Evolutionarily well-conserved, γδ T cells may have been precursors to modern B and αβ T cells (1). γδ T cells are distinct from αβ T cells, in part, by the genes that encode their TCRs, their ability to recognize Ag in the absence of classical MHC Ag presentation, and their role in maintaining the integrity of epithelial tissue (2). γδ T cells represent a small percentage of the circulating T cells in adult humans and mice, but make up a majority of the intraepithelial lymphocyte (IEL) 3 population in the gut and other epithelial mucosa. Conversely, γδ T cells are a major subset in adult ruminants and make up a majority (up to 70%) of circulating lymphocytes in neonatal calves (3). They are the first T cells to develop and are recruited, and expand in response, to various infections in humans, rodents, and other animals (4). Evidence suggests that γδ T cells play an important role in innate immunity at mucosal surfaces (5) and may contribute to immunity in neonates (2).

Although there is considerable evidence from work in humans, rodents, and ruminants that γδ T cells participate in a variety of responses, their overall importance and the similarities of γδ T cells between species are still unclear. A number of variables complicate the analyses of these cells in these different systems. First, distinct functional subsets of γδ T cells, some of which have opposing activities, confound the interpretation of the role of γδ T cells as a whole. These subsets can be distinguished by their TCR (6, 7, 8, 9), tissue localization (10, 11, 12), and/or coexpression of lineage-specific differentiation molecules (10, 11, 12). γδ T cell subsets in some systems selectively migrate to sites of inflammation, exacerbate inflammatory reactions, and contribute to antimicrobial responses, whereas other subsets suppress inflammatory and/or immune responses (8, 12, 13). Unfortunately, these subsets are not equally represented nor are their phenotypes the same in all species. For example, in adult human blood, the major proportion of the circulating pool of γδ T cells express the same TCR (Vγ9/Vδ2 (also termed Vγ2/Vδ2)) and selectively respond to phosphoantigens of various pathogens, such as Mycobacterium tuberculosis (6). Microbial responsive γδ T cell subsets exist in mice as well, but their TCR gene usage and the Ags they recognize are different (7, 8, 9). However, a subset distinguished by low expression of either the αα or αβ dimer form of CD8 is common to all species, and compromises a major portion of the IELs in rodents and ruminants (10, 11, 12). Second, experimental variables associated with animal age, environment (microbial/allergen/toxin exposure), and tissue source (blood, lymph, tissues) also complicate development of a consensus about the functional importance of γδ T cells (2, 4, 11, 14, 15, 16, 17, 18, 19). Even within the γδ TCR gene deletion mouse model, the precise and likely highly diverse roles of γδ T cells in immunity remain enigmatic, as their function appears to be specific to infectious agent or assault (2). Besides the inability to study individual γδ T cell subsets in TCR gene knockout mice, compensatory mechanisms likely develop due to the redundancy of the immune system, thus potentially cloaking a vast array of γδ T cell functions.

One approach to minimize the impact of the aforementioned variables is to perform global gene expression analyses of γδ T cells to identify trends conserved between species, to distinguish γδ T cells from other T cells, and to differentiate between subsets of γδ T cells. Advances in genomic technology now provide the means of rapidly comparing global transcriptional differences between rare cell populations. These approaches have been used extensively in many biological settings, including analysis of T cell subsets. Recently, four studies using genomic approaches to analyze transcription profiles of tissue and blood γδ T cells have been published. The intent of this review is to compare the results from these genomic reports and to elucidate general transcriptional trends for γδ T cells that emerge despite several variables between studies. In addition, we briefly summarize some key findings and emphasize new research directions suggested by these studies.

There are many excellent studies of gene transcription in T cells, including the analysis of γδ T cells, using techniques such as Northern blot, RNase protection assays, RT-PCR, and quantitative RT-PCR, which have provided tremendous insight into our understanding of immunology. However, we will focus on two genomic approaches (Fig. 1) that have been used in the study of γδ T cells: microarray technology, including cDNA and oligonucleotide arrays, and serial analysis of gene expression (SAGE). Arrays are highly sensitive and uncomplicated. Analysis of microarray data is simplified by prior annotation of the genes, the ability to select specific genes to spot on the array, and by the increasing number of bioinformatics tools available for analysis. Arrays are the most widely used functional genomics tool in immunology (20) and have been used to compare CD4+ and CD8+ type 1 and type 2 αβ T cells (21), to examine the activation of αβ T cells (22), and to characterize αβ Th cell development (23). SAGE is technically more challenging and sequencing costs can be high, but offers some advantages over microarrays, as the genes that are identified are not predetermined, thus the data are unbiased and comprehensive (24). After normalization, SAGE libraries are also comparable to all other SAGE libraries, irrelevant of the time or place of their construction. Long SAGE can be used to isolate longer (21 bp) tags and is ideal for new gene discovery (25). Finally, confidence in the quantification of low abundance transcripts and the reliability of SAGE-predicted gene regulation can be increased by simply sequencing additional tags from the same SAGE library. In summary, both microarray and SAGE techniques are valuable new methods for analysis of transcriptional trends in specific cell populations.

FIGURE 1.

Genomics tools used to study immune cell function. A, Microarray technology involves hybridization of fluorescently labeled total mRNA to a microchip spotted with oligonucleotides or cDNAs representing thousands of genes. Differently labeled mRNA from two different cell populations or treatments can be applied, and essentially compete for hybridization to spots on the array, thus, the differential expression levels of mRNA between the two samples is measured. B, SAGE is used to isolate 14-bp sequence “tags” derived from the 3′ end of every mRNA present in a cell population. This sequence is sufficient to identify the mRNA from which it is derived. The tags are sequenced and identified by comparison to sequence databases. L, linker; B, biotin; S, streptavidin-conjugated to magnetic beads.

FIGURE 1.

Genomics tools used to study immune cell function. A, Microarray technology involves hybridization of fluorescently labeled total mRNA to a microchip spotted with oligonucleotides or cDNAs representing thousands of genes. Differently labeled mRNA from two different cell populations or treatments can be applied, and essentially compete for hybridization to spots on the array, thus, the differential expression levels of mRNA between the two samples is measured. B, SAGE is used to isolate 14-bp sequence “tags” derived from the 3′ end of every mRNA present in a cell population. This sequence is sufficient to identify the mRNA from which it is derived. The tags are sequenced and identified by comparison to sequence databases. L, linker; B, biotin; S, streptavidin-conjugated to magnetic beads.

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These genomic techniques have recently been applied to study γδ T cells in the murine and bovine systems, revealing interesting and convergent gene expression trends despite species, animal age, and experimental dissimilarity. Fahrer et al. (26) used oligonucleotide microarrays to compare the expression profiles of γδ IELs and mesenteric lymph node CD8+ αβ T cells in an adult mouse Yersinia pseudotuberculosis infection model (26). The comparison indicated that γδ IELs are likely capable of diverse immunological responses, and suggested roles in intestinal lipid metabolism, physiology, and cholesterol homeostasis, emphasizing a close physical and functional relationship with the gut epithelium. Although the γδ IELs were constitutively activated, they also expressed several inhibitory receptors. However, infection with Y. pseudotuberculosis did not change the transcription of genes in the γδ IELs compared to γδ IELs derived from uninfected mice. In contrast, CD8+ αβ T cells found in the mesenteric lymph node of the infected animal appeared to be activated following infection. Unfortunately, there are no published reports showing that γδ T cells respond to Y. pseudotuberculosis infection, and the clinical symptoms of the animals were not reported. Given that subsets of γδ T cells can respond very differently to various infectious agents, this particular agent may only activate a subset of γδ IELs, or may not affect them at all and thus, either the change in gene expression profiles was too subtle to detect or would not be expected. However, in this analysis αβ T cells from a different tissue had transcriptional profiles that were very distinct from γδ IELs (26).

A similar study identified an “activated yet resting,” cytolytic and immunoregulatory phenotype for intestinal IELs by applying SAGE to analyze gene expression in adult mouse γδ and αβ IELs (27). This is the first study that directly compared γδ and αβ T cells from the same anatomical location. Although the expression of several effector genes was high in both populations, the expression of conventional cytokines and cytokine receptors was low. The expression profiles of known genes did not clearly differentiate γδ from αβ IELs. This underscores the potential importance of signals in the tissue microenvironment in directing resting IELs, regardless of T cell type, into conformance and/or the impact of the isolation procedure in artificially reducing differentially expressed transcripts. A majority of the mRNAs that were differentially expressed between γδ and αβ IELs were unknown genes, whose eventual characterization may provide exciting new insight into differences in γδ and αβ IELs.

Both of these mouse studies analyzed γδ IELs as one group and presented a phenotype for γδ IELs that was both activated and resting. Two recent analyses from our laboratory suggest one possible reason for this mixed and seemingly opposing state of the γδ IELs. We have analyzed circulating bovine γδ T cells subsets, defined predominantly by expression of CD8, using cDNA microarrays and SAGE (28, 29). After high-speed cell sorting, both subsets were globally activated with either Con A and IL-2 (for array analysis) or PMA and ionomycin (for SAGE analysis) for 3–4 h prior to RNA extraction to re-establish mRNAs that were potentially down-regulated during the sorting process. The overriding conclusion from these studies was that the transcriptional activities of these two γδ T cell subsets were very different, almost polar. The analyses of bovine γδ T cell subsets indicated that while CD8 γδ T cells were activated, proliferative, and inflammatory, the CD8+ subset expressed genes consistent with quiescence, trafficking to the mucosa, and immune suppression. Both of these investigations also identified genes consistent with a proapoptotic trend in the CD8+ γδ T cells and a resistance to apoptosis in the CD8 γδ T cells. The tendency of CD8+ γδ T cells toward apoptosis and quiescence is consistent with a tissue-specific, anti-inflammatory “immune sentinel” phenotype, whereas resistance to apoptosis is consistent with the inflammatory nature of CD8 γδ T cells (30). Further, the microarray analysis of bovine γδ T cell subsets (28) indicated that CD8+ γδ T cells were sensitive to IFN and expressed lymphoid chemokines. The SAGE analysis of bovine γδ T cell subsets (29) underscored their relationship to myeloid cells by their expression of genes such as CD14, CD68, scavenger receptor 1, and B lymphocyte-induced maturation protein (BLIMP-1). BLIMP-1 is a master regulator of B and myeloid cell differentiation that was also detected in the murine microarray analysis (26).

The obvious disadvantage of the studies with bovine cells is that the bovine genome is not completely sequenced. Thus, we relied on cross-reactivity in human cDNA arrays in the microarray analysis and incomplete identification of tags in the case of the SAGE investigation (28, 29). Genes that were not identified in these analyses may have been missed because of the lack of cross-reactivity or genomic sequence. Despite this disadvantage, not only were several of the same genes and trends identified in both of the bovine analyses, each study also revealed unique, differentially expressed genes, some of which were also identified in the murine studies.

Table I shows several genes that were identified in more than one of the recent genomic analyses of γδ T cells and their relative expression levels within the various studies. Gene expression trends for these examples correlated well, despite different approaches and skepticism about reproducibility of genomics studies between laboratories. The bovine studies show that γδ T cell subsets in circulation express many of the same genes as γδ IELs in murine tissue. Specifically, there is overlap between the various studies in the expression of chemokines (RANTES) and macrophage inflammatory protein (MIP)1β, cytokines (lymphotoxin), adhesion molecules (β7 integrin), cytokine receptors (IL-2R and CXCR4), signaling molecules (regulator of G protein signaling-1 and PIM-1), and transcription factors (BLIMP-1 and JunB), among other genes (Table I). Because the bovine cells were isolated from the blood whereas the mouse cells were isolated from the gut mucosa, this suggests that the tissue microenvironment may only be partially responsible for these gene expression profiles of the IELs.

The two mouse analyses were highly consistent and comprehensive; however, because γδ T cells were analyzed as one group in each study it is impossible to attribute a gene to a specific subset of the cells. In contrast, the bovine studies place the expression of a given gene in a γδ T cell subset and thus, the potential specific function of the gene in these cells is narrowed. For example, Shires et al. (27) correlated expression of JunB with low expression of antiapoptotic regulators Bcl-2 and Bcl-xL and suggested a suppressive relationship (27). However, in the circulating bovine CD8 γδ T cell subset, strong expression of JunB correlated with expression of several antiapoptotic regulators, suggesting that in γδ T cells, the relationship between these genes is not suppressive and is potentially distinct (28, 29). These seemingly contradictory trends emphasize the value of studying gene expression in γδ T cell subsets to the overall understanding of gene function in γδ T cells as a whole.

A major goal of such genomic studies should be to suggest avenues for further detailed investigations. This includes careful analysis of the factors that control gene expression and the precise functional pathway of a given expressed protein in the context of the surrounding tissues and cells. One unexpected specific gene product of interest was the transcription factor BLIMP-1, which has previously been shown to be involved in B and myeloid cell differentiation (31, 32). BLIMP-1 was shown by two genomic studies to be expressed in γδ T cells, in mouse γδ IELs in the microarray analysis by Fahrer et al. (26) and in bovine CD8 γδ T cells by SAGE (29). In the investigation by Meissner et al. (29), BLIMP-1 expression in γδ T cells was confirmed by quantitative real-time RT-PCR and RNase protection assays. BLIMP-1 expression in γδ T cells is very intriguing. BLIMP-1 is known to suppress expression of c-Myc, MHC class II, and inhibitor of differentiation 3 (Id3) in B and myeloid cells (31). The expression of BLIMP-1 in CD8 γδ T cells is consistent with a higher level of MHC class II and Id3 expression in CD8+ γδ T cells as compared to CD8 γδ T cells (28). The extensive functional activities of BLIMP-1, its role in driving B and myeloid cell differentiation, and its selective expression in a subset of γδ T cells provide compelling reasons to study the potential role of this unique transcription factor in γδ T cell differentiation.

A number of genes normally associated with myeloid cells were expressed in circulating γδ T cells, including pathogen-associated molecular pattern (PAMP) receptors, such as CD14, scavenger receptor, and mannose-binding receptor (28, 29). This, combined with previous studies showing that γδ T cells respond to microbial products, such as alkylamines and phosphoantigens (33), prompted us to extend these analyses to other PAMP receptors, most notably Toll-like receptors (TLRs). TLRs are the receptors for PAMPs, such as LPS and peptidoglycan, and are expressed on dendritic, myeloid, epithelial, and endothelial cells (34). The mRNAs encoding TLRs 2 and 4 have been demonstrated in murine γδ T cells (35). We have now amplified TLR 2, 3, 4, and 5 transcripts from bovine γδ T cell subsets by real-time RT-PCR and are studying the functions of these PAMP receptors. Thus, a general trend identified by genomic analyses has spurred a new course of investigation.

Though not intended to be comprehensive, below we outline three suggested areas of investigation using specific examples where additional genomic analyses might be useful in furthering our understanding of γδ T cell biology. Comparisons of γδ and αβ T cells as well as different γδ T cell subsets could be done in each of these settings.

Activation states can alter the transcriptional profiles of immune cells. Although the issue of artifactual activation was a major concern in the two mouse studies, the probability that transcripts were down-regulated during tissue processing and cell isolation was not addressed. To overcome this potential problem, both subsets in the bovine studies were globally activated with either Con A/IL-2 or PMA and ionomycin prior to RNA extraction. Clearly, neither approach is an ideal reflection of the in vivo situation. We have preliminary evidence suggesting that mitogen activation affects various splenic T cell types differently. Our results suggest that upon global activation of both populations isolated from the bovine spleen, γδ T cells become more dissimilar from αβ T cells. Specifically, based on the analysis of >80,000 initial SAGE tags from resting and 6-h Con A/IL-2-activated splenic γδ and αβ T cells, the resting cells shared 81% of expressed genes whereas the activated γδ and αβ T cells shared only 66%. This difference was primarily based on a greater increase in γδ T cell-specific genes (J. C. Graff and M. A. Jutila, manuscript in preparation). These preliminary studies suggest that understanding the progression of gene expression profiles during in vitro and, more importantly, in vivo activation of γδ T cells derived from various anatomical sites will reveal novel signaling pathways and downstream responses unique to these cells.

Further genomic comparisons of γδ T cell transcriptional profiles during responses against relevant infectious agents are clearly necessary. To date, a majority of such “infectomics” studies has been performed on cultured T cells. Given the complexity of the tissue microenvironment in which γδ T cells are frequently found, and the clear influence the microenvironment has on γδ T cells, it is critical that these cells not be taken out of context, and that infection studies be conducted in vivo or directly ex vivo whenever possible. Subsets of γδ T cells can contribute to protective immunity against a wide range of pathogens, including those that cause malaria and tuberculosis (36). γδ T cells have also been shown to expand in the peripheral blood of human patients infected with many different pathogens, including salmonella, toxoplasma, and HIV (4). In cases where γδ T cells decrease in peripheral blood, such as in Bordetella pertussis infection in children, they may traffic to the sites of infection and have an important role upon arrival (37). Many of these diseases are also of veterinary health relevance, such as salmonellosis, for which the ruminant is an excellent model, both from the perspective of the disease and in the availability of γδ T cells. When genomics techniques are applied to various infection models, it is likely the distinct immunological functions of γδ T cells or γδ T cell subsets will begin to emerge.

Although it is known that γδ T cell populations can be altered in various situations of lung pathology, their precise function in asthma and other airway diseases is only now beginning to be revealed (38). γδ T cells can prevent murine airway hyperresponsiveness by maintaining homeostasis in airway mucosa during inflammatory challenge (39). Conversely, distinct γδ T cell subsets may actually promote allergic pulmonary inflammation (40, 41). γδ T cells additionally have a clear importance in recovery from airway epithelial damage after ozone assault (42). The application of genomics techniques to γδ T cells in studies of airway inflammation will likely provide many answers as to their precise mechanisms of protection. An excellent model for lung pathology (43) and a good source of γδ T cells is the ovine model, which has likely been underutilized due to lack of specific reagents. However, we have recently demonstrated that use of ruminants is not an obstacle to the application of genomics techniques (28, 29). Regardless of species, genomics studies of lung γδ T cells may provide new insight into their elusive roles in respiratory pathology and protection.

It has been repeatedly shown that the functions of γδ and αβ T cells are not completely redundant, and given the strong evolutionary conservation of γδ T cells, their function is surely unique. These distinctions will likely emerge through genomic investigations following specific stimuli, such as infection, that are likely to influence γδ T cell subsets differently. Furthermore, expression of individual genes analyzed using less comprehensive techniques are likely to be less conserved than are the more global patterns and trends discerned from genomic analyses, as the investigations discussed herein have demonstrated. A major argument against genomics techniques is that the approach is not hypothesis-driven. However, for a rare, ancient, and functionally elusive cell population such as γδ T cells, there is insufficient data to begin to make assumptions about their transcriptional profiles in various situations. Indeed, much of the genomics data uncover novel and unexpected genes and pathways. Thus, genomics techniques are ideal tools that result in more focused and directed hypotheses with which to approach γδ T cells in various anatomical sites and infection models.

Table I.

Expression values for specific genes identified in more than one of the recent genomic investigations of γδ T cellsa

Gene Name(s)Murine
Fahrer et al. (26 )bShires et al. (27 )c
γδ IELsCD8+ αβ T cellsfγδ IELsαβ IELs
Granzyme A ++++ ++++ ++++ 
Granzyme B ++++ +/− +++ +++ 
RANTES ++++ +++ ++++ +++ 
MIP-1α +/− − +/− +/− 
MIP-1β (ACT2) +/− − +/− +/− 
Lymphotactin +/− +/− +/− 
Lymphotoxin β   +/− +/− 
CXCR4 +/− − − 
Integrin β7   +/− +/− 
Thymosin β4 (prothymosin β4)   +++ +++ 
     
     
Regulator of G protein signaling-1 +++ ++ 
Id-2 ++   
Diacylglycerol kinase −   
Nur 77 +/−   
ZAP-70   +/− +/− 
BLIMP-1 −   
Jun-B ++ 
PIM-1 ++ +/−   
Bcl-2 (Shires)(27 ), Bcl-xL (Meissner (29 ), Hedges (28 ))   +/− +/− 
IL-2R +/−   
MHC class I   ++ ++ 
β2 microglobulin   ++ ++ 
Gene Name(s)Murine
Fahrer et al. (26 )bShires et al. (27 )c
γδ IELsCD8+ αβ T cellsfγδ IELsαβ IELs
Granzyme A ++++ ++++ ++++ 
Granzyme B ++++ +/− +++ +++ 
RANTES ++++ +++ ++++ +++ 
MIP-1α +/− − +/− +/− 
MIP-1β (ACT2) +/− − +/− +/− 
Lymphotactin +/− +/− +/− 
Lymphotoxin β   +/− +/− 
CXCR4 +/− − − 
Integrin β7   +/− +/− 
Thymosin β4 (prothymosin β4)   +++ +++ 
     
     
Regulator of G protein signaling-1 +++ ++ 
Id-2 ++   
Diacylglycerol kinase −   
Nur 77 +/−   
ZAP-70   +/− +/− 
BLIMP-1 −   
Jun-B ++ 
PIM-1 ++ +/−   
Bcl-2 (Shires)(27 ), Bcl-xL (Meissner (29 ), Hedges (28 ))   +/− +/− 
IL-2R +/−   
MHC class I   ++ ++ 
β2 microglobulin   ++ ++ 
a

Expression values range from ++++ (very high) to +/− (very low) to − (not detected) based on the range of results within each study.

b

Normalized gene expression values as calculated by GENECHIP 3.0 software (Affymetrix, Santa Clara, CA).

c

Tag abundance based on libraries containing ≈91,000 tags each.

d

Tag abundance based on libraries containing ≈21,000 tags each.

e

Raw fluorescent signals collected by Incyte (Palo Alto, CA).

f

Derived from mesenteric lymph node.

Table IA.

Continued

BovineProtein Function
Meissner et al. (29 )dHedges et al. (28 )e
CD8+ γδ T cellsCD8 γδ T cellsCD8+ γδ T cellsCD8 γδ T cells
    Cytotoxic protein contained in granules 
    Cytotoxic protein contained in granules 
  +/− Chemokine, agonist for CCR1, 3, 4, and 5 
    Chemokine, agonist for CCR1, 4, and 5 
  ++ +/− Chemokine, agonist for CCR3, 5, and 8 
    Chemokine, agonist for XCR1 
  +/− Can induce cell death or survival in cells expressing receptor 
  +/− Chemokine receptor for stromal cell-derived factor-1 
  +/− Mucosal homing receptor 
  ++++ ++ Induction of metalloproteinases, chemotaxis, angiogenesis, and inhibition of inflammation, inhibition of bone marrow stem cell proliferation 
  ++++ Negative regulator of G protein-coupled receptor signaling 
  ++++ Transcriptional repressor in T cell development 
  ++++ Attenuates protein kinase C θ-induced responses 
  ++ N10/NGFI-B/Nr4a1 transcription factor 
  ++ ++++ ζ-associated protein of 70 kDa; T cell activation 
+/−   B and myeloid cell differentiation transcription factor 
+++ ++++ +/− Regulates cytokine expression in Th2 cells 
  ++ ++++ Serine/threonine kinase, T cell proliferation 
− ++ +/− ++ Antiapoptotic regulators 
     
−   T cell activation 
+++ ++++   Ag presentation 
+/−   Ag presentation 
BovineProtein Function
Meissner et al. (29 )dHedges et al. (28 )e
CD8+ γδ T cellsCD8 γδ T cellsCD8+ γδ T cellsCD8 γδ T cells
    Cytotoxic protein contained in granules 
    Cytotoxic protein contained in granules 
  +/− Chemokine, agonist for CCR1, 3, 4, and 5 
    Chemokine, agonist for CCR1, 4, and 5 
  ++ +/− Chemokine, agonist for CCR3, 5, and 8 
    Chemokine, agonist for XCR1 
  +/− Can induce cell death or survival in cells expressing receptor 
  +/− Chemokine receptor for stromal cell-derived factor-1 
  +/− Mucosal homing receptor 
  ++++ ++ Induction of metalloproteinases, chemotaxis, angiogenesis, and inhibition of inflammation, inhibition of bone marrow stem cell proliferation 
  ++++ Negative regulator of G protein-coupled receptor signaling 
  ++++ Transcriptional repressor in T cell development 
  ++++ Attenuates protein kinase C θ-induced responses 
  ++ N10/NGFI-B/Nr4a1 transcription factor 
  ++ ++++ ζ-associated protein of 70 kDa; T cell activation 
+/−   B and myeloid cell differentiation transcription factor 
+++ ++++ +/− Regulates cytokine expression in Th2 cells 
  ++ ++++ Serine/threonine kinase, T cell proliferation 
− ++ +/− ++ Antiapoptotic regulators 
     
−   T cell activation 
+++ ++++   Ag presentation 
+/−   Ag presentation 

We thank Drs. Willi Born and Mark Quinn for critical review and several helpful suggestions.

1

This work was supported by U.S. Department of Agriculture Initiative for Future Agriculture and Food Systems Grant 2000-04446.

3

Abbreviations used in this paper: IEL, intraepithelial lymphocyte; SAGE, serial analysis of gene expression; BLIMP-1, B lymphocyte-induced maturation protein; PAMP, pathogen-associated molecular pattern; TLR, Toll-like receptor.

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