"Immunoinformatic Identification of T-Cell and B-Cell Epitopes From Giardia lamblia Immunogenic Proteins as Candidates to Develop Peptide-Based Vaccines Against Giardiasis".

Giardiasis is one of the most common gastrointestinal infections worldwide, mainly in developing countries. The etiological agent is the Giardia lamblia parasite. Giardiasis mainly affects children and immunocompromised people, causing symptoms such as diarrhea, dehydration, abdominal cramps, nausea, and malnutrition. In order to develop an effective vaccine against giardiasis, it is necessary to understand the host-Giardia interactions, the immunological mechanisms involved in protection against infection, and to characterize the parasite antigens that activate the host immune system. In this study, we identify and characterize potential T-cell and B-cell epitopes of Giardia immunogenic proteins by immunoinformatic approaches, and we discuss the potential role of those epitopes to stimulate the host´s immune system. We selected the main immunogenic and protective proteins of Giardia experimentally investigated. We predicted T-cell and B-cell epitopes using immunoinformatic tools (NetMHCII and BCPREDS). Variable surface proteins (VSPs), structural (giardins), metabolic, and cyst wall proteins were identified as the more relevant immunogens of G. lamblia. We described the protein sequences with the highest affinity to bind MHC class II molecules from mouse (I-Ak and I-Ad) and human (DRB1*03:01 and DRB1*13:01) alleles, as well as we selected promiscuous epitopes, which bind to the most common range of MHC class II molecules in human population. In addition, we identified the presence of conserved epitopes within the main protein families (giardins, VSP, CWP) of Giardia. To our knowledge, this is the first in silico study that analyze immunogenic proteins of G. lamblia by combining bioinformatics strategies to identify potential T-cell and B-cell epitopes, which can be potential candidates in the development of peptide-based vaccines. The bioinformatics analysis demonstrated in this study provides a deeper understanding of the Giardia immunogens that bind to critical molecules of the host immune system, such as MHC class II and antibodies, as well as strategies to rational design of peptide-based vaccine against giardiasis.

The role of immunoinformatics in the development of T-cell peptide-based vaccines against Mycobacterium tuberculosis.

INTRODUCTION: Tuberculosis (TB) is a major health problem worldwide. The BCG, the only authorized vaccine to fight TB, shows a variable protection in the adult population highlighting the need of a new vaccine. Immunoinformatics offers a variety of tools that can predict immunogenic T-cell peptides of Mycobacterium tuberculosis (Mtb) that can be used to create a new vaccine. Immunoinformatics has made possible the identification of immunogenic T-cell peptides of Mtb that have been tested in vitro showing a potential for using these molecules as part of a new TB vaccine. AREAS COVERED: This review summarizes the most common immunoinformatics tools to identify immunogenic T-cell peptides and presents a compilation about research studies that have identified T-cell peptides of Mtb by using immunoinformatics. Also, it is provided a summary of the TB vaccines undergoing clinical trials. EXPERT OPINION: In the next few years, the field of peptide-based vaccines will keep growing along with the development of more efficient and sophisticated immunoinformatic tools to identify immunogenic peptides with a greater accuracy.

Identification of immunogenic T-cell peptides of Mycobacterium tuberculosis PE_PGRS33 protein.

The development of a more efficient vaccine is needed to improve tuberculosis control. One of the current approaches is to identify immunogenic T-cell peptides that can elicit a protective and specific immune response. These peptides come from immunogenic proteins of the pathogen. The PE_PGRS33 protein of Mycobacterium tuberculosis has been proved immunogenic. However, little is known about immunogenic T-cell peptides of PE_PGRS33 and their interactions with MHC-II molecules. Therefore, we used the SYFPHEITHI database to determine the immunogenic PE_PGRS33 T-cell peptides. Next, we built homology models by using MOE v2018.1 software in order to obtain information about the specific interactions between the peptides and I-Ak. The AlgPred server was employed to look for allergenic sites in PE_PGRS33. We developed a sequence alignment between PE_PGRS33 and all the human proteins by using BLAST. Three peptides were commercially synthesized, and their activity was evaluated in vitro by the stimulation of PBMC from household contacts of TB patients. Our in silico results showed five immunogenic T-cell peptides. BLAST analysis showed low homology of PE_PGRS33 with human proteins and AlgPred did not reveal allergenic sites in PE_PGRS33. The three peptides triggered the activation of CD4+ T cells from the households contacts, showed by the production of IFN-γ. We identified three immunogenic peptides of PE_PGRS33 that demonstrated activity in vitro which allows to deepen into the immune response towards mycobacterial antigens, moving forward to the identification of new vaccine candidates.