Immunology research has been transformed in the post-genomics era, with high throughput molecular biology and information technologies taking an increasingly central role. This has led to the development of a new area of science termed "Immunomics", that encompasses genomic, high throughput and bioinformatic approaches to immunology. In recognition of the increasing importance of this field, Immunome Research is a new Open Access, online journal, that will publish cutting edge research across the field of Immunomics. Immunome Research will publish a wide range of article types including specialty immunology databases, immunology database tools, immunome epitope research, epitope analysis tools, high-throughput technologies (gene sequencing, microarrays, proteomics), white papers, mathematical and theoretical models, and prediction tools. Immunome Research is the official journal of the International Immunomics Society (IIMMS).
The development of DNA microarray technology a decade ago led to the establishment of functional genomics as one of the most active and successful scientific disciplines today. With the ongoing development of immunomic microarray technology—a spatially addressable, large-scale technology for measurement of specific immunological response—the new challenge of functional immunomics is emerging, which bears similarities to but is also significantly different from functional genomics. Immunonic data has been successfully used to identify biological markers involved in autoimmune diseases, allergies, viral infections such as human immunodeficiency virus (HIV), influenza, diabetes, and responses to cancer vaccines. This review intends to provide a coherent vision of this nascent scientific field, and speculate on future research directions. We discuss at some length issues such as epitope prediction, immunomic microarray technology and its applications, and computation and statistical challenges related to functional immunomics. Based on the recent discovery of regulation mechanisms in T cell responses, we envision the use of immunomic microarrays as a tool for advances in systems biology of cellular immune responses, by means of immunomic regulatory network models.
Predicting the T-cell-mediated immune response is an important task in vaccine design and thus one of the key problems in computational immunomics. Various methods have been developed during the last decade and are available online. We present EpiToolKit, a web server that has been specifically designed to offer a problem-solving environment for computational immunomics. EpiToolKit offers a variety of different prediction methods for major histocompatibility complex class I and II ligands as well as minor histocompatibility antigens. These predictions are embedded in a user-friendly interface allowing refining, editing and constraining the searches conveniently. We illustrate the value of the approach with a set of novel tumor-associated peptides. EpiToolKit is available online at www.epitoolkit.org.
Immunomics research uses in silico epitope prediction, as well as in vivo and in vitro approaches. We inoculated BALB/c (H2d) mice with 17DD yellow fever vaccine to investigate the correlations between approaches used for epitope discovery: ELISPOT assays, binding assays, and prediction software. Our results showed a good agreement between ELISPOT and binding assays, which seemed to correlate with the protein immunogenicity. PREDBALB/c prediction software partially agreed with the ELISPOT and binding assay results, but presented low specificity. The use of prediction software to exclude peptides containing no epitopes, followed by high throughput screening of the remaining peptides by ELISPOT, and the use of MHC-biding assays to characterize the MHC restrictions demonstrated to be an efficient strategy. The results allowed the characterization of 2 MHC class I and 17 class II epitopes in the envelope protein of the YF virus in BALB/c (H2d) mice.
It can be argued that the arrival of the “genomics era” has significantly shifted the paradigm of vaccine and therapeutics development from microbiological to sequence-based approaches. Genome sequences provide a previously unattainable route to investigate the mechanisms that underpin pathogenesis. Genomics, transcriptomics, metabolomics, structural genomics, proteomics, and immunomics are being exploited to perfect the identification of targets, to design new vaccines and drugs, and to predict their effects in patients. Furthermore, human genomics and related studies are providing insights into aspects of host biology that are important in infectious disease. This ever-growing body of genomic data and new genome-based approaches will play a critical role in the future to enable timely development of vaccines and therapeutics to control emerging infectious diseases.
The recent publication of the Schistosoma japonicum and S. mansoni genomes has expanded greatly the opportunities for post-genomic schistosomiasis vaccine research. Immunomics protein microarrays provide an excellent application of this new schistosome sequence information, having been utilised successfully for vaccine antigen discovery with a range of bacterial and viral pathogens, and malaria.
Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction...
The Plasmodium parasite, the causative agent of malaria, is an excellent model for immunomic-based approaches to vaccine development. The Plasmodium parasite has a complex life cycle with multiple stages and stage-specific expression of ~ 5,300 putative proteins. No malaria vaccine has yet been licensed. Many believe that an effective vaccine will need to target several antigens and multiple stages, and will require the generation of both antibody and cellular immune responses. Vaccine efforts to date have been stage-specific and based on only a very limited number of proteins representing < 0.5% of the genome. The recent availability of comprehensive genomic, proteomic and transcriptomic datasets from human and selected non-human primate and rodent malarias provide a foundation to exploit for vaccine development. This information can be mined to identify promising vaccine candidate antigens, by proteome-wide screening of antibody and T cell reactivity using specimens from individuals exposed to malaria and technology platforms such as protein arrays, high throughput protein production and epitope prediction algorithms. Such antigens could be incorporated into a rational vaccine development process that targets specific stages of the Plasmodium parasite life cycle with immune responses implicated in parasite elimination and control. Immunomic approaches which enable the selection of the best possible targets by prioritizing antigens according to clinically relevant criteria may overcome the problem of poorly immunogenic...
The origin of the recombination-activating genes (RAGs) is considered to be a foundation hallmark for adaptive immunity, characterised by the presence of antigen receptor genes that provide the ability to recognise and respond to specific peptide antigens. In vertebrates, a diverse repertoire of antigen-specific receptors, T cell receptors and immunoglobulins is generated by V(D)J recombination performed by the RAG-1 and RAG-2 protein complex. RAG homologues were identified in many jawed vertebrates. Despite their crucial importance, no homologues have been found in jawless vertebrates and invertebrates. This paper focuses on the RAG homologues in humans and other vertebrates for which the genome is completely sequenced, and also discuses the main contribution of the use of RAG homologues in phylogenetics and vertebrate evolution. Since mutations in both genes cause a spectrum of severe combined immunodeficiencies, including the Omenn syndrome (OS), these topics are discussed in detail. Finally, the relevance to genomic diversity and implications to immunomics are addressed. The search for homologues could enlighten us about the evolutionary processes that shaped the adaptive immune system. Understanding the diversity of the adaptive immune system is crucially important for the design and development of new therapies to modulate the immune responses in humans and/or animal models.
This review will focus on the elements of the skin’s immune system, immune cells and/or non-immune cells that support immune mechanisms, molecules with immune origin and/or immune functions that are involved in skin
carcinogenesis. All these immune elements are compulsory in the development of skin tumors and/or sustainability of the neoplastic process. In this light, recent data gathered in this review will acknowledge all immune elements that contribute to skin tumorigenesis; moreover, they can serve as immune biomarkers. These immune markers can contribute to the
diagnostic improvement, prognosis forecast, therapy monitoring, and even personalized therapeutical approach in skin cancer. Immune processes that sustain tumorigenesis in non-melanoma and melanoma skin cancers are described in the framework of recent data.
Schistosomiasis is a neglected tropical disease that is responsible for almost 300,000 deaths annually. Mass drug administration (MDA) is used worldwide for the control of schistosomiasis, but chemotherapy fails to prevent reinfection with schistosomes, so MDA alone is not sufficient to eliminate the disease, and a prophylactic vaccine is required. Herein, we take advantage of recent advances in systems biology and longitudinal studies in schistosomiasis endemic areas in Brazil to pilot an immunomics approach to the discovery of schistosomiasis vaccine antigens. We selected mostly surface-derived proteins, produced them using an in vitro rapid translation system and then printed them to generate the first protein microarray for a multi-cellular pathogen. Using well-established Brazilian cohorts of putatively resistant (PR) and chronically infected (CI) individuals stratified by the intensity of their S. mansoni infection, we probed arrays for IgG subclass and IgE responses to these antigens to detect antibody signatures that were reflective of protective vs. non-protective immune responses. Moreover, probing for IgE responses allowed us to identify antigens that might induce potentially deleterious hypersensitivity responses if used as subunit vaccines in endemic populations. Using multi-dimensional cluster analysis we showed that PR individuals mounted a distinct and robust IgG1 response to a small set of newly discovered and well-characterized surface (tegument) antigens in contrast to CI individuals who mounted strong IgE and IgG4 responses to many antigens. Herein...
Visceral leishmaniasis (VL) or kala-azar, a vector-borne protozoan disease, shows endemicity in larger areas of the tropical, subtropical and the Mediterranean countries. WHO report suggested that an annual incidence of VL is nearly 200,000 to 400,000 cases, resulting in 20,000 to 30,000 deaths per year. Treatment with available anti-leishmanial drugs are not cost effective, with varied efficacies and higher relapse rate, which poses a major challenge to current kala-azar control program in Indian subcontinent. Therefore, a vaccine against VL is imperative and knowing the fact that recovered individuals developed lifelong immunity against re-infection, it is feasible. Vaccine development program, though time taking, has recently gained momentum with the emergence of omic era, i.e., from genomics to immunomics. Classical as well as molecular methodologies have been overtaken with alternative strategies wherein proteomics based knowledge combined with computational techniques (immunoinformatics) speed up the identification and detailed characterization of new antigens for potential vaccine candidates. This may eventually help in the designing of polyvalent synthetic and recombinant chimeric vaccines as an effective intervention measures to control the disease in endemic areas. This review focuses on such newer approaches being utilized for vaccine development against VL.
Cancer is one of the four major non-communicable diseases (NCD), responsible for ~14.6% of all human deaths. Currently, there are >100 different known types of cancer and >500 genes involved in cancer. Ongoing research efforts have been focused on cancer etiology and therapy. As a result, there is an exponential growth of cancer-associated data from diverse resources, such as scientific publications, genome-wide association studies, gene expression experiments, gene-gene or protein-protein interaction data, enzymatic assays, epigenomics, immunomics and cytogenetics, stored in relevant repositories. These data are complex and heterogeneous, ranging from unprocessed, unstructured data in the form of raw sequences and polymorphisms to well-annotated, structured data. Consequently, the storage, mining, retrieval and analysis of these data in an efficient and meaningful manner pose a major challenge to biomedical investigators. In the current review, we present the central, publicly accessible databases that contain data pertinent to cancer, the resources available for delivering and analyzing information from these databases, as well as databases dedicated to specific types of cancer. Examples for this wealth of cancer-related information and bioinformatic tools have also been provided.
Schistosoma haematobium affects more than 100 million people throughout Africa and is the causative agent of urogenital schistosomiasis. The parasite is strongly associated with urothelial cancer in infected individuals and as such is designated a group I carcinogen by the International Agency for Research on Cancer. Using a protein microarray containing schistosome proteins, we sought to identify antigens that were the targets of protective IgG1 immune responses in S. haematobium-exposed individuals that acquire drug-induced resistance (DIR) to schistosomiasis after praziquantel treatment. Numerous antigens with known vaccine potential were identified, including calpain (Smp80), tetraspanins, glutathione-S-transferases, and glucose transporters (SGTP1), as well as previously uncharacterized proteins. Reactive IgG1 responses were not elevated in exposed individuals who did not acquire DIR. To complement our human subjects study, we screened for antigen targets of rhesus macaques rendered resistant to S. japonicum by experimental infection followed by self-cure, and discovered a number of new and known vaccine targets, including major targets recognized by our human subjects. This study has further validated the immunomics-based approach to schistosomiasis vaccine antigen discovery and identified numerous novel potential vaccine antigens.
Antigen-specific immune responses against peptides derived from missense gene mutations have been identified in multiple cancers. The application of personalized peptide vaccines based on the tumor mutation repertoire of each cancer patient is a near-term clinical reality. These peptides can be identified for pre-validation by leveraging the results of massive gene sequencing efforts in cancer. In this study, we utilized NetMHC 3.2 to predict nanomolar peptide binding affinity to 57 human HLA-A and B alleles. All peptides were derived from 5,685 missense mutations in 312 genes annotated as functionally relevant in the Cancer Genome Project. Of the 26,672,189 potential 8–11 mer peptide-HLA pairs evaluated, 0.4% (127,800) display binding affinities < 50 nM, predicting high affinity interactions. These peptides can be segregated into two groups based on the binding affinity to HLA proteins relative to germline-encoded sequences: peptides for which both the mutant and wild-type forms are high affinity binders, and peptides for which only the mutant form is a high affinity binder. Current evidence directs the attention to mutations that increase HLA binding affinity, as compared with cognate wild-type peptide sequences, as these potentially are more relevant for vaccine development from a clinical perspective. Our analysis generated a database including all predicted HLA binding peptides and the corresponding change in binding affinity as a result of point mutations. Our study constitutes a broad foundation for the development of personalized peptide vaccines that hone-in on functionally relevant targets in multiple cancers in individuals with diverse HLA haplotypes.
Biology is a large application area for machine learning techniques. Applications range from gene start prediction over prediction of drug activity to the prediction of the three-dimensional structure of proteins. This thesis deals with kernel-based machine learning in proteomics and immunomics applications. In all applications, we are interested in predicting properties of peptides, which are parts of proteins. These peptides play an important role in many biological systems.
In the first part, we introduce a new kernel which can be used together with a support vector machine for predicting chromatographic separation of peptides in reversed-phase liquid chromatography and strong anion exchange solid-phase extraction. The predictor for reversed-phase liquid chromatography can be used to build a p-value-based filter for identifications in proteomics. The filter is based on the idea that if the measured and the predicted behavior differ significantly, the identified sequence is probably wrong. In this way, we can filter out false identifications. First, this is useful for increasing the precision of identifications. Second, one can lower mass spectrometric scoring thresholds and filter out false identifications to get a significant increase in the number of correctly identified spectra at comparable precision. We also show in the following section that we can extend our method to predict retention times in two-dimensional chromatographic separations...
Complete characterization of antibody specificities associated to natural infections is expected to provide a rich source of serologic biomarkers with potential applications in molecular diagnosis, follow-up of chemotherapeutic treatments, and prioritization of targets for vaccine development. Here, we developed a highly-multiplexed platform based on next-generation high-density peptide microarrays to map these specificities in Chagas Disease, an exemplar of a human infectious disease caused by the protozoan Trypanosoma cruzi. We designed a high-density peptide microarray containing more than 175,000 overlapping 15mer peptides derived from T. cruzi proteins. Peptides were synthesized in situ on microarray slides, spanning the complete length of 457 parasite proteins with fully overlapped 15mers (1 residue shift). Screening of these slides with antibodies purified from infected patients and healthy donors demonstrated both a high technical reproducibility as well as epitope mapping consistency when compared with earlier low-throughput technologies. Using a conservative signal threshold to classify positive (reactive) peptides we identified 2,031 disease-specific peptides and 97 novel parasite antigens, effectively doubling the number of known antigens and providing a 10-fold increase in the number of fine mapped antigenic determinants for this disease. Finally...