Immunomics
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Immunomics is the study of
Traditionally, scientists studying the immune system have had to search for
Immunomics has made this approach easier by its ability to look at the immune system as a whole and characterize it as a dynamic model. It has revealed that some of the immune system's most distinguishing features are the continuous motility, turnover, and plasticity of its constituent cells. In addition, current genomic technologies, like
Definition
A host's immune system responds to pathogen invasion by a set of pathogen-specific responses in which many “players” participate; these include
History
Limited by available microarrays and a non-complete human genome at this point in time, this same set of researchers were motivated to create a specialized microarray that focused on genes preferentially expressed in a given cell type, or known to be functionally important in a given biological process. As a result, Alizadeh and colleagues designed the “Lymphochip” cDNA microarray, which contained 13,000 genes and was enriched for genes of importance to the immune system.[4]
Iyer et al.’s 1999 article was another to reveal the importance of applying genomic technologies to immunological research. Although not intending to address any aspect of immunity at the start of their experiment, these researchers observed that the expression profiles of
In 2006, Moutaftsi et al. demonstrated that epitope-mapping tools could accurately identify the epitopes responsible for 95% of the murine T-cell response to
Technologies used
Immunomic microarrays
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]
Lymphochip
The Lymphochip is a specialized human cDNA microarray enriched for genes related to immune function and created by
T- and- B-cell-epitope mapping tools
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:
- 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.
- Immunoinformaticstools predict regions of these candidate genes that interact with T cells by scanning genome-derived protein sequences of a pathogen.
- 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]
Available mapping tools
- EpiMatrix
- TEPITOPE
- Multipred
- MHC Thread
- MHCPred
- NetMHC
- LpPep
- BIMAS
Tetramer staining by flow cytometry
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
Contributions to understanding the immune system
Immunomics has made a considerable impact on the understanding of the immune system by uncovering differences in gene expression profiles of cell types, characterizing immune response, illuminating immune cell lineages and relationship, and establishing gene regulatory networks. Whereas the following list of contributions is not complete, it is meant to demonstrate the broad application of immunomic research and powerful consequences on immunology.
Immune cell activation and differentiation
B lymphocyte anergy
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,
Lymphocyte differentiation
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]
Lymphoid malignancies
About 9 of every 10 human lymphoid cancers derive from B cells. Distinct immunome-wide expression patterns in a large number of
Immune response
Macrophage responses to bacteria
Microarrays have analyzed global responses of
Dendritic response to pathogen
Distinguishing immune cell types
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
Immune cell regulatory networks
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]
Practical applications
Vaccine development
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.
Disease diagnosis
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,
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]
Immunological genome project
The immune system is a network of genetic and signaling pathways connected by a network of interacting cells. The
By 2008, the ImmGen project involved seven immunology and three computational biology laboratories across the United States and over 200 cell populations involved in the immune system had been identified and described. This consortium has created a data browser to explore the expression patterns of particular genes, networks of co-regulated genes, and genes that can reliably distinguish cell types. Raw data is also accessible from the NCBI's Gene Expression Omnibus.[17][18]
Databases
- Immune Response in silico (IRIS)
- Reference Database of Immune Cells
- Immunological Genome Project
- Immune Epitope Database and Analysis Resource (IEDB)
- IMGT
- SYFPEiTHi
- AniJen
- MHCBN
- IPD
- Epitome
- Allergome
See also
References
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- ^ De Groot AS, Martin W (2003). “From immunome to vaccine: epitope mapping and vaccine design tools.” Immunoinformatics: Bioinformatic Strategies for Better Understanding of Immune Function. Wiley, Chichester. Novartis Foundation Symposium 254, 57-76.[1]
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- ^ S2CID 10688314.
- PMID 19150507.
- PMID 10710308.
- ^ The Immunological Genome Project
- ^ NCBI Gene Expression Omnibus
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