Bioinformatics for study of autoimmunity

Time for T? Immunoinformatics addresses vaccine design for neglected tropical and emerging infectious diseases

Vaccines have been invaluable for global health, saving lives and reducing healthcare costs, while also raising the quality of human life. However, newly emerging infectious diseases (EID) and more well-established tropical disease pathogens present complex challenges to vaccine developers; in particular, neglected tropical diseases, which are most prevalent among the world's poorest, include many pathogens with large sizes, multistage life cycles and a variety of nonhuman vectors. EID such as MERS-CoV and H7N9 are highly pathogenic for humans. For many of these pathogens, while their genomes are available, immune correlates of protection are currently unknown. These complexities make developing vaccines for EID and neglected tropical diseases all the more difficult. In this review, we describe the implementation of an immunoinformatics-driven approach to systematically search for key determinants of immunity in newly available genome sequence data and design vaccines. This approach holds promise for the development of 21st century vaccines, improving human health everywhere.

Models for evaluation of relative immunogenic potential of protein particles in biopharmaceutical protein formulations

An immune response to a therapeutic protein that compromises the biopharmaceutical activity or cross-reacts with an endogenous protein is a serious clinical event. The role of protein aggregates and particles in biopharmaceutical formulations in mediating this immune response has gained considerable attention over the recent past. Model systems that could consistently and reliably predict the relative immunogenicity of biopharmaceutical protein formulations would be extremely valuable. Several approaches have been developed in an attempt to provide this insight, including in silico algorithms, in vitro tests utilizing human leukocytes and in vivo animal models. This commentary provides an update of these various approaches as well as the author's perspectives on the pros and cons of these different methods.

CTLs' repertoire shaping in the thymus: a Monte Carlo simulation

MOTIVATION

The human immune system evolved a multi-layered control mechanism to eliminate self-reactive cells. Of these so-called tolerance induction mechanisms, lymphocytes T education in the thymus gland represents the very first one. This complicated process is not fully understood and quantitative models able to help in this endeavor are lacking. Here, we present a stochastic computational model of the thymus which combines data-driven prediction methods and a novel method based on protein-protein potential measurements for assessing molecular binding among cell receptors, major histocompatibility complex (MHC) molecules, and self-peptides.

RESULTS

Of all possible specificities of immature T cells entering the thymus, only a small fraction is actually selected for maturation. Monte Carlo simulations of thymocytes selection in the thymus are performed varying the size of the self and a parameter determining the number of encounter with antigen-presenting cells (APCs). We score the fraction of self-reacting thymocytes leaving the thymus as mature naive T cells and show that self-reactivity is only marginally dependent on the number of self-molecules presented by APCs, while it is strongly affected by a parameter proportional to the time spent in the thymus. We study how this measure changes when we vary the number of MHC alleles and found an optimal number not too different from what we have in reality. The main result of this study is more methodological than biological as we show that immunoinformatics data and methods can be used in systemic level simulation of immune processes.

Critical role of glycosylation in determining the length and structure of T cell epitopes

Background

Using a combined in silico approach, we investigated the glycosylation of T cell epitopes and autoantigens. The present systems biology analysis was made possible by currently available databases (representing full proteomes, known human T cell epitopes and autoantigens) as well as glycosylation prediction tools.

Results

We analyzed the probable glycosylation of human T cell epitope sequences extracted from the ImmuneEpitope Database. Our analysis suggests that in contrast to full length SwissProt entries, only a minimal portion of experimentally verified T cell epitopes is potentially N- or O-glycosylated (2.26% and 1.22%, respectively). Bayesian analysis of entries extracted from the Autoantigen Database suggests a correlation between N-glycosylation and autoantigenicity. The analysis of random generated sequences shows that glycosylation probability is also affected by peptide length. Our data suggest that the lack of peptide glycosylation, a feature that probably favors effective recognition by T cells, might have resulted in a selective advantage for short peptides to become T cell epitopes. The length of T cell epitopes is at the intersection of curves determining specificity and glycosylation probability. Thus, the range of length of naturally occurring T cell epitopes may ensure the maximum specificity with the minimal glycosylation probability.

Conclusion

The findings of this bioinformatical approach shed light on fundamental factors that might have shaped adaptive immunity during evolution. Our data suggest that amino acid sequence-based hypo/non-glycosylation of certain segments of proteins might be substantial for determining T cell immunity/autoimmunity.

Immunogenetics of drug-induced skin blistering disorders. Part II: Synthesis

The overall immunopathogenesis relevant to a large series of disorders caused by a drug or its associated hyperimmune condition is discussed based upon examining the genetics of severe drug-induced bullous skin problems (sporadic idiosyncratic adverse events including Stevens–Johnson syndrome and Toxic epidermal necrolysis). New results from an exemplar study on shared precipitating and perpetuating inner causes with other related disease phenotypes including aphtous stomatitis, Behçets, erythema multiforme, Hashimoto’s thyroiditis, pemphigus, periodic fevers, Sweet’s syndrome and drug-induced multisystem hypersensitivity are presented. A call for a collaborative, wider demographic profiling and deeper immunotyping in suggested future work is made.

Proteomic toolbox for autoimmunity research

Autoimmune diseases are genetically complex and poorly understood, and may lead to clinically severe consequences including end-organ damage. Given this scenario, early biomarker discovery is becoming increasingly important for early diagnosis and treatment in these diseases. Among the different approaches tried, the application of proteomic analysis of body fluids has great potential as a non-invasive tool for early diagnosis in many different disease settings. During the past 10 years, proteomics-based approaches have made steady inroads into the study of various autoimmune diseases. In this review, we summarize the highlights of various traditional as well as novel proteomic methods, including 2D-MS/MS, multi-dimensional HPLC-MS/MS, CE-MS/MS, SELDI-TOF-MS/MS, iTRAQ and a variety of targeted antibody-based protein arrays, which have been particularly informative in the field of autoimmunity.

Modeling the adaptive immune system: predictions and simulations

MOTIVATION

Immunological bioinformatics methods are applicable to a broad range of scientific areas. The specifics of how and where they might be implemented have recently been reviewed in the literature. However, the background and concerns for selecting between the different available methods have so far not been adequately covered.

SUMMARY

Before using predictions systems, it is necessary to not only understand how the methods are constructed but also their strength and limitations. The prediction systems in humoral epitope discovery are still in their infancy, but have reached a reasonable level of predictive strength. In cellular immunology, MHC class I binding predictions are now very strong and cover most of the known HLA specificities. These systems work well for epitope discovery, and predictions of the MHC class I pathway have been further improved by integration with state-of-the-art prediction tools for proteasomal cleavage and TAP binding. By comparison, class II MHC binding predictions have not developed to a comparable accuracy level, but new tools have emerged that deliver significantly improved predictions not only in terms of accuracy, but also in MHC specificity coverage. Simulation systems and mathematical modeling are also now beginning to reach a level where these methods will be able to answer more complex immunological questions.

Immunoinformatics: an overview of computational tools and techniques for understanding immune function

In recent years, there has been a rapid expansion in the application of information technology to biological data. Although the use of information science techniques is less common for the discipline of immunology, this field has seen great strides in recent years. This review addresses why in silico modeling is needed in immunology research, highlights some of the major areas of research and suggests what may be important for the future of immunoinformatics.