Serial analysis of gene expression in murine fetal thymocyte cell lines

Feng-Qi Zhao1,3, Zong-Mei Sheng1, Mark M. Tsai1, Alan E. Hubbs1, Ruixue Wang1, Timothy J. O’Leary1, David J. Izon2,4 and Jeffery K. Taubenberger1

1 Molecular Pathology Division, Department of Cellular Pathology and Genetics, Armed Forces Institute of Pathology, Rockville, MD 20850, USA 2 Department of Pathology and Laboratory Medicine, University of Pennsylvania Medical Center, Philadelphia, PA 19104, USA 3 Present address: Department of Animal Science, University of Vermont, Burlington, VT 05405-0148, USA 4 Present address: Cancer Biology, Telethon Institute for Child Health Research, Subiaco, WA 6008 Australia

Correspondence to: J. K. Taubenberger; E-mail: taubenbe{at}afip.osd.mil
Transmitting editor: A. Singer


    Abstract
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgments
 References
 
FTL-1, -3 and -10 are three murine day 14 fetal thymocyte cell lines produced in order to model developmental stages within early (CD3CD4CD8) thymocyte differentiation. In this study, we used the serial analysis of gene expression (SAGE) method to perform a systematic analysis of transcripts present in these three cell lines. A total of 77,313 SAGE tags were sequence identified from the three cell lines, representing 24,645 unique transcripts. Differentially expressed mRNA transcripts representing different gene classes were identified, including T cell functional genes, cytokine receptors, adhesion molecules and transcription factors. These results may serve as a model of the transcriptome of early thymocyte differentiation. A large number of unknown expressed sequence tags were also found to be differentially expressed. In order to validate the SAGE data, selected differentially expressed transcripts identified by SAGE were analyzed by quantitative RT-PCR in normal murine double-negative stage DN1–4 thymocytes. Expression of the transcription factors RUNX2 and PHD finger protein 2 and of the IGF type 1 receptor was shown to have differentially regulated expression patterns in sorted DN1–4 cells. These genes, and others identified by this analysis, are likely to play important roles in the development of T cells.

Keywords: differentiation, gene expression, serial analysis of gene expression, T cells, thymocytes, transcription factors


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgments
 References
 
T cell differentiation is a highly controlled process involving the immigration of thymic precursor cells into the thymus, and the subsequent developmentally controlled expression of regulatory and functional proteins, the acquisition of cell surface proteins, and the ordered rearrangement of the TCR genes [for reviews, see (15)]. An extensive understanding of intrathymic differentiation has been gained by phenotypic analysis of different developmental stages by the coordinate expression of cell surface antigens, predominantly of CD4 and CD8, such that cells are characterized as being double negative (DN), double positive (DP) and CD4 or CD8 single positive (SP). DN cells have been further divided into four developmental stages, DN1–4, based on their expression of CD25 and CD44 (6).

Our understanding of the process of T cell differentiation at the genetic level has also made progress in the last decade, with the identification of several transcription factors and other regulatory genes that play crucial roles in lineage-specific differentiation (1,3). Elucidation of molecular mechanisms controlling temporally regulated gene expression during intrathymic development would greatly facilitate our understanding of T cell differentiation and function. The small number of DN thymocytes that can be isolated from adult or fetal thymus (7,8) hampers such studies.

In order to model early stages of thymic development in vitro, 10 transformed murine fetal thymocyte cell lines were produced by incubating day 14–17 fetal organ cultures with a v-myc/v-raf-containing retroviral construct (9). These cells are CD3CD4CD8 (DN) by surface antigen analysis, and express different T cell-associated and T cell-specific genes. For instance, FTL-1 does not express any CD3 subunit genes. FTL-2 and -3 express CD3{gamma}, but not CD3{delta} or CD3{epsilon}. FTL-10 expresses CD3{gamma}, CD3{delta} and CD3{epsilon}. Based on the general pattern of additive gene expression and rearrangement status of the TCR genes, these cell lines were ordered in such a way as to reflect possible stages of early DN thymocyte differentiation and provide a useful model with which to study the molecular events in early thymocyte development.

The serial analysis of gene expression (SAGE) method, developed in 1995 by Velculescu et al. (10), allows for a quantitative, representative and comprehensive profile of gene expression, and thus provides a powerful means to compare gene expression between two populations. SAGE relies on the proposition that a short nucleotide sequence tag (~10–15 bp) isolated from a defined position of a transcript is sufficient for its identification. These short sequence tags are isolated from poly(A)-selected mRNA, ligated to form long concatemers, cloned and sequenced. The generated tag sequences are used to search the GenBank database to identify each corresponding transcript. The frequency of each tag directly reflects transcript abundance. Theoretically, a series of 9 nucleotides would have a high probability of being unique in the genome, since the number of possible nonamers (49 or 262,144) is much larger than the number of genes in a mammalian genome (30,000–40,000) (1113). Examination of published SAGE tag lists, however, suggests that ~75% of SAGE tags match to a single gene sequence, while approximately one-fourth match sequences from multiple genes (14). A variety of tools are available by which the correct gene(s) associated with these non-unique SAGE tags can be identified. Used in this way, SAGE provides an accurate profile of gene expression at both the qualitative and the quantitative level.

SAGE analyses on sorted normal DN thymocytes would be difficult because large quantities of polyadenylated RNA are needed. In this study, we have applied the powerful SAGE method to analyze the gene expression profiles of three of our v-myc/v-raf-transformed murine fetal thymocyte cell lines (FTL-1, -3 and -10). Because these cells seem to reflect different stages of early T cell development, the analysis of the gene expression profiles of these cells may help elucidate the molecular events involved in the earliest phases of thymocyte development and may provide insight into the key regulators that control T cell differentiation. Several differentially expressed genes identified in this analysis may play crucial roles in early T cell differentiation. To validate these results, quantitative RT-PCR analysis of selected differentially expressed genes in normal mouse DN1–4 thymocytes was performed. Expression patterns of genes identified in this study can be determined in normal thymocytes of different stages and thus these results may serve as a model of the transcriptome of early thymocyte differentiation.


    Methods
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgments
 References
 
Cells and cell culture
Transformed murine fetal thymocyte cell lines FTL-1, -3 and -10 were three of 10 cell lines derived by incubating fetal thymic organ cultures with a v-myc/v-raf-containing retroviral construct as previously described (9). The KE8 T cell line was a gift from Kevin Holmes (National Institute for Allergy and Infectious Diseases). The cells were cultured in the medium DMEM, supplemented with 10% (v/v) FCS, 5.5 x 10–5 M 2-mercaptoethanol, 2 mM glutamine, 10 mM HEPES, 100 U/ml penicillin and 100 µg/ml streptomycin. Cell culture reagents were purchased from Life Technologies (Gaithersburg, MD). Harvested cells were washed once with cold PBS prior to freezing at –70°C.

DN thymocytes (CD4CD8) were prepared from thymocytes from young adult (6–8 weeks old) C57/BL6 mice. The thymocytes were stained with allophycocyanin–anti-CD44 (PharMingen, San Diego, CA), phycoerythrin–anti-CD25 (Caltag, Burlingame, CA), biotin–anti-CD3, -CD4, -CD8 and -CD19, and anti-Mac-1 (‘Lin’ cocktail, all from PharMingen), using the following gates Lin/CD44+/CD25 (DN1), Lin/CD44+/CD25+ (DN2), Lin/CD44/CD25+ (DN3) and Lin/CD44/CD25 (DN4) (15). Biotinylated antibodies were revealed with streptavidin–Cy5 (PharMingen).

RNA isolation
Polyadenylated (polyA+) RNA was directly prepared from FTL-1, -3 and -10 cells using the Micro-FastTrack mRNA isolation kit (Invitrogen, San Diego, CA), and total RNA was isolated using the TRIzol reagent (Life Technologies), following the manufacturer’s instructions.

SAGE
The SAGE analysis was performed as previously described (10,16). In brief, double-stranded cDNA was synthesized from 5 µg of polyadenylated RNA using biotinylated oligo-dT18 (Integrated DNA Technologies, Coralville, IA). The cDNA was digested with anchoring enzyme NlaIII and the resulting 3' cDNA terminal fragments were bound to streptavidin-coated magnetic beads (Dynal, Oslo, Norway). The bound cDNA was ligated to two oligonucleotide linkers containing the recognition site for the tagging enzyme BsmFI and then digested with BsmFI to release tags from the beads. The ditags were formed by ligating the released tags to one another and then PCR-amplified, isolated, concatemerized and cloned into the pZero vector (Invitrogen). The transformation colonies were PCR-screened using M13 forward and reverse primers. PCR products >300 bp were sequenced using an ABI 377 automated sequencer (Applied Biosystems, Foster City, CA). Sequence files were analyzed using SAGE software (10,16) (kindly provided by Kenneth W. Kinzler, Johns Hopkins University), and the resources of the National Center for Biotechnology SAGE web page (http://www.ncbi.nlm.nih.gov/SAGE/) and UniGene web page (http://www.ncbi.nlm.nih.gov/UniGene/) (14,17). Linker sequences and the repeated ditags were eliminated before analysis.

To compare these SAGE libraries, each tag number was normalized using SAGE software (10,16). Statistical analysis of significance was performed using Monte Carlo analysis that allows for a large number of comparisons to be made using the SAGE data. For analysis, the null hypothesis assumes that the transcriptomes of the two populations are the same. For each sequence tag, 105 simulations were performed to determine the relative likelihood that the observed difference in tag number between two SAGE data sets is due to chance alone (p-chance).

RT-PCR
Total RNA (5 µg) was reverse transcribed with 200 U MMLV reverse transcriptase and 0.1 mM oligo(dT)12–18 in 20 µl total volume following the manufacturer’s instructions (Life Technologies). PCR was performed using 0.5 µl of the RT reaction as a template in the following conditions: 1 x PCR buffer II (Applied Biosystems), 2.5 mM MgCl2, 200 µM dNTPs, 0.4 µM of each 5' and 3' primers, and 5 U of Taq polymerase (Applied Biosystems) in a 50-µl reaction. The primers used are listed in Table 1 and were synthesized by Integrated DNA Technologies (Coralville, IA). Reactions were carried out in a Perkin-Elmer model 9600 thermal cycler under the following conditions: 5 min at 94°C, followed by 35 cycles of 94°C for 60 s, 60°C for 30 s and 72°C for 90 s, followed by a 7-min extension at 72°C. The PCR products were analyzed by agarose gel electrophoresis and ethidium bromide staining.


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Table 1. RT-PCR primers
 
Quantitative real-time RT-PCR
First-strand cDNA was synthesized from 100 ng total RNA. The primers and probes used for quantitative real-time RT-PCR are listed in Table 1, and were designed using Primer Express software (Applied Biosystems). Primers were synthesized by Integrated DNA Technologies and probes, labeled with 6-carboxy-fluorescein (FAM) and the quencher 6-carboxy-tetramethylrhodamine (TAMRA), were purchased from Applied Biosystems. The 18S rRNA levels provided an endogenous control using the TaqMan ribosomal RNA Control Reagents kit (Applied Biosystems). The rRNA probe was labeled with VIC and the quencher TAMRA. Three sets of primers and internal oligonucleotide probes were used to detect target gene transcripts. Quantitation was performed using data generated with the ABI Prism 7700 Sequence Detection System (Applied Biosystems). CT values were determined using TaqMan SDS analysis software (18). For each primer and probe set tested, triplicate CT values were averaged. Because CT values vary linearly with the logarithm of the amount of RNA, this average represents the geometric mean. The average CT value of rRNA was subtracted from the average experimental gene CT value (IGF1R, PHD-2 or RUNX2) to give the {Delta}CT value. The average {Delta}CT value for the positive control (KE8) was then subtracted from the average {Delta}CT value of each sample to give the {Delta}{Delta}CT value. The comparative CT method was used for quantitation of relative gene expression. The amount of target RT-PCR product, normalized to an endogenous standard and relative to a control gene, is given by the formula 2{Delta}{Delta}CT (18).

Gene rearrangement analysis
Genomic DNA was prepared for PCR by lysing cells in lysis buffer (10 mM Tris, pH 8.4, 50 mM KCl, 2 mM MgCl2, 0.45% NP-40, 0.45% Tween 20 and 60 µg/ml proteinase K), incubating them at 55°C for 1 h, then inactivating the protease by heating to 95°C for 10 min. This DNA was used directly for PCR. The PCR reactions were the same as indicated in RT-PCR above except using 1 µl template (300 ng DNA) and the primers used for Ig heavy chain and {kappa} light chain rearrangement status were as described (19,20). PCR with 40 cycles of amplification was performed, consisting of 1 min at 94°C, 1 min at 55°C and 1.5 min at 72°C, followed by a single 10-min period at 72°C. An aliquot of 20 µl of each reaction was analyzed on a 1.4% agarose gel. The gel was blot-transferred to a Sure Blot nylon membrane (Oncor, Gaithersburg, MD). Ig heavy chain blots were probed with 32P-labeled JH DNA (pJ11, containing JH3 and JH4, kindly provided by Sisir Chattopadhyay, National Institute of Allergy and Infectious Diseases) (21) using standard protocols (22). Ig {kappa} light chain blots were probed with a 32P-labeled JH germline region 1014-bp probe amplified with the following primers using the amplification conditions above: forward primer 5'-GTG GTG GAC GTT CGG TGG AG-3' and reverse primer 5'-ACT TTG TCC CCG AGC CGA AC-3'.


    Results
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgments
 References
 
General results and analysis of SAGE tags
Three independent SAGE libraries were constructed from mRNA isolated from FTL-1, -3 and -10 cells. Between 800 and 2500 clones were isolated and sequenced to obtain 12,487, 32,316 and 32,510 tags from FTL-1, -3 and -10 cells respectively, which represent 6020, 13,369 and 11,735 unique transcripts in each cell. In total, 77,313 tags were sequenced, jointly representing 24,645 unique transcripts. The expressed tags were searched through the NCBI UniGene database to identify individual genes. Of these, 4462 tags (18.1%) were mapped to known genes, 3145 tags (12.8%) represented expressed sequence tags (EST) in the UniGene database. The remaining tags reflected unknown (unmatched) genes.

In Table 2, we have listed the 50 most abundant tags expressed in FTL-1, -3 and -10 cells. For each tag, the matching transcripts with their UniGene Cluster numbers are listed and the frequency with which the putative mRNA is identified in each cell. As expected, a large fraction of these abundant tags matches with widely expressed housekeeping genes, such as ribosomal proteins, the cytoskeleton proteins (actin, ferritin and histone H2A.1), proteins associated with protein synthesis and metabolism (elongation factors) and energy metabolism (glyceraldehyde-3-phosphate dehydrogenase). Among these most abundant transcripts only lectin L14 (galactose binding, soluble 1) has been shown to play a possible role in embryonic development (23).


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Table 2. Top 50 of most abundantly expressed genes in all FTL-1, -3 and -10 cells
 
FTL-1 has a gene expression pattern consistent with the B cell lineage
By examining the genes expressed in FTL-1 cells, we found that FTL-1 has a gene expression profile consistent with a B lymphocyte (Table 3). It expresses several B lymphocte-specific genes, including immunoglobulin (Ig) genes, Ig J chain precursor, Ig {kappa} chain variable 28, constant region of Ig {lambda}-2 light chain and Ig heavy chain 6. The first two Ig genes expressed in FTL-1 were confirmed by RT-PCR (Fig. 1). Other B cell-associated genes expressed in FTL-1 are Bcl-3 and B cell translocation gene 3 (BTG3).



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Fig. 1. RT-PCR analysis for differential gene expression in FTL-1, -3 and -10 cells. Total RNA (5 µg) was reverse transcribed in 20 µl total volume and 0.5 µl was used in each PCR reaction as described. The number of times the SAGE tag for each of the transcripts was identified (as shown in Tables 4, 5 and 7) is shown along the bottom of the figure. (Abbreviations: Lectin L14 = Lectin, galactose binding, soluble 1; Sec. leukocyte protease inh. = secretory leukocyte protease inhibitor.) ß-Actin served as the RT-PCR positive control.

 
Southern blot analysis for Ig heavy chain rearrangement in FTL-1 demonstrated DJ but not VDJ rearrangement (data not shown). Southern blot analysis for Ig {kappa} light chain rearrangement showed a germline pattern (data not shown). This gene expression pattern, coupled with the partial IgH rearrangement, is consistent with a pre-B cell.

Differential gene expression between the pre-B cell FTL-1 cell line and DN T cell FTL-3 and -10 cell lines
Because FTL-1 has an expression pattern consistent with the B-lymphocyte lineage, the SAGE data from FTL-3 and -10 were grouped into one data set and compared to the gene expression profile of FTL-1 cells. Because they represent different cell types, there are >335 genes that display at least a 5-fold difference in their expression between FTL-1 and grouped FTL-3 and -10 cells. Significance was determined using Monte Carlo algorithms in the SAGE software (10,16). In Table 4, we have listed the top 50 genes overexpressed in FTL-1 versus FTL-3 and -10 cells. The calculated p-chance that differences in gene expression between FTL-1 and the grouped FTL-3 and -10 data sets were due to chance alone ranged from 0 to 0.008. Of these 50 tags, only 13 match known genes in UniGene database (17), 14 match mouse EST sequences (24), 14 had multiple matches and nine have no known match. The top two most abundant transcripts overexpressed in FTL-1 (versus FTL-3 and -10) are secretory leukocyte protease inhibitor and thioredoxin-like 2. In Table 5, we have listed the top 50 genes overexpressed in FTL-3 and -10 versus FTL-1. Using Monte Carlo analysis, the calculated p-chance that differences in gene expression between the grouped FTL-3 and -10 data sets and FTL-1 for these 50 tags were due to chance alone ranged from 0 to 0.005. Of these 50 genes, 36 match known genes, eight match EST sequences and six had multiple matches. Lectin L14 (galactose-binding, soluble 1) is the most abundant transcript overexpressed in FTL-3 and -10 cells. Some of other known genes in this list are S100 calcium-binding protein A4, secreted phosphoprotein 1, PHD finger protein 2, prothymosin ß4 and heat shock proteins.


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Table 4. Transcripts overexpressed in FTL-1 cells (versus FTL-3 and -10 cells)
 

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Table 5. Transcripts overexpressed in FTL-3 and -10 cells (versus FTL-1 cells)
 
Differential gene expression between FTL-3 and -10
In Table 6, we have listed the top 55 genes overexpressed in FTL-3 versus -10. The 61 most overexpressed genes in FTL-10 versus -3 are listed in Table 7. The calculated p-chance that differences in gene expression for these tags between FTL-3 and -10 were due to chance alone ranged from 0 to 0.003. In this comparison, gene expression differences that might play a direct role in the control of thymocyte differentiation were sought. Transcripts over-expressed in FTL-3 versus -10 included insulin-like growth factor 1 receptor, E-selectin ligand 1, non-muscle myosin light chain and RUNX2. There were three tags overexpressed in FTL-3 with no matches in the database and 14 that matched EST. Genes overexpressed in FTL-10 versus FTL-3 included GATA-3, the CD3 genes and PHD finger protein 2. A large number of tags in this comparison also either had no match (two tags) or matched EST (17 tags).


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Table 6. Transcripts overexpressed in FTL-3 (versus FTL-10 cells)
 

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Table 7. Transcripts overexpressed in FTL-10 (versus FTL-3 cells)
 
Further study of differentially expressed genes between FLT-3 and -10
To validate the SAGE results, RT-PCR for selected transcripts was performed (Table 1). The results for these transcripts corroborated the expression differences observed in the SAGE data; representative results are shown in Fig 1.

In order to demonstrate that gene expression differences identified by SAGE in the FTL cell lines reflect biological differences in developing thymocytes, quantitative real-time RT-PCR was performed for three genes identified from the SAGE data set that were differentially expressed between the FLT-3 and -10 cell lines: insulin-like growth factor 1 receptor (IGF1R) (25), PHD finger protein 2 (26) and Runt-related transcription factor 2 (RUNX2) (27). Quantitative real-time RT-PCR (TaqMan) was performed using RNA isolated from sorted DN (CD4CD8) thymocytes, at developmental stages DN1 (CD44+CD25), DN2 (CD44+CD25+), DN3 (CD44CD25+) and DN4 (CD44CD25) (6). The results, presented graphically as the comparative CT method (see Methods) derived by the formula 2{Delta}{Delta}CT, are shown in Fig. 2. Expression patterns of each of these three genes varied considerably during progression from the DN1 to 4 stages. IGF1R expression decreased by 21-fold between DN1 and 2, and expression remained low in DN3 and 4 cells. PHD finger protein 2 expression was bi-modal, and dropped 3.6-fold between DN1 and 2, increased slightly in DN3, and then showed a 3-fold increase from DN3 to 4. RUNX2 expression was similar to PHD finger protein 2 expression with a 3-fold drop in expression between DN1 and 2 cells, and with a 2-fold increase in expression between DN3 and 4 cells. PHD finger protein 2 expression, however, was highest in DN4 stage cells and RUNX2 expression was highest in DN1. Comparable SAGE results between FTL-3 and -10 are shown in Tables 5 (PHD finger protein 2) and 6 (IGF1-R and RUNX2).



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Fig. 2. Quantitative RT-PCR for Insulin growth factor 1 receptor (IGF1R), PHD finger protein 2 (PHD-2) and Runt-related transcription factor 2 (RUNX2) expression in sorted normal DN1–4 thymocytes. Results are shown graphically as gene expression relative to expression in KE8 cells where the amount of target RT-PCR product, normalized to an endogenous standard and relative to a control gene (ribosomal RNA), is given by the formula 2{Delta}{Delta}CT (18).

 

    Discussion
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgments
 References
 
The SAGE method (10) allows for a quantitative, representative and comprehensive profile of gene expression between two cell populations. In this study, SAGE was used to examine the transcript expression patterns in DN fetal thymocyte cell lines that have been shown to express differentially a set of T cell-associated genes (9). As previously described, the 10 FTL cell lines, produced by v-myc/v-raf transformation of day 14 fetal thymic organ cultures, may reflect an ordered pattern of gene expression during DN thymocyte differentiation. These observations were extended in the current study by examining >77,000 transcripts from three of the FTL cell lines, giving a quantitative representation of >24,000 unique transcripts. The expression patterns revealed in this analysis may serve to model the transcriptome of early thymocytes.

The SAGE data set reveals several interesting genes whose expression patterns in thymocytes or T cells have not been previously examined. Examples of genes expressed in FTL-3 and -10 but not in FTL-1 most likely reflect gene expression patterns of early DN thymocytes (Table 5). The following are examples of genes demonstrating this expression pattern:

•Mouse L14 lectin (Table 5, tag sequence 1) was originally isolated in a differential screen designed to identify genes that are regulated during the differentiation of murine embryonic stem cells in vitro (23). Its expression pattern during embryogenesis suggests that it may have multiple roles during pre- and post-implantation development (28). The role this S-type lectin plays in thymocyte differentiation is not known.

•PHD finger protein 2 (Table 5, tag sequence 29), containing a PHD-type zinc finger domain, is one of a diverse group of chromatin-mediated transcriptional regulators (26,29). Its expression pattern has not been previously described in hematopoietic cells.

The FTL-3 and -10 cell lines showed significant differences in gene expression profiles (Tables 6 and 7) that may reflect developmentally regulated patterns of DN thymocyte gene expression. Examples of genes already shown to play a role in early thymocyte development were identified by the SAGE analysis as well as those not previously known to be involved in T lymphopoiesis:

•The zinc-finger transcription factor GATA-3 (Table 7, tag sequence 28) is known to play a key role in T lymphopoiesis and its expression is necessary for the development of early thymocyte stages (3032). GATA-3 (as shown by targeted insertion of a LacZ reporter into the GATA-3 gene by homologous recombination) has also previously been shown to be expressed on a high percentage of DN1 stage prothymocytes (32), with down-regulation in DN2 and 3 stages.

•Insulin-like growth factor type 1 has also been implicated to play a regulatory role in T cell development through expression of its receptor (IGF1R) (25) (Table 6, tag sequence 17).

•Syndecan 1 (Table 6, tag sequence 9), a heparan sulfate-rich integral membrane proteoglycan which functions as a matrix receptor, has been shown to be expressed by B-lymphocytes in a developmentally regulated manner (33) but not in T cells.

•Secreted phosphoprotein 1 (Table 6, tag sequence 35), also known as osteopontin or Eta-1 (34), is a T cell cytokine that has been shown to play a key role in the development of type 1 immunity (35). Eta-1 has also been shown to be a protein ligand of CD44 (36). Its expression has not been examined in developing thymocytes.

•Runt-related transcription factor 2 (RUNX2) (Table 6, tag sequence 52), also known as AML3, CBFa1 and PEBP2{alpha}A, has been shown to play a key role in bone development (37), but is also expressed in the T cell lineage (38). RUNX2 is one member of a family of transcription factors, the core binding factor family, which play essential roles in eukaryotic development, osteogenesis, hematopoiesis and, by chromosomal translocation, in leukemogenesis (27). Transgenic mice with RUNX2 under the control of the CD2 locus control region show abnormal T cell development and a small percent develop T cell lymphomas (27).

To examine the biological relevance of these findings, three differentially expressed genes identified in the SAGE analysis were examined by quantitative RT-PCR (TaqMan) in sorted, normal DN1–4 thymocytes. As seen in Fig. 2, IGF1R, PHD finger protein 2 and RUNX2 showed significant quantitative differences in expression as thymocytes progress from stage DN1 to 4. IGF1R showed high expression in DN1 with a marked down-regulation of expression in stages DN2–4. As DN progressively commit to the T cell lineage throughout their development down-regulation of expression of this gene may be associated with loss of multipotency towards the B cell, NK cell and myeloid pathways. Additionally, IGF1 receptor expression was previously shown to be highest in DN cells as compared to DP or SP thymocytes (25), where it was shown that IGF1 enhances DNA synthesis. DN cells have the highest percentage of cells in S/G2/M phases of cell division (39). The role that IGF1R plays in commitment to the T cell lineage is not known, but the data presented here and elsewhere suggest a role in DN proliferation.

PHD finger protein 2, also known as PHF2, belongs to a group of transcriptional regulators thought to play a role in gene expression by influencing chromatin structure (26,29). The PHD zinc finger domain, characterized by the C4HC3 motif, was originally identified in an Arabidopsis homeodomain protein, HAT3.1, hence the name PHD (‘plant homeodomain’) (40). Subsequently numerous transcriptional regulators containing PHD finger domains have been identified in plant, yeast, Caenorhabditis elegans, Drosophila, mouse and human genomes (41). While the function of these diverse proteins is not yet fully understood, it is thought that many proteins containing PHD finger domains reside in multiprotein complexes and the domains may be involved in protein–protein interactions (29,42). Interestingly, several genes involved in recurrent chromosomal translocations in leukemias have also been shown to contain PHD finger domains including MLL, AF10 and MOZ (41,43). It is unclear what role PHD finger protein 2 plays in T lymphopoieis.

RUNX2 expression showed a similar down-regulation between the DN1 and 2 stages, and also showed an up-regulation between stages DN3 and 4, perhaps reflecting a role in TCRß rearrangement at the DN3 stage. It is not currently understood what functions the products of these two genes play in the transition between the DN1 and 4 stages of thymocyte development.

In the previous study examining the gene expression patterns of the FTL cell lines, FTL-1 was shown to express no T cell-specific genes (9). Here we demonstrate that this cell line has a pattern of gene expression (along with partial Ig heavy chain rearrangement) consistent with a pre-B cell. Since the lines were produced by transformation of day 14 fetal thymic organ cultures, the evidence suggests that this line was derived from a B lymphocyte precursor present in the fetal thymus. It has been demonstrated that precursor cells (DN) in the thymus can give rise not only to T cells, but also to NK cells, B cells and dendritic cells (44,45). Interestingly, recent studies have shown that a significant number of B cells develop intrathymically and are exported to the periphery (46,47).

T lymphopoiesis has been extensively studied and many genes thought to play a developmental role have been identified [see recent reviews (2,3,5,4850)]. Techniques like Northern blotting, RT-PCR or subtraction libraries (51) have been used. While such studies have identified crucial receptors, transcription factors and other gene classes, traditional approaches are limited by the number of genes that can be analyzed. Quantitative assessments of gene expression differences are difficult using such techniques. More recently, subtraction libraries coupled with high-throughput EST sequencing has been applied to sorted human thymocytes (52), and array technologies have been applied to gene expression analyses of hematopoietic stem cells (53) and lymphomas (54). SAGE analysis has been performed on resting and activated mast cells (55). The goals of these studies have been to generate a database of comparative gene expression during differentiation, activation or neoplastic transformation. The current study extends these observations to model gene expression in DN thymocytes. The current study has focused on the 50 or so most differentially expressed genes for which statistical support for differential expression is extremely high. A large number of genes that are thought to play a role in T lymphopoiesis were identified during the SAGE analysis, but their tag numbers were low, so that there was only weak statistical support for differential gene expression. Examples of these genes included CD3{gamma}, CD3{delta} and CD3{epsilon}, the IL-2 receptor {alpha} (CD25), the zinc finger transcription factor Ikaros, the helix-loop-helix transcription factors E2A and HEB, and the HMG (high mobility group) box-containing transcription factors LEF-1 and TCF-1 (3,5,6). Future work will examine quantitatively the expression patterns of such identified genes in normal thymocytes of different stages, as well as identify differentially regulated genes that are not yet characterized.


    Acknowledgments
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgments
 References
 
The authors thank Ying Liu, Ryan Satcher and Dr Ronald Przygodzki for technical support, Dr Warren Pear for providing reagents and the use of cell sorting equipment, and Dr Kenneth W. Kinzler for providing us with the SAGE software. We thank Ann H. Reid for helpful discussion. This work was supported by grants from the American Registry of Pathology and by the intramural funds of the Armed Forces Institute of Pathology. The opinions or assertions contained herein are the private views of the authors and are not to be construed as those of the US Department of the Army or the US Department of Defense. This is a US government work. There are no restrictions on its use.


    Abbreviations
 
DN—double negative

DP—double positive

EST—expressed sequence tag

FAM—6-carboxy-fluorescein

SAGE—serial analysis of gene expression

SP—single positive

TAMRA—6-carboxy-tetramethylrhodamine


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Table 3. B cell-specific transcripts expressed in FTL-1 cells
 

    References
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgments
 References
 
  1. Rodewald, H. R. and Fehling, H. J. 1998. Molecular and cellular events in early thymocyte development. Adv. Immunol. 69:1.[ISI][Medline]
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