics Algorithms (GA) structures with features of retroviral replication, providing a great genetic diversity, confirmed by better results achieved by RIGA comparing with GA applied to some Real-Valued Benchmarking Functions.

This paper describes the development of a new hybrid meta-heuristic of optimization based on a viral lifecycle, specifically the retroviruses (the nature's swiftest evolvers'), called Retroviral Iterative Genetic Algorithm (RIGA). This algorithm uses Genetics Algorithms (GA) structures with features of retroviral replication, providing a great genetic diversity, confirmed by better results achieved by RIGA comparing with GA applied to some Real-Valued Benchmarking Functions.