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.

Role of Infections in the Pathogenesis of Rheumatoid Arthritis: Focus on Mycobacteria

Rheumatoid arthritis (RA) is a systemic inflammatory autoimmune disease characterized by chronic erosive polyarthritis. A complex interaction between a favorable genetic background, and the presence of a specific immune response against a broad-spectrum of environmental factors seems to play a role in determining susceptibility to RA. Among different pathogens, mycobacteria (including Mycobacterium avium subspecies paratuberculosis, MAP), and Epstein–Barr virus (EBV), have extensively been proposed to promote specific cellular and humoral response in susceptible individuals, by activating pathways linked to RA development. In this review, we discuss the available experimental and clinical evidence on the interplay between mycobacterial and EBV infections, and the development of the immune dysregulation in RA.

Challenges and solutions for a rational vaccine design for TB-endemic regions

Vaccines have been successful for global eradication or control of dreaded diseases such as smallpox, diphtheria, tetanus, yellow fever, whooping cough, polio, and measles. Unfortunately, this success has not been achieved for controlling tuberculosis (TB) worldwide. Bacillus Calmette Guérin (BCG) is the only available vaccine against TB. Paradoxically, BCG has deciphered success in the Western world but has failed in TB-endemic areas. In this article, we highlight and discuss the aspects of immunity responsible for controlling Mycobacterium tuberculosis infection and factors responsible for the failure of BCG in TB-endemic countries. In addition, we also suggest strategies that contribute toward the development of successful vaccine in protecting populations where BCG has failed.

Lipidated promiscuous peptides vaccine for tuberculosis-endemic regions

Despite nine decades of Bacillus Calmette--Guérin (BCG) vaccination, tuberculosis continues to be a major global health challenge. Clinical trials worldwide have proved the inadequacy of the BCG vaccine in preventing the manifestation of pulmonary tuberculosis in adults. Ironically, the efficacy of BCG is poorest in tuberculosis endemic areas. Factors such as nontuberculous or environmental mycobacteria and helminth infestation have been suggested to limit the efficacy of BCG. Hence, in high TB-burden countries, radically novel strategies of vaccination are urgently required. Here we showcase the properties of lipidated promiscuous peptide vaccines that target and activate cells of the innate and adaptive immune systems by employing a Toll-like receptor-2 agonist, S-[2,3-bis(palmitoyloxy)propyl]cysteine (Pam2Cys). Such a strategy elicits robust protection and enduring memory responses by type 1 T helper cells (Th1). Consequently, lipidated peptides may yield a better vaccine than BCG.

Predictions versus high-throughput experiments in T-cell epitope discovery: competition or synergy?

Prediction methods as well as experimental methods for T-cell epitope discovery have developed significantly in recent years. High-throughput experimental methods have made it possible to perform full-length protein scans for epitopes restricted to a limited number of MHC alleles. The high costs and limitations regarding the number of proteins and MHC alleles that are feasibly handled by such experimental methods have made in silico prediction models of high interest. MHC binding prediction methods are today of a very high quality and can predict MHC binding peptides with high accuracy. This is possible for a large range of MHC alleles and relevant length of binding peptides. The predictions can easily be performed for complete proteomes of any size. Prediction methods are still, however, dependent on good experimental methods for validation, and should merely be used as a guide for rational epitope discovery. We expect prediction methods as well as experimental validation methods to continue to develop and that we will soon see clinical trials of products whose development has been guided by prediction methods.

Potential T cell epitopes of Mycobacterium tuberculosis that can instigate molecular mimicry against host: implications in autoimmune pathogenesis

Background

Molecular mimicry between microbial antigens and host-proteins is one of the etiological enigmas for the occurrence of autoimmune diseases. T cells that recognize cross-reactive epitopes may trigger autoimmune reactions. Intriguingly, autoimmune diseases have been reported to be prevalent in tuberculosis endemic populations. Further, association of Mycobacterium tuberculosis (M. tuberculosis) has been implicated in different autoimmune diseases, including rheumatoid arthritis and multiple sclerosis. Although, in silico analyses have identified a number of M. tuberculosis specific vaccine candidates, the analysis on prospective cross-reactive epitopes, that may elicit autoimmune response, has not been yet attempted. Here, we have employed bioinformatics tools to determine T cell epitopes of homologous antigenic regions between M. tuberculosis and human proteomes.

Results

Employing bioinformatics tools, we have identified potentially cross-reactive T cell epitopes restricted to predominant class I and II alleles of human leukocyte antigens (HLA). These are similar to peptides of mycobacterial proteins and considerable numbers of them are promiscuous. Some of the identified antigens corroborated with established autoimmune diseases linked with mycobacterial infection.

Conclusions

The present study reveals many target proteins and their putative T cell epitopes that might have significant application in understanding the molecular basis of possible T cell autoimmune reactions during M. tuberculosis infections.

Promiscuous peptide of 16 kDa antigen linked to Pam2Cys protects against Mycobacterium tuberculosis by evoking enduring memory T-cell response

One of the main reasons considered for BCG failure in tuberculosis-endemic areas is impediment by environmental mycobacteria in its processing and generation of memory T-cell response. To overcome this problem, we developed a unique lipopeptide (L91) by linking the promiscuous peptide (sequence 91-110) of 16 kDa antigen of Mycobacterium tuberculosis to Pam2Cys. L91 does not require extensive antigen processing and generates enduring Th1 memory response. This is evidenced by the fact that L91 significantly improved the activation, proliferation, and generation of protective T cells. Furthermore, L91 surmounts the barrier of major histocompatibility complex polymorphism and induces better protection than BCG. This peptide has self-adjuvanting properties and activates dendritic cells. Importantly, L91 activates T cells isolated from purified protein derivative-positive healthy volunteers that responded weakly to free peptide (F91). In essence, L91 can be a potent future vaccine candidate against tuberculosis.

Immunotherapy of chronic myeloid leukemia: present state and future prospects

In spite of the considerable successes that have been achieved in the treatment of chronic myeloid leukemia (CML), cure for the disease can only be obtained by the present means in a rather small minority of patients. During the past decade, considerable progress has been made in the understanding of the immunology of CML, which has raised hopes that this disease may be curable by supplementing the current targeted chemotherapy with immunotherapeutic approaches. More than ten small-scale clinical trials have been carried out with experimental vaccines predominantly based on the p210bcr-abl fusion protein. Their results suggested beneficial effects in some patients. Recent data obtained in human patients as well as in animal models indicate that the p210bcr-abl protein does not carry the immunodominant epitope(s). These observations, combined with the recognition of an ever increasing number of other immunogenic proteins in CML cells, strongly support the concept that gene-modified, cell-based vaccines containing the full spectrum of tumor antigens might be the most effective immunotherapeutic approach. Recently created mathematical models have provided important leads for the timing of the combination of targeted drug therapy with vaccine administration. A strategy of how targeted drug therapy might be combined with vaccination is outlined.

T-cell epitope prediction and immune complex simulation using molecular dynamics: state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics.

Major histocompatibility complex class I binding predictions as a tool in epitope discovery

SUMMARY

Over the last decade, in silico models of the major histocompatibility complex (MHC) class I pathway have developed significantly. Before, peptide binding could only be reliably modelled for a few major human or mouse histocompatibility molecules; now, high-accuracy predictions are available for any human leucocyte antigen (HLA) -A or -B molecule with known protein sequence. Furthermore, peptide binding to MHC molecules from several non-human primates, mouse strains and other mammals can now be predicted. In this review, a number of different prediction methods are briefly explained, highlighting the most useful and historically important. Selected case stories, where these 'reverse immunology' systems have been used in actual epitope discovery, are briefly reviewed. We conclude that this new generation of epitope discovery systems has become a highly efficient tool for epitope discovery, and recommend that the less accurate prediction systems of the past be abandoned, as these are obsolete.