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Background Leprosy is caused by Mycobacterium leprae and Mycobacterium lepromatosis. Most of the affected population lives in low-income countries and may take up to 10 years to show any clinical signs, which is how physicians diagnose it. However, due to progressive cell damage, early diagnosis is very important. The best way to confirm leprosy is through bacilloscopic, which only confirms the diagnosis and has low accuracy or PCR, that requires specialized operators and is expensive. Since the bacteria are fastidious and do not grow in any culture media, therefore, diagnosing leprosy in the lab is still a challenge. In this concern, a recombinant multi-epitope protein can be a beneficial strategy in the management of the diagnosis, as diverse immunogenic epitopes are precisely selected to detect specific antibodies. Therefore, the purposes of the present study were to select immunogenic epitopes from different relevant proteins, with immunogenic properties, and then to construct a recombinant multi-epitope protein that accuses the presence of the antibodies in the early stages of the disease, making it more than appropriate to be applied as a diagnostic tool. Results We selected 22 common proteins from both species and, using bioinformatics tools, predicted B and T cell epitopes. After multiple filtering and analyzing, we ended up with 29 epitopes {MHC-I (total 18) and MHC-II (total 11)} from 10 proteins, which were then merged into one construct. Its secondary and tertiary structures were also predicted and refined to comprise the amino acid residues in the best conformation possible. The multi-epitope protein construct was stable, non-host homologous, non-allergic, non-toxic, and elicit humoral and cellular responses. It has conformational B cell epitopes and potential to elicit IFN-γ, IL-4, and IL-10 secretion. Conclusions This novel recombinant multi-epitope protein constructed using the common epitopes from M. leprae and M. lepromatosis has a huge immunological potential, is stable, and can be lyophilized to be used in ELISA plates or even in biosensors, which are user-friendly diagnosis tools, facilitating translation into human sample tests.

CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior O Mycoplasma pneumoniae é uma bactéria com características únicas quanto a seus aspectos morfológicos e metabólicos, sendo um dos principais patógenos associados à pneumonia, que mata cerca de três milhões de pessoas anualmente, e que tem se tornado mais incidente nos últimos anos graças ao sucesso da vacina pneumocócica. As peculiaridades relacionadas ao M.pneumoniae corroboram com a resistência à certos tipos de antibióticos, além da evidente capacidade de resistir aos fármacos atuantes, ademais, esse patógeno é extremamente difícil de ser cultivado e trabalhado em laboratório. A carência por vacinas contra a pneumonia é uma questão de preocupação global visto o aumento de patógenos resistentes, custos hospitalares associados à doença e a alta taxa de mortalidade. Métodos de desenvolvimento de vacinas utilizando dados genômicos por meio de ferramentas de bioinformática, demonstram ser estratégias promissoras, principalmente para microorganismos de difícil cultivo como o M.pneumoniae. Dessa forma, por meio da Vacinologia Reversa e Imunoinformática, foi construído, in silico, uma vacina multi-epítopo de alta cobertura contra o M.pneumoniae. A vacina construída contém epítopos pertencentes á lipoproteínas e proteínas adesinas altamente imunogênicas, selecionadas criteriosamente a fim de pertencerem à todas 88 linhagens de M.pneumoniae estudadas e serem reconhecidos por moléculas do complexo de histocompatibilidade frequentes na população mundial. Testes estruturais demonstraram estabilidade e qualidade relacionada aos parâmetros físicoquímicos. O docking molecular do complexo vacina-receptor do tipo Toll, juntamente com a simulação da dinâmica molecular demonstraram satisfatória interação com o sistema imune, além das avaliações de imunogenicidade e simulação imunológica. A vacina demonstrou ser segura e sem potencial alérgico. Dessa forma, essa prospecção contribui para o desenvolvimento de uma vacina e prevenção da pneumonia causada pelo M.pneumoniae, demonstrando uma estratégia alternativa para formulação de vacinas contra patógenos de difícil cultivo, corroborando para o entendimento da interação e dos mecanismos imunológicos decorrentes das infecções por M.pneumoniae. Mycoplasma pneumoniae is a bacterium with unique characteristics in terms of its morphological and metabolic aspects, being one of the main pathogens associated with pneumonia, which kills about three million people annually, and which has become more incident in recent years thanks to the success pneumococcal vaccine. The peculiarities related to M.pneumoniae corroborate the resistance to certain types of antibiotics, in addition to the evident ability to resist active drugs, also, this pathogen is extremely difficult to be cultivated and used in the laboratory. The lack of pneumonia vaccines is a matter of global concern given the increase in resistant pathogens, hospital costs associated with the disease and the high mortality rate. Vaccine development methods using genomic data applying bioinformatics tools, have demonstrated to be promising strategies, mainly for microorganisms of difficult cultivation such as M. pneumoniae. Thus, through Reverse Vacinology and Immunoinformatics, a high coverage multi-epitope vaccine against M.pneumoniae was built in silico. The constructed vaccine contains epitopes belonging to highly immunogenic lipoproteins and adhesin proteins, carefully selected to belong to all 88 M.pneumoniae strains studied and to be recognized by molecules of the histocompatibility complex common in the world population. Structural tests have demonstrated stability and quality related to physical-chemical parameters. The molecular docking of the Toll-like vaccine-receptor complex, together with the simulation of molecular dynamics, demonstrated satisfactory interaction with the immune system, besides to evaluations of immunogenicity and immunological simulation. The vaccine has been shown to be safe and without allergic potential. Thus, this prospecting contributes to the development of a vaccine and prevention of pneumonia caused by M.pneumoniae, demonstrating an alternative strategy for formulating vaccines against pathogens that are difficult to cultivate furthermore, corroborating to the understanding of the interaction and the immunological mechanisms resulting from infections by M.pneumoniae.

Pneumonia is an infectious disease caused by bacteria, viruses or fungi that results in millions of deaths globally. Despite the existence of prophylactic methods against some of the major pathogens of the disease, there is no efficient prophylaxis against atypical agents such as Mycoplasma pneumoniae , a bacterium associated with cases of community-acquired pneumonia. Because of the morphological peculiarity of M. pneumoniae , which leads to an increased resistance to antibiotics, studies that prospectively investigate the development of vaccines and drug targets appear to be one of the best ways forward. Hence, in this paper, bioinformatics tools were used for vaccine and pharmacological prediction. We conducted comparative genomic analysis on the genomes of 88 M. pneumoniae strains, as opposed to a reverse vaccinology analysis, in relation to the capacity of M. pneumoniae proteins to bind to the major histocompatibility complex, revealing seven targets with immunogenic potential. Predictive cytoplasmic proteins were tested as potential drug targets by studying their structures in relation to other proteins, metabolic pathways and molecular anchorage, which identified five possible drug targets. These findings are a valuable addition to the development of vaccines and the selection of new in vivo drug targets that may contribute to further elucidating the molecular basis of M. pneumoniae –host interactions.

10 molecules with higher binding affinity for each pharmacological target of interest.

the map shows levels of similarity between each strain of M. pneumoniae studied according to a color scale. The scale goes from green, which represents a higher level of similarity to red, representing low level of similarity.

Table 1 represents the identity of the targets considered to be very high capacity for modeling through MHOLline. Table 2 shows the classification of all potential drug targets according to the MHOLline quality filter and Table 3 shows the MHOLline criteria for target classification according to program specifications.