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specific genes identified from a compendium of microarray expression data
Immune response in silico (IRIS): immune specific genes identified from a compendium of microarray expression data
A R Abbas1, D Baldwin1, Y Ma1, W Ouyang2, A Gurney2,3, F Martin2, S Fong2, M van Lookeren Campagne2, P Godowski2, P M Williams3, A C Chan2 and H F Clark1,2
1Department of Bioinformatics, Genentech, Inc., South San Francisco, CA, USA2Department of Immunology, Genentech, Inc., South San Francisco, CA, USA3Department of Molecular Biology, Genentech, Inc., South San Francisco, CA, USACorrespondence: Dr HF Clark, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA.
Top of pageAbstractImmune cell specific expression is one indication of the importance of a gene's role in the immune response. We have compiled a compendium of microarray expression data for virtually all human genes from six key immune cell types and their activated and differentiated states. Immune Response In Silico (IRIS) is a collection of genes that have been selected for specific expression in immune cells. The expression pattern of IRIS genes recapitulates the phylogeny of immune cells in terms of the lineages of their differentiation. Gene Ontology assignments for IRIS genes reveal significant involvement in inflammation and immunity. Genes encoding CD antigens, cytokines, integrins and many other gene families playing key roles in the immune response are highly represented. IRIS also includes proteins of unknown function and expressed sequence tags that may not represent genes. The predicted cellular localization of IRIS proteins is evenly distributed between cell surface and intracellular compartments, indicating that immune specificity is important at many points in the signaling pathways of the immune fake van cleef & arpels clover diamond ring response. This enables the immune response to attack foreign invaders while recognizing and tolerating self antigens. Two distinct lineages of immune cells have evolved with cell types that specialize in each of the many roles that are required for this immune response. The myeloid lineage of monocytes, macrophages, dendritic cells and neutrophils carries out the innate immune response, recognizing microbial pathogens typically by carbohydrates found only in bacterial proteins. The lymphoid lineage of T cells, B cells and natural killer (NK) cells enables adaptive immunity by distinguishing self from non self antigens and also providing a memory of foreign proteins seen before. Other immune cells, such as eosinophils, basophils and mast cells, also participate in the immune response. Although a large repertoire of genes that play key roles in the differentiation, function and regulation of these immune cells is already well described, this has not yet led to a complete understanding of immune diseases.
Genome wide microarray expression profiling of immune cells provides opportunities for identifying other genes that may function in the immune response. Microarray experiments have been carried out on various immune cell subsets by a number of different investigators in order to understand gene expression differences during differentiation and activation.1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 These studies have provided invaluable insight into comprehensive gene expression profiles that define many different immune cell subsets and states of differentiation and activation. In particular, a recent study detailed elegantly the genes expressed specifically in many subsets of the T cell lineage.18 Comparison of gene expression profiles across a broad range of immune cell types and nonimmune tissues is necessary, however, to fully appreciate which gene expression differences are unique to each immune cell subset, which are found in multiple subsets or across immune cell lineages, and which are found more widely across the many cell types in the human body and thus may be involved in more general cellular processes. Differences among microarray platforms and experimental protocols used by different investigators often confound such comparison. Here, a compendium of microarray expression data from a broad representation of isolated immune cell subsets has been generated on the same microarray platform and analysis of gene expression profile across the major cell types of the immune system is made possible. Moreover, gene expression in all other major tissues allows determination of immune specific expression. Traditional methods of determining expression are performed on a gene by gene basis, and single microarray experiments show differential expression between just a few immune cell subsets. Here, genes fitting a complex expression criterion are identified on a genomic scale.
Immune cell specific expression is one indication of the importance of a gene's role in the function of the immune system. 'Cluster of differentiation' (CD) antigens are used experimentally as specific markers for immune cell subsets and they often play a key role in the function of that cell. For example, the T cell receptor is expressed only on T lymphocytes and is responsible for self antigen recognition, a primary function of this immune cell.19 Here, immune cell specifically expressed genes are identified by a survey of expression profiles across all the immune cells and major non immune tissues by a method termed Immune Response In Silico (IRIS). Immune cell specificity is determined generally by higher expression in any immune cell than expression in any nonimmune cell tissue. Finer characterization of specificity within an immune cell type, such as T cells, and an immune cell lineage, such as lymphoid cells, is also determined. Furthermore, expression profiles within subsets of an immune cell, such as classes of T cells expressing CD4 or CD8 antigens, memory and helper T cells and resting vs activated cells, further refine the specificity of immune specific genes. IRIS has identified both well characterized immune genes and a number of highly immune specific genes with unknown function.
Top of pageResultsIRIS identifies genes more highly expressed in immune cells than in any of the major organs of the body. Gene expression is surveyed across a compendium of samples, including immune cell subsets (Table 1) and a comprehensive range of normal tissues. The immune cells have been isolated from normal human blood by purifying each cell type via its specific cell surface markers. Activated and differentiated subsets were developed by in vitro stimulations. RNA samples were labeled and run on microarray chips that include probesets for virtually every human gene. Genes are often represented by more than one probeset and the expression levels vary, although the expression profiles are usually consistent. Genes are selected for IRIS based on cutoff values for expression levels in immune vs nonimmune samples, as shown in Supplementary Figure 1. Prior to the determination of these cutoffs, genomewide expression levels were evaluated by surveying the range of expression of all genes and the expression levels of a few families of genes already known to play important roles in the immune system. The highest mean expression level of any immune cell subset within each cell type as defined in Table 1 is calculated for each IRIS gene. Hierarchical clustering, a statistical method for grouping genes by the similarity of their expression profiles, reveals patterns of gene expression that are distinct among immune cell lineages. The dendrogram of the relationships of these genes mimics the evolutionary relationships of the lineages of immune cell types. The heat map showing all IRIS genes also illustrates the complexity of expression profiles, with most genes expressed at some level in more than one immune cell type. The highest mean expression of any cell subset for each probeset represents the expression of that cell type. The dendrograms shows the similarity of expression profiles for genes in the rows and cell types in the columns. Intensity values less than 1000 have the lightest shading and intensity values greater than 10,000 have the darkest shading.
Full figure and legend (166K)
IRIS categoriesWhile clustering is a useful method for identifying patterns of gene expression, it fails to clearly define the parameters of these patterns. Therefore, cutoff values of gene expression signatures in various immune cell types were approximated and used to define the IRIS categories that were observed in the clustering (Table 1) and these parameters are defined in the Cell Lineage Assignment section of Materials and methods. The probesets are categorized by their profiles according to the degree of specificity within immune cells. Profiles specific to one cell type are assigned to the categories T cell, NK cell, B cell, monocyte, dendritic or neutrophil. Profiles specific to more than one cell type within a lineage are assigned to lymphoid or myeloid categories. Profiles specific to cell types across lineages, as well as probesets with expression levels equivalent in all immune cells, are assigned to the multiple category. Genes represented by several probesets may appear in more than one category. A gene is best described as specific to the most exclusive category to which it has been assigned, that is, the highest expressing probeset may be in the T cell category and more weakly expressing probesets for that gene may fall into the lymphoid or multiple categories because of the stringency of cutoff levels used.
Patterns of expression profiles within each lineageCell types within a lineage share some immune specific genes, suggesting that they confer common functions among those cells. The expression profiles are very diverse and complex, but some general patterns emerge. As described above, the IRIS categories attempt to group genes within a cell type, or, if specific to more than one cell type, within a lineage. Here, these categories are assessed for general patterns of expression profiles. K means clustering reveals groups of expression profiles within an IRIS category, as shown in Figure 2. Within the lymphoid category clustered in Figure 2a, T cells and NK cells are shown to share a number of immune fake van cleef ring specific genes (clusters 1, 4 and 6), whereas B cells express more distinct genes (clusters 3 and 5). Often, the genes shared between T and NK cells are specifically expressed in the activated state of both cells (cluster 6). The myeloid category shown in Figure 2b reveals a similar relationship between genes transiently expressed in differentiating macrophages and also in LPS induced dendritic cells (cluster 8). Profile clusters of genes highly specific to single cell types are shown in Supplementary Figure 2. T cells have several general patterns of specificity among subsets of T cells, as shown in Supplementary Figure 2a. CD8 cells have a number of unique genes (cluster 1), and also share some genes with CD4 cells (cluster 2). T helper cells share some specific genes (clusters 3 and 6) and a number of genes appear only in activated memory T cells (cluster 5). B cells also show distinct profiles, as shown in Supplementary Figure 2b. Some genes are expressed specifically in both na and memory B cells (cluster 2), others only in plasma cells (clusters 3 and 4), while there are no specific B cell genes expressed distinctly in na or memory cells alone. Likewise, monocytes and macrophages shown in Supplementary Figure 2c reveal a class of genes specific to monocytes (cluster 1) and another transiently expressed in differentiating macrophages (cluster 2), but none specific to fully differentiated macrophages. However, Figure 2b shows that fully differentiated macrophages share profiles with neutrophils (cluster 2) and dendritic cells (cluster 5). All of these observations provide insights into the differentiation of immune cells as they have evolved to play specific roles in the immune response. The y axis is intensity level.
Full figure and legend (344K)
Statistical significance of IRISThe statistical significance of specificity is assessed for the genes within each IRIS cell type or lineage, as shown in Supplementary Figure 4. The F statistic measures both immune cell variation and sample variation simultaneously, so a low F value can either indicate similar expression of a gene across immune cells, or gene expression variability among samples, but does not distinguish between the two. Conversely, a gene highly specific to one cell type has a high F value, particularly if the replicate samples all have similar expression of that gene. Sample variability occurs because of both incomplete reproducibility of microarray conditions and biological variation between blood donors due to unknown causes. The multiple category has the highest proportion of genes with low F values, suggesting that this category includes most of the genes falsely assigned as immune specific. While the microarray technology used here is a well established experimental method for detecting differential gene expression, independent confirmation by real time quantitative PCR (RT QPCR) is carried out before pursuing further functional studies on genes of interest.20
Gene ontology and gene familiesGenes of known function are assigned a term from Gene Ontology, a structured vocabulary for describing biological process, molecular function and cellular component.21 Table 2 shows the Biological Processes most highly specific to IRIS, meaning that these ontologies are seen in IRIS in higher numbers than in a random set of genes. Many specific responses to different foreign invaders, chemotaxis of immune cells to sites of inflammation and other aspects of the immune response are represented. In summary, 57% of the well characterized IRIS genes have these immune functions. Similar results are seen with the molecular function ontologies (data not shown), with the most highly represented being antigen binding and the activities of chemokines, cytokines and their receptors. Table 3 shows the gene families represented in IRIS that are known to have many members with key roles in the differentiation, function and regulation of the immune system, including those with the molecular functions mentioned. The Protcomp algorithm (Softberry, Inc.) predicts for the 1589 IRIS genes with ORFs that 24% of the encoded proteins are in the plasma membrane, 13% are secreted, 24% are nuclear and the remaining 39% are in other intracellular compartments. An expression differential of 10 fold between the highest nonimmune tissue level and highest immune cell level selects 122 genes from the single cell type categories. The expression profiles of the highest expressing of these genes are shown in Figure 3 and the rest are shown in Supplementary Figure 3. Reported literature on many of these genes confirms that specific expression of transcripts often results in specific protein expression and that they have key roles in the immune response. For each cell type, a few genes of interest are described below. Highly specific genes with lower expression levels are shown in Supplementary Figure 3.
Full figure and legend (299K)
T cellsGenes with specificity in T cells have expression profiles in a wide variety of patterns as shown in Figure 3a and Supplementary Figure 3a. The T cell receptor alpha locus, TCRA, as well as CD3 subunits D, E G are Classic fake van cleef jewelry expressed on most T cell subsets. CD8A and B1 are heterodimers of the established protein marker for CD8+ T cells which stabilizes interactions between the T cell receptor and an MHC class I peptide complex,23 and are expressed only in CD8 cells. LEF1 is expressed specifically in both CD8 and CD4 cells. LEF1 is a transcription factor that has been implicated as critical in a differentiation defect that leads to T cell lymphomas.24 A number of genes exhibit exquisite selectivity for memory vs na T cells and most of these show enhanced expression in Th1 or Th2 subsets. Cytokine IL9 is specific to both Th2 and activated memory cells, whereas CTLA4 and ICOS are highly expressed in activated memory cells but also seen at lower levels in other T cell subsets and are both cell surface regulatory receptors.25 GZMK is specific to CD8, Th2 and resting memory cells and is a serine protease that is proposed to prevent damage of bystander cells at sites of inflammation.26 LAG3 is found primarily on Th1 cells, but also on stimulated CD8+ T cells (data not shown), and it stimulates the maturation of dendritic cells.27
NK cellsThe highest expressing specific gene in NK cells is KLRF1, a C type lectin with immunoreceptor tyrosine based inhibitory motifs (ITIM) motifs that stimulates cytolysis28 as shown in Figure 3b. Its expression is highest in resting cells, and decreases upon activation. The KIR gene cluster is also represented, although the probesets probably cross hybridize with these closely related genes that have probably arisen from gene duplication. Some members are established protein markers for this cell type and all are known to play a key role in recognition of HLA class I ligands.29 NS1 BP is specifically expressed in activated NK cells and binds the influenza A virus NS1 protein.30
B cellsB cells are represented by a diversity of specific expression profiles. TCL1A is expressed solely in na B cells and is an intracellular enzyme that regulates an early stage of B and T cell differentiation,31, 32 as shown with the other B cell genes in Figure 3c and Supplementary Figure 3b. CD20 and a number of other genes have equivalent expression in both na B cells and memory cells expressing either IgG/IgA or IgM. An antibody therapeutic targeting CD20, Rituxan, is extremely effective in specific ablation of B cells.33 CD19 is an established protein marker for B cells. BAFFR is the principal receptor for the signaling pathway that maintains mature B cell survival.34 BANK1 protein has also been shown to be specific to B cells and may play a role in foreign antigen induced immune response.35 PAX5 is a transcription factor that plays an essential role in commitment to the B cell lineage.36 CD79A and B are two components of the B cell antigen receptor37 and are expressed at lower levels in plasma cells. FCRH1, FCRH2 and IRTA2 are closely related in sequence to the Fc receptor which functions in antibody binding and regulation of the immune response,38 but they have not been functionally characterized.39, 40, 41, 42 FCRH1 and FCRH2 are found primarily on na and memory B cells, while IRTA2 is specific to plasma cells from bone marrow. A number of other genes are specific to plasma cells from blood, from bone marrow or from both sources. BCMA is a member of the TNF receptor family that is hypothesized to transduce signals for B cell survival and proliferation.43
Monocytes and macrophagesSeveral distinct patterns of specific monocyte and macrophage expression are seen in Figure 3d and Supplementary Figure 3c. Expressed only in monocytes is PRAM1, an adaptor protein that appears to be involved in the differentation of monocytes.44 The second and most common pattern is transient expression in monocytes differentiated after 1 day, and lost after 7 days. Chemokines CCL24, CXCL1 and CXCL3, and chemokine receptor CCRL2,45 are expressed in the same pattern. GPR84 is a G protein coupled receptor, possibly a chemokine receptor, with protein expression detected on neutrophils and eosinophils,46 with this transient macrophage expression not yet reported. IL1B has the highest level of mean expression in this category, although there is a wide range of expression levels in individual blood donors. IL1B protein is also expressed by other cell types under certain conditions.47, 48 IL1A, the other chain in the IL1 heterodimer, is also expressed in this pattern, although it has been shown that IL1A and IL1B are regulated independently.49 Three other cytokines, IL1F9, IL19 and IL24, are expressed in a pattern similar to that of IL1. IL1F9 appears to play a modulatory role in IL1 receptor signaling.50 IL19 and
Immune response in silico (IRIS): immune specific genes identified from a compendium of microarray expression data
A R Abbas1, D Baldwin1, Y Ma1, W Ouyang2, A Gurney2,3, F Martin2, S Fong2, M van Lookeren Campagne2, P Godowski2, P M Williams3, A C Chan2 and H F Clark1,2
1Department of Bioinformatics, Genentech, Inc., South San Francisco, CA, USA2Department of Immunology, Genentech, Inc., South San Francisco, CA, USA3Department of Molecular Biology, Genentech, Inc., South San Francisco, CA, USACorrespondence: Dr HF Clark, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA.
Top of pageAbstractImmune cell specific expression is one indication of the importance of a gene's role in the immune response. We have compiled a compendium of microarray expression data for virtually all human genes from six key immune cell types and their activated and differentiated states. Immune Response In Silico (IRIS) is a collection of genes that have been selected for specific expression in immune cells. The expression pattern of IRIS genes recapitulates the phylogeny of immune cells in terms of the lineages of their differentiation. Gene Ontology assignments for IRIS genes reveal significant involvement in inflammation and immunity. Genes encoding CD antigens, cytokines, integrins and many other gene families playing key roles in the immune response are highly represented. IRIS also includes proteins of unknown function and expressed sequence tags that may not represent genes. The predicted cellular localization of IRIS proteins is evenly distributed between cell surface and intracellular compartments, indicating that immune specificity is important at many points in the signaling pathways of the immune fake van cleef & arpels clover diamond ring response. This enables the immune response to attack foreign invaders while recognizing and tolerating self antigens. Two distinct lineages of immune cells have evolved with cell types that specialize in each of the many roles that are required for this immune response. The myeloid lineage of monocytes, macrophages, dendritic cells and neutrophils carries out the innate immune response, recognizing microbial pathogens typically by carbohydrates found only in bacterial proteins. The lymphoid lineage of T cells, B cells and natural killer (NK) cells enables adaptive immunity by distinguishing self from non self antigens and also providing a memory of foreign proteins seen before. Other immune cells, such as eosinophils, basophils and mast cells, also participate in the immune response. Although a large repertoire of genes that play key roles in the differentiation, function and regulation of these immune cells is already well described, this has not yet led to a complete understanding of immune diseases.
Genome wide microarray expression profiling of immune cells provides opportunities for identifying other genes that may function in the immune response. Microarray experiments have been carried out on various immune cell subsets by a number of different investigators in order to understand gene expression differences during differentiation and activation.1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 These studies have provided invaluable insight into comprehensive gene expression profiles that define many different immune cell subsets and states of differentiation and activation. In particular, a recent study detailed elegantly the genes expressed specifically in many subsets of the T cell lineage.18 Comparison of gene expression profiles across a broad range of immune cell types and nonimmune tissues is necessary, however, to fully appreciate which gene expression differences are unique to each immune cell subset, which are found in multiple subsets or across immune cell lineages, and which are found more widely across the many cell types in the human body and thus may be involved in more general cellular processes. Differences among microarray platforms and experimental protocols used by different investigators often confound such comparison. Here, a compendium of microarray expression data from a broad representation of isolated immune cell subsets has been generated on the same microarray platform and analysis of gene expression profile across the major cell types of the immune system is made possible. Moreover, gene expression in all other major tissues allows determination of immune specific expression. Traditional methods of determining expression are performed on a gene by gene basis, and single microarray experiments show differential expression between just a few immune cell subsets. Here, genes fitting a complex expression criterion are identified on a genomic scale.
Immune cell specific expression is one indication of the importance of a gene's role in the function of the immune system. 'Cluster of differentiation' (CD) antigens are used experimentally as specific markers for immune cell subsets and they often play a key role in the function of that cell. For example, the T cell receptor is expressed only on T lymphocytes and is responsible for self antigen recognition, a primary function of this immune cell.19 Here, immune cell specifically expressed genes are identified by a survey of expression profiles across all the immune cells and major non immune tissues by a method termed Immune Response In Silico (IRIS). Immune cell specificity is determined generally by higher expression in any immune cell than expression in any nonimmune cell tissue. Finer characterization of specificity within an immune cell type, such as T cells, and an immune cell lineage, such as lymphoid cells, is also determined. Furthermore, expression profiles within subsets of an immune cell, such as classes of T cells expressing CD4 or CD8 antigens, memory and helper T cells and resting vs activated cells, further refine the specificity of immune specific genes. IRIS has identified both well characterized immune genes and a number of highly immune specific genes with unknown function.
Top of pageResultsIRIS identifies genes more highly expressed in immune cells than in any of the major organs of the body. Gene expression is surveyed across a compendium of samples, including immune cell subsets (Table 1) and a comprehensive range of normal tissues. The immune cells have been isolated from normal human blood by purifying each cell type via its specific cell surface markers. Activated and differentiated subsets were developed by in vitro stimulations. RNA samples were labeled and run on microarray chips that include probesets for virtually every human gene. Genes are often represented by more than one probeset and the expression levels vary, although the expression profiles are usually consistent. Genes are selected for IRIS based on cutoff values for expression levels in immune vs nonimmune samples, as shown in Supplementary Figure 1. Prior to the determination of these cutoffs, genomewide expression levels were evaluated by surveying the range of expression of all genes and the expression levels of a few families of genes already known to play important roles in the immune system. The highest mean expression level of any immune cell subset within each cell type as defined in Table 1 is calculated for each IRIS gene. Hierarchical clustering, a statistical method for grouping genes by the similarity of their expression profiles, reveals patterns of gene expression that are distinct among immune cell lineages. The dendrogram of the relationships of these genes mimics the evolutionary relationships of the lineages of immune cell types. The heat map showing all IRIS genes also illustrates the complexity of expression profiles, with most genes expressed at some level in more than one immune cell type. The highest mean expression of any cell subset for each probeset represents the expression of that cell type. The dendrograms shows the similarity of expression profiles for genes in the rows and cell types in the columns. Intensity values less than 1000 have the lightest shading and intensity values greater than 10,000 have the darkest shading.
Full figure and legend (166K)
IRIS categoriesWhile clustering is a useful method for identifying patterns of gene expression, it fails to clearly define the parameters of these patterns. Therefore, cutoff values of gene expression signatures in various immune cell types were approximated and used to define the IRIS categories that were observed in the clustering (Table 1) and these parameters are defined in the Cell Lineage Assignment section of Materials and methods. The probesets are categorized by their profiles according to the degree of specificity within immune cells. Profiles specific to one cell type are assigned to the categories T cell, NK cell, B cell, monocyte, dendritic or neutrophil. Profiles specific to more than one cell type within a lineage are assigned to lymphoid or myeloid categories. Profiles specific to cell types across lineages, as well as probesets with expression levels equivalent in all immune cells, are assigned to the multiple category. Genes represented by several probesets may appear in more than one category. A gene is best described as specific to the most exclusive category to which it has been assigned, that is, the highest expressing probeset may be in the T cell category and more weakly expressing probesets for that gene may fall into the lymphoid or multiple categories because of the stringency of cutoff levels used.
Patterns of expression profiles within each lineageCell types within a lineage share some immune specific genes, suggesting that they confer common functions among those cells. The expression profiles are very diverse and complex, but some general patterns emerge. As described above, the IRIS categories attempt to group genes within a cell type, or, if specific to more than one cell type, within a lineage. Here, these categories are assessed for general patterns of expression profiles. K means clustering reveals groups of expression profiles within an IRIS category, as shown in Figure 2. Within the lymphoid category clustered in Figure 2a, T cells and NK cells are shown to share a number of immune fake van cleef ring specific genes (clusters 1, 4 and 6), whereas B cells express more distinct genes (clusters 3 and 5). Often, the genes shared between T and NK cells are specifically expressed in the activated state of both cells (cluster 6). The myeloid category shown in Figure 2b reveals a similar relationship between genes transiently expressed in differentiating macrophages and also in LPS induced dendritic cells (cluster 8). Profile clusters of genes highly specific to single cell types are shown in Supplementary Figure 2. T cells have several general patterns of specificity among subsets of T cells, as shown in Supplementary Figure 2a. CD8 cells have a number of unique genes (cluster 1), and also share some genes with CD4 cells (cluster 2). T helper cells share some specific genes (clusters 3 and 6) and a number of genes appear only in activated memory T cells (cluster 5). B cells also show distinct profiles, as shown in Supplementary Figure 2b. Some genes are expressed specifically in both na and memory B cells (cluster 2), others only in plasma cells (clusters 3 and 4), while there are no specific B cell genes expressed distinctly in na or memory cells alone. Likewise, monocytes and macrophages shown in Supplementary Figure 2c reveal a class of genes specific to monocytes (cluster 1) and another transiently expressed in differentiating macrophages (cluster 2), but none specific to fully differentiated macrophages. However, Figure 2b shows that fully differentiated macrophages share profiles with neutrophils (cluster 2) and dendritic cells (cluster 5). All of these observations provide insights into the differentiation of immune cells as they have evolved to play specific roles in the immune response. The y axis is intensity level.
Full figure and legend (344K)
Statistical significance of IRISThe statistical significance of specificity is assessed for the genes within each IRIS cell type or lineage, as shown in Supplementary Figure 4. The F statistic measures both immune cell variation and sample variation simultaneously, so a low F value can either indicate similar expression of a gene across immune cells, or gene expression variability among samples, but does not distinguish between the two. Conversely, a gene highly specific to one cell type has a high F value, particularly if the replicate samples all have similar expression of that gene. Sample variability occurs because of both incomplete reproducibility of microarray conditions and biological variation between blood donors due to unknown causes. The multiple category has the highest proportion of genes with low F values, suggesting that this category includes most of the genes falsely assigned as immune specific. While the microarray technology used here is a well established experimental method for detecting differential gene expression, independent confirmation by real time quantitative PCR (RT QPCR) is carried out before pursuing further functional studies on genes of interest.20
Gene ontology and gene familiesGenes of known function are assigned a term from Gene Ontology, a structured vocabulary for describing biological process, molecular function and cellular component.21 Table 2 shows the Biological Processes most highly specific to IRIS, meaning that these ontologies are seen in IRIS in higher numbers than in a random set of genes. Many specific responses to different foreign invaders, chemotaxis of immune cells to sites of inflammation and other aspects of the immune response are represented. In summary, 57% of the well characterized IRIS genes have these immune functions. Similar results are seen with the molecular function ontologies (data not shown), with the most highly represented being antigen binding and the activities of chemokines, cytokines and their receptors. Table 3 shows the gene families represented in IRIS that are known to have many members with key roles in the differentiation, function and regulation of the immune system, including those with the molecular functions mentioned. The Protcomp algorithm (Softberry, Inc.) predicts for the 1589 IRIS genes with ORFs that 24% of the encoded proteins are in the plasma membrane, 13% are secreted, 24% are nuclear and the remaining 39% are in other intracellular compartments. An expression differential of 10 fold between the highest nonimmune tissue level and highest immune cell level selects 122 genes from the single cell type categories. The expression profiles of the highest expressing of these genes are shown in Figure 3 and the rest are shown in Supplementary Figure 3. Reported literature on many of these genes confirms that specific expression of transcripts often results in specific protein expression and that they have key roles in the immune response. For each cell type, a few genes of interest are described below. Highly specific genes with lower expression levels are shown in Supplementary Figure 3.
Full figure and legend (299K)
T cellsGenes with specificity in T cells have expression profiles in a wide variety of patterns as shown in Figure 3a and Supplementary Figure 3a. The T cell receptor alpha locus, TCRA, as well as CD3 subunits D, E G are Classic fake van cleef jewelry expressed on most T cell subsets. CD8A and B1 are heterodimers of the established protein marker for CD8+ T cells which stabilizes interactions between the T cell receptor and an MHC class I peptide complex,23 and are expressed only in CD8 cells. LEF1 is expressed specifically in both CD8 and CD4 cells. LEF1 is a transcription factor that has been implicated as critical in a differentiation defect that leads to T cell lymphomas.24 A number of genes exhibit exquisite selectivity for memory vs na T cells and most of these show enhanced expression in Th1 or Th2 subsets. Cytokine IL9 is specific to both Th2 and activated memory cells, whereas CTLA4 and ICOS are highly expressed in activated memory cells but also seen at lower levels in other T cell subsets and are both cell surface regulatory receptors.25 GZMK is specific to CD8, Th2 and resting memory cells and is a serine protease that is proposed to prevent damage of bystander cells at sites of inflammation.26 LAG3 is found primarily on Th1 cells, but also on stimulated CD8+ T cells (data not shown), and it stimulates the maturation of dendritic cells.27
NK cellsThe highest expressing specific gene in NK cells is KLRF1, a C type lectin with immunoreceptor tyrosine based inhibitory motifs (ITIM) motifs that stimulates cytolysis28 as shown in Figure 3b. Its expression is highest in resting cells, and decreases upon activation. The KIR gene cluster is also represented, although the probesets probably cross hybridize with these closely related genes that have probably arisen from gene duplication. Some members are established protein markers for this cell type and all are known to play a key role in recognition of HLA class I ligands.29 NS1 BP is specifically expressed in activated NK cells and binds the influenza A virus NS1 protein.30
B cellsB cells are represented by a diversity of specific expression profiles. TCL1A is expressed solely in na B cells and is an intracellular enzyme that regulates an early stage of B and T cell differentiation,31, 32 as shown with the other B cell genes in Figure 3c and Supplementary Figure 3b. CD20 and a number of other genes have equivalent expression in both na B cells and memory cells expressing either IgG/IgA or IgM. An antibody therapeutic targeting CD20, Rituxan, is extremely effective in specific ablation of B cells.33 CD19 is an established protein marker for B cells. BAFFR is the principal receptor for the signaling pathway that maintains mature B cell survival.34 BANK1 protein has also been shown to be specific to B cells and may play a role in foreign antigen induced immune response.35 PAX5 is a transcription factor that plays an essential role in commitment to the B cell lineage.36 CD79A and B are two components of the B cell antigen receptor37 and are expressed at lower levels in plasma cells. FCRH1, FCRH2 and IRTA2 are closely related in sequence to the Fc receptor which functions in antibody binding and regulation of the immune response,38 but they have not been functionally characterized.39, 40, 41, 42 FCRH1 and FCRH2 are found primarily on na and memory B cells, while IRTA2 is specific to plasma cells from bone marrow. A number of other genes are specific to plasma cells from blood, from bone marrow or from both sources. BCMA is a member of the TNF receptor family that is hypothesized to transduce signals for B cell survival and proliferation.43
Monocytes and macrophagesSeveral distinct patterns of specific monocyte and macrophage expression are seen in Figure 3d and Supplementary Figure 3c. Expressed only in monocytes is PRAM1, an adaptor protein that appears to be involved in the differentation of monocytes.44 The second and most common pattern is transient expression in monocytes differentiated after 1 day, and lost after 7 days. Chemokines CCL24, CXCL1 and CXCL3, and chemokine receptor CCRL2,45 are expressed in the same pattern. GPR84 is a G protein coupled receptor, possibly a chemokine receptor, with protein expression detected on neutrophils and eosinophils,46 with this transient macrophage expression not yet reported. IL1B has the highest level of mean expression in this category, although there is a wide range of expression levels in individual blood donors. IL1B protein is also expressed by other cell types under certain conditions.47, 48 IL1A, the other chain in the IL1 heterodimer, is also expressed in this pattern, although it has been shown that IL1A and IL1B are regulated independently.49 Three other cytokines, IL1F9, IL19 and IL24, are expressed in a pattern similar to that of IL1. IL1F9 appears to play a modulatory role in IL1 receptor signaling.50 IL19 and
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