Immunomics

Several types of microarrays have been created to specifically observe the immune system response and interactions. Antibody microarrays use antibodies as probes and antigens as targets. They can be used to directly measure the antigen concentrations for which the antibody probes are specific. Peptide microarrays use antigen peptides as probes and serum antibodies as targets. These can be used for functional immunomic applications to the understanding of autoimmune diseases and allergies, definition of B-cell epitopes, vaccine studies, detection assays, and analysis of antibody specificity. MHC microarrays are the most recent development in immunomic arrays and use peptide-MHC complexes and their co-stimulatory molecules as probes and T-cell populations as targets. Bound T-cells are activated and secrete cytokines, which are captured by specific detection antibodies. This microarray can map MHC-restricted T cell epitopes.[7]

The Lymphochip: A specialized cDNA microarray

The Lymphochip is a specialized human cDNA microarray enriched for genes related to immune function and created by Ash Alizadeh at Stanford University. 17,853 cDNA clones were taken from three sources. The first set of clones were selected if identified expressed sequence tags (ESTs) were unique or enriched specifically in lymphoid cDNA libraries; these represent ~80% of the Lymphochip clones. The second set of clones was identified during first-generation microarray analysis of immune responses. Finally, 3,183 genes that are known or suspected to have roles in immune function, oncogenesis, apoptosis, cell proliferation, or being open reading frames from pathogenic human viruses were used on the Lymphochip. New genes are frequently being added.

Epitope mapping identifies the sites of antibodies to which their target antigens bind. In the past, scientists would have to isolate antigens, digest them into smaller fragments, and determine which of these fragments stimulated T- and B- cell responses to define an antibody's epitope. Immunomics harnesses the power of bioinformatics and offers mapping algorithms that accelerate the discovery of epitope sequences. These algorithms are relevant to vaccine design and for characterizing and modifying immune responses in the context of autoimmunity, endocrinology, allergy, transplantation, diagnostics and engineering of therapeutic proteins.

T-cell and B-cell epitope mapping algorithms can computationally predict epitopes based on the genomic sequence of pathogens, without prior knowledge of a protein's structure or function. A series of steps are used to identify epitopes:

1. Comparison between virulent and avirulent organisms identify candidate genes that code for epitopes that solicit T-cell responses by looking for sequences that are unique to virulent strains. Additionally, differential microarray technologies can discover pathogen-specific genes that are upregulated during host-interaction and may be relevant for analysis because they are critical to the function of the pathogen.
2. Immunoinformatics tools predict regions of these candidate genes that interact with T cells by scanning genome-derived protein sequences of a pathogen.
3. These predicted peptides are synthesized and used in in vitro screening against T cells. Recognizing a positive immune response can suggest that this peptide contains an epitope that stimulates immune response in the course of natural infection or disease.[8]

• EpiMatrix
• TEPITOPE
• Multipred
• MHC Thread
• MHCPred
• NetMHC
• LpPep
• BIMAS

The guiding principle behind flow cytometry is that cells or subcellular particles are tagged with fluorescent probes are passed through a laser beam and sorted by the strength of fluorescence emitted by cells contained in the droplets. MHC [[tetramer staining]] by flow cytometry identifies and isolates specific T cells based on the binding specificity of their cell surface receptors with fluorescently-tagged MHC-peptide complexes.[9]

ELISPOT is a modified version of the ELISA immunoassay and is a common method of monitoring immune responses.

Microarrays have discovered gene expression patterns that correlate with antigen-induced activation or anergy in B lymphocytes. Lymphocyte anergy pathways involve induction of some, but not all of the signaling pathways used during lymphocyte activation. For example, NFAT and MAPK/ERK kinase pathways are expressed in anergic (or “tolerant) cell lines, whereas NF-kB and c-Jun N-terminal kinases pathways are not. Of the 300 genes that were altered in expression after antigen-stimulated naïve B cells, only 8 of these genes were regulated in tolerant B cells. Understanding these “tolerance” pathways have important implications for designing immunosuppressive drugs. These gene expression signatures of tolerant B cells could be used during drug screens to probe for compounds that mimic the functional effects of natural tolerance.[10]

Gene expression profiles during human lymphocyte differentiation has followed mature, naïve B cells from their resting state through germinal center reactions and into terminal differentiation. These studies have shown that germinal center B cells represent a distinct stage in differentiation because the gene expression profile is different from activated peripheral B cells. Although no in vitro culture system has been able to induce resting peripheral B cells to adopt a full germinal center phenotype, these gene expression profiles can be used to measure the success of in vitro cultures in mimicking the germinal center state as they are being developed.[11]

About 9 of every 10 human lymphoid cancers derive from B cells. Distinct immunome-wide expression patterns in a large number of diffuse large cell lymphoma (DLCL)– the most common form of non-Hodgkin's lymphoma – have identified at least two different subtypes in what was previously thought to be a single disease. One subset of these DLCLs shows a similar gene expression pattern to that of normal germinal center B cells and implies that the tumor cell originated from a germinal center B cell. Other surveys of B cell malignancies show that follicular lymphomas share expression features with germinal center B cells, whereas chronic lymphocytic leukemia cells resemble resting peripheral blood lymphocytes. Furthermore, heterogeneity in each of these cell lines also suggest that different subtypes exist within each type of lymphoma, just as it has been shown in DLCL. Such knowledge can be used to direct patients to the most appropriate therapy.[12]

Microarrays have analyzed global responses of macrophages to different microorganisms and have confirmed that these responses sustain and control inflammatory processes, and also kill microorganisms. These independent studies have been able to better describe how macrophages mount attacks against different microorganisms. A “core transcriptional response” was observed to induce 132 genes and repress 59 genes. Induced genes include pro-inflammatory chemokines and cytokines, and their respective receptors. A “pathogen-specific response” was also observed.[13]

Dendritic cells (DCs) help macrophages sustain inflammatory processes and participate in the innate immune system response, but can also prime adaptive immunity. Gene expression analyses have shown that DCs can “multi-task” by temporally segregating their different functions. Soon after recognizing an infectious agent, immature DCs transition to a state of early activation via a core response characterized by rapid downregulation of genes involved with pathogen recognition and phagocytosis, upregulation of cytokine and chemokine genes to recruit other immune cells to the side of inflammations; and expression of genes that control migratory capacity. Early activated DCs are enabled to migrate from non-lymphoid tissues to lymph nodes, where they can prime T-cell responses. These early DCs responses are related to innate immunity and consist of the “core transcriptional response” of DCs. Pathogen-specific responses have a stronger influence on the DC's ability to regulate adaptive immunity.

Comparing distinctions between immune cells’ overall transcriptional program can generate plots that position each cell type to best reflect its expression profile relative to all other cells and can reveal interesting relationships between cell types. For example, the transcriptional profiles from thymic medullary epithelial immune cells mapped closer to lymphocytes than to other epithelia. This can suggest that a functional interaction exists between these two cells type and requires the sharing of particular transcripts and proteins. When comparing gene expression profiles from cells of the blood system, T-cell and B-cell subsets tightly group with their respective cell types.

By looking at the transcriptional profile of different T-cells, scientists have shown that natural killer T-cells are a close variant of conventional CD4+ T cells, rather than an intermediary cell-type between T cells and natural killer cells. Additionally, DCs, natural killer cells, and B cells are tightly grouped based on their global expression profiles. It may have been expected that B lymphocytes and T lymphocytes would cluster separately from each other, or that natural killer cells would be more closely related to T cells because they share common precursors, cytolytic activity, and similar activation markers. Therefore, immunomics has established relationship between cell lineages that depart from classical views. Additionally, it may better explain the observed plasticity in lymphoid and myeloid cell differentiation because of the considerable overlap between global expression profiles of these different lineages.[14]

Networks represent the broadest level of genetic interactions and aim to link all genes and transcripts in the immunological genome. Cellular phenotypes and differentiation states are ultimately established by the activity of these networks of co-regulated genes. One of the most complete networks in immunology has deciphered regulatory connections among normal and transformed human B cells. This analysis suggests a hierarchical network where a small number of highly connected genes (called “hubs”) regulated most interactions. Proto-oncogene MYC emerged as a major hub and highly influential regulator for B cells. Notably, MYC was found to directly control BYSL, a highly conserved, but poorly characterized gene, and is the largest hub in the whole B cell network. This suggests that BYSL encodes an important cellular molecule and a critical effecter of MYC function, and motivates additional studies to elucidate its function. Therefore, using gene expression data to create networks can reveal genes highly influential in immune cell differentiation that pre-genomic technologies had not yet identified.[14]

As quoted by Stefania Bambini and Rino Rappuoli, “New powerful genomics technologies have increased the number of disease that can be addressed by vaccination, and decreased the time for discover research and vaccine development.” The availability of complete genome sequences of pathogens in combination with high-throughput genomics technologies have helped to accelerate vaccine development. Reverse vaccinology uses genomic sequences of viral, bacterial, or parasitic pathogens to identify genes potentially encoding genes that promote pathogenesis.[15] The first application of reverse vaccinology identified vaccine candidates against Neisseria meningitidis serogroup B. Computational tools identified 600 putative surface-exposed or secreted proteins from the complete genome sequence of a MenB pathogenic strain, on the basis of sequence features. These putative proteins were expressed in E. coli, purified, and used to immunize mice. Tests using mice immune sera estimated the ability of antibodies to protect against these proteins. The proteins able to solicit a robust immune response were checked for sequence conservation across a panel of meningitides strains and allowed for further selection of antigen able to elicit an immune response against most strains in the panel. On the basis of these antigen sequences, scientists have been able to develop a universal “cocktail” vaccine against Neisseria meninitidis that uses five antigens to promote immunity.[16] Similar approaches have been used for a variety of other human pathogens, such as Streptococcus pneumoniae, Chlamydia pneumoniae, Bacillus anthracis, Porphyromonas gingivalis, Mycobacterium tuberculosis, Helicobacter pylori, amongst others. Additionally, studies have started for the development of vaccines against viruses.

The inventory of receptors and signal transduction pathways that immune cells use to monitor and defend the body gives rise to signature patterns of altered gene expression in peripheral blood cells that reflect the character of the infection or injury. Therefore, recognizing characteristic expression profiles of peripheral blood cells may be a powerful diagnostic tool by recruiting these cells as “spies” to detect occult diseases or agents that cannot be readily cultured from the host.

For example, cytomegalovirus (CMV) infection of fibroblasts and HTLV-I infection of T lymphocytes revealed distinct gene expression profiles. CMV infection provoked a unique interferon response whereas HTLV-1 infection induced NF-kB target genes. A type of white blood cells have also been tested again bacterial exposures and immunome expression varied based on the type of bacterial strain used.

Monitoring the change of peripheral blood gene expression can also help determine the course of infection and help treat patients with a therapy tailored to their disease stage. This approach has already been used against sepsis – a disease that progresses through a predictable line of events. Changes gene expression signatures may precede clinical exacerbation of symptoms, like in multiple sclerosis, and allow physicians to nip these “flare-ups” in the bud.[1]