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Microarray analysis distinguishes differential gene expression patterns from large and small colony Thymidine kinase mutants of L5178Y mouse lymphoma cells

  • 1,
  • 2,
  • 1,
  • 2,
  • 1 and
  • 2Email author
BMC Bioinformatics20067(Suppl 2):S9

Published: 26 September 2006



The Thymidine kinase (Tk) mutants generated from the widely used L5178Y mouse lymphoma assay fall into two categories, small colony and large colony. Cells from the large colonies grow at a normal rate while cells from the small colonies grow slower than normal. The relative proportion of large and small colonies after mutagen treatment is associated with a mutagen's ability to induce point mutations and/or chromosomal mutations. The molecular distinction between large and small colony mutants, however, is not clear.


To gain insights into the underlying mechanisms responsible for the mutant colony phenotype, microarray gene expression analysis was carried out on 4 small and 4 large colony Tk mutant samples. NCTR-fabricated long-oligonucleotide microarrays of 20,000 mouse genes were used in a two-color reference design experiment. The data were analyzed within ArrayTrack software that was developed at the NCTR. Principal component analysis and hierarchical clustering of the gene expression profiles showed that the samples were clearly separated into two groups based on their colony size phenotypes. The Welch T-test was used for determining significant changes in gene expression between the large and small colony groups and 90 genes whose expression was significantly altered were identified (p < 0.01; fold change > 1.5). Using Ingenuity Pathways Analysis (IPA), 50 out of the 90 significant genes were found in the IPA database and mapped to four networks associated with cell growth. Eleven percent of the 90 significant genes were located on chromosome 11 where the Tk gene resides while only 5.6% of the genes on the microarrays mapped to chromosome 11. All of the chromosome 11 significant genes were expressed at a higher level in the small colony mutants compared to the large colony mutants. Also, most of the significant genes located on chromosome 11 were disproportionally concentrated on the distal end of chromosome 11 where the Tk mutations occurred.


The results indicate that microarray analysis can define cellular phenotypes and identify genes that are related to the colony size phenotypes. The findings suggest that genes in the DNA segment altered by the Tk mutations were significantly up-regulated in the small colony mutants, but not in the large colony mutants, leading to differential expression of a set of growth regulation genes that are related to cell apoptosis and other cellular functions related to the restriction of cell growth.


  • Colony Size
  • Ingenuity Pathway Analysis
  • Small Colony
  • Large Colony
  • Microarray Gene Expression Analysis


The mouse lymphoma assay (MLA) is used internationally for regulatory decision-making and it is the mammalian in vitro gene mutation assay preferred by the U.S. FDA, the U.S. EPA, and the International Committee on Harmonization (including the European, Japanese and U.S. pharmaceutical companies and regulatory agencies) [13]. The MLA is conducted using an L5178Y cell line that is heterozygous for the Tk gene. The assay detects forward mutation of the wild-type Tk allele (Tk 1b) located on mouse chromosome 11 [4]. In this assay, Tk-deficient (Tk-/- or Tk0/-) mutants of the L5178Y/Tk+/- mouse lymphoma cells are selected by the pyrimidine analog trifluorothymidine (TFT) because TFT inhibits division of the Tk competent (Tk+/-) cells that are capable of incorporating TFT into the DNA. The mutant cells cannot incorporate TFT into their DNA because of the Tk gene deficiency. Therefore, the mutants can grow and develop into colonies in the selective growth medium while the Tk-competent cells are growth arrested and do not divide.

A striking feature of the Tk mutant colonies recovered in the MLA is the presence of two size classes of mutants. Immediately following their isolation, the cells in the large colonies grow at a normal rate, while cells in the small colonies grow slowly. The relative frequency of the two colony classes is mutagen dependent. Generally, clastogens induce more small colony mutants while point mutagens induce more large colony mutants [58]. It should be noted that many chemicals induce both small and large colony mutants.

It is important to obtain definitive information concerning the underlying molecular basis for the small and large colony mutant phenotype. This can be particularly important in a regulatory context. There is increasing interest in distinguishing between chemicals that cause point mutations and those that cause chromosomal mutants. The small and large colony Tk mutant phenotype was identified more than 30 years ago and there have been several hypotheses proposed to explain the difference between these two mutant types. The first one suggested that the small-colony mutants result from large scale damage to the chromosome 11b on which the Tk+ allele resides while large-colony mutants result from mutational events affecting the expression of only the Tk gene [911]. This hypothesis was expanded to state that small colony mutants are the consequence of intergenic lesions affecting the Tk gene and other putative growth control gene(s) that may or may not be on chromosome 11. Large colony mutants are the consequence of either intragenic lesions limited to the Tk gene or intergenic lesions that do not affect the growth control gene [12, 13]. So far, the growth control gene or genes have not been identified. Further analysis using microsatellite markers on chromosome 11 demonstrated that both large and small colony mutants can apparently have relatively large alterations of chromosome 11b [14, 15]. Also, it is clear that the entire chromosome 11b is lost in some large colony mutants [15]. Another possible hypothesis, demonstrated in other cell types, invokes a process of chromosome damage and repair. In this model, a cell with chromosome damage would suffer arrested growth until the cell repairs the damage [16, 17]. None of these hypotheses, however, can fully explain the difference between the small and large colony phenotypes because the molecular analysis of mutants (primarily microsatellite analysis) does not reveal a clear cut distinction in the degree of "damage" between small and large colony mutants. That is, the fundamental mechanistic difference(s) between the small and large colony mutant phenotypes has not been elucidated using the available analytical techniques.

The advent of gene microarrays permits the analysis of gene expression for thousands of genes simultaneously in biological samples of interest, permitting functional interpretation of the transcriptome state of any given cell type at a particular physiologic state [18, 19]. These cellular mRNA expression profiles yield global genomic fingerprints that identify the biological state of that cell. Molecular profiling has rapidly become an effective approach to further understanding of the phenotypes of tumor cells [20, 21]. Because the colony size of Tk mutants is determined by the growth rate of mutant cells, it would be expected that there would be some difference in the expression levels of one or more specific growth regulation genes. Microarray analysis should allow us to measure gene expression in large and small colony mutants and discern possible mechanisms that lead to the two phenotypes based on the differences between the gene expression profiles.

ArrayTrack software developed at the National Center for Toxicological Research provides an integrated solution for managing, analyzing, and interpreting microarray gene expression data. It is MIAME (Minimum Information about a Microarray Experiment) supportive for storing both microarray data and experiment parameters associated with a pharmacogenomics or toxicogenomics study. Using ArrayTrack, users can easily select a normalization method and a statistical method applied to a stored microarray dataset to cluster genes into different groups according to expression profiles, to determine a list of differentially expressed genes (significant genes), and to link the gene list directly to pathways and gene ontology for functional analysis. ArrayTrack is being integrated and further refined at the U.S. FDA as a review tool for the pharmacogenomics data submission program ArrayTrack is also freely available to public [22].

In this study, previously isolated small and large Tk mutants were evaluated for differences in gene expression using microarray technology. We used this approach to address the following 4 questions: (1) whether microarray analysis using ArrayTrack could distinguish the two Tk mutant phenotypes; (2) whether microarray analysis could identify candidate genes that might contribute to the phenotype difference; (3) whether genes altered in their expression might be localized on chromosome 11 near the Tk gene; and (4) whether the results might provide insight into the underlying mechanisms responsible for the two types of mutants. For this analysis, we selected 4 small and 4 large colony mutant samples from a previously conducted 3'-azido-3'-deoxythymidine (AZT)-treatment [23]. These mutants were selected because they all showed the same microsatellite pattern. They were LOH for both Tk and D11Mit42 (see Figure 2). We used this strategy for the mutant selection so that we could determine if the microarray approach, which evaluates individual gene expression, would provide a level of resolution not provided by the microsatellite analysis. We found that microarray analysis using ArrayTrack distinguished the two different cellular phenotypes and identified a set of candidate genes that are responsible for the colony size phenotypes. Also, the expression of a high proportion of genes located near the Tk gene on chromosome 11 was differentially altered between the large and small colony mutant samples, which might be the original reason for the formation of two different sizes of colony mutants.
Figure 1
Figure 1

Large and small colony Tk mutants in TFT selective medium. A. shows different size of colonies in a 96-well plate and B. displays a typical large colony mutant and a typical small colony mutant.

Figure 2
Figure 2

The loss of heterozygosity (LOH) pattern of all mouse lymphoma Tk mutants (4 small and 4 large colony mutant samples. The codes in the upper row are microsatellite loci on chromosome 11. The numbers in the middle row are the distances from the top of the chromosome to the locus in cM (centimorgan). The symbol indicates LOH and indicates that the locus retains heterozygosity.

Materials and Methods

Cells and culture conditions

L5178Y/Tk+/--3.7.2C mouse lymphoma Tk-deficient mutants were grown according to the methods described by Chen and Moore [4]. Briefly, the basic medium was Fischer's medium for leukemic cells of mice with L-glutamine (Quality Biological Inc., Gaithersburg, MD) supplemented with 10% heat-inactivated horse serum, pluronic F68 (0.1%), sodium pyruvate (1 mM), penicillin (100 U/ml), and streptomycin (100 μg/ml). The cultures were maintained in a humidified incubator with 5% CO2 in air at 37°C. Unless otherwise noted, all culture supplies were purchased from Invitrogen Life Technologies (Carlsbad, CA).

Selection of large and small colony Tk mutants and expansion of mutant cells

Large and small colony mutants were selected from a series of mutants isolated following AZT treatment [23]. An example of large and small colonies in selective medium in 96-well plates is shown in Figure 1. The mutants were selected based on the considerations described in the introduction. All mutants showed the same microsatellite LOH pattern (LOH at Tk and D11 Mit42 – see Figure 2) indicating that the chromosome alteration might occur at the Tk locus and extend up to 58.5 cM.

In addition, only mutants whose growth rates were relatively stable were chosen for microarray analysis of gene expression. The average doubling time for the large colony mutants was 9.6 ± 0.2 hours while the average doubling time for the small colony mutants was 17.5 ± 0.4 hours (p < 0.001, Student T-test).

RNA Isolation and cDNA Labeling

Total RNA was extracted from mutants whose cells were collected at log stage of growth using Qiagen RNeasy kits with on-column DNase digestion. The concentration and quality of RNA samples were measured by a spectrophotometer and an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA). All the RNA samples were stored at -80°C until used for microarray analysis.

The aminoallyl-indirect labeling protocol by The Institute for Genomic Research (TIGR, Rockville, MD) was followed with a few modifications. Briefly, 10 μg of total RNA was primed with 6 μg of random hexamer primers (Invitrogen, Carlsbad, CA) in a final volume of 16.5 μl. The RNA was reverse-transcribed in a 30 μl reaction containing 0.5 mM dATP, dCTP, dGTP, 0.3 mM dTTP (Invitrogen) and 0.2 mM aminoallyl-dUTP (aa-dUTP, Ambion), 40 U RNase inhibitor (Invitrogen), 400 U Superscript II (Invitrogen), 10 mM DTT and 1X first strand buffer at 42°C for 2 hours to generate aminoallyl-labeled cDNA. The purified aminoallyl-cDNA was coupled with either Cy3 or Cy5 monoreactive dyes (Amersham Pharmacia, Piscataway, NJ). Uncoupled dyes were removed by QIAquick PCR purification kit. cDNA yields and dye incorporation efficiencies were determined using a NanoDrop spectrophotometer. A reference design was used in which RNA sample from each large or small colony mutant was labeled with Cy5 and paired with Mouse Universal Reference RNA (Stratagene, La Jolla, CA) labeled with Cy3.

Hybridization and imaging

Pairs of Cy3 and Cy5 labeled cDNAs were dissolved in 60 μl of hybridization buffer (5× SSC, 0.1% SDS with 27% formamide) and incubated with a mouse 20 k oligonucleotide microarray that was fabricated in-house at the NCTR using oligonucleotides from MWG, Inc. (High Point, NC). The hybridization was performed in hybridization cassettes (ArrayIt, Sunnyvale, CA) in a water bath at 50°C for 16–18 hours. The slides were then washed in pre-warmed (30°C) 2X SSC (containing 0.1% SDS) for 5 minutes, 1X SSC for 5 minutes, and 0.5X SSC for 5 minutes. The hybridized slides were dried immediately by centrifugation and scanned with a GenePix 4000B scanner (Axon Instruments, Sunnyvale, CA) at 10 μm resolution. The resulting images were analyzed by measuring the fluorescence of all features on the slides using the GenePix Pro 6.0 software (Axon Instruments). The median fluorescence intensity of all the pixels within one feature was taken as the intensity value for each feature.

Data analysis

The raw data were imported into ArrayTrack (NCTR/FDA) and normalized using Total Intensity Normalization after subtracting backgrounds. Expression ratios (sample/universal RNA) from 8 arrays (4 large colony and 4 small colony mutant RNA samples) were log2-transformed. The logged data were used for principal component analysis (PCA), hierarchical cluster analysis (HCA), and statistical analysis for identifying genes that were differentially expressed in large versus small colony mutants. The Welch T-test was used for determining significant changes in gene expression between the large and small colony mutant groups. Significant genes were selected with a cut-off of p < 0.01 and fold change > 1.5. The selected genes were further analyzed for their functions and networks using Ingenuity Pathway Analysis (IPA, Ingenuity Systems Inc., Redwood City, CA). Information on genes' chromosome locations were obtained using Gene Library within ArrayTrack.


Principal component analysis and hierarchical clustering of the gene expression profiles

Genome-wide expression profiles for 8 Tk mutant samples were generated using the mouse oligonucleotide microarray. Principal components analysis (PCA) within ArrayTrack was used to carry out an examination of the relationship among the samples (Figure 3). A separation between large and small colony mutants was clearly observed. The large colonies are more tightly grouped together while the small colonies more loosely grouped. Hierarchical Cluster Analysis (HCA) using ArrayTrack also revealed distinct grouping of the mutant samples according to their colony size, suggesting that gene expression patterns are different between the large and small colony mutants (Figure 4).
Figure 3
Figure 3

Principal component analysis (PCA) 3D view for gene expression profiles from 8 large and small colony Tk mutant samples. The PCA is based on log2 ratios and the expression profiles are across all the 20,000 genes in the microarrays. The blue and red dots indicate large and small colony mutants, respectively. The first three principal components are plotted. The captured variances of PC1 (first principal component), PC2 (second principal component) and PC3 (third principal component; the label is not shown) were 25.5%, 17.4%, and 15.5%, respectively.

Figure 4
Figure 4

Hierarchical clustering of large and small colony mutant samples. Ward's Minimum Variance method was used. Clustering is based on log2 ratios and the expression profiles are across all the 20,000 genes in the microarrays.

Identification of differentially expressed genes between large and small colony Tk mutants

Welch T-test was used for determining significant changes in gene expression between large and small colony mutant groups. A volcano plot for differentially expressed genes between large and small colony mutants is shown in Figure 5. A group of 90 genes was found to be differentially expressed with a cutoff (p < 0.01 and fold change > 1.5). Among the 90 genes, 15 genes were up-regulated and 75 genes were down-regulated when the gene expression ratios in large colony mutants were compared to those in small colony mutants (large/small). A complete list of these genes can be found in Table 1. There were 431 genes whose p-value was smaller than 0.01 without fold-change cutoff and 1034 genes that have a fold-change greater than 1.5 without considering the p-value.
Figure 5
Figure 5

Volcano plot for differentially expressed genes between large and small colony mutants. The plot is based on log2 ratios and the expression profiles are across all the 20,000 genes in the microarrays. A gene is identified as significantly altered if the p-value is smaller than 0.01 and fold change is greater than 1.5. Red dots indicate significant genes; pink dots indicate genes that have p < 0.01 and fold changes < 1.5; yellow dots indicate genes that have p > 0.01 and fold change > 1.5; and black dots indicate genes that have p > 0.01 and fold change < 1.5.

Table 1

Genes differentially expressed between large and small colony mutants (p < 0.01; fold change > 1.5).

Locus ID

Gene Name

P Value

Fold Change






histocompatibility 47





protocadherin gamma subfamily C, 4





tudor domain containing 7





Ly6/neurotoxin 1





GTPase, very large interferon inducible 1





RIKEN cDNA 9230104L09 gene





nuclear factor I/B





acyl-CoA synthetase long-chain family member 1





cholinergic receptor, muscarinic 1, CNS










transformation related protein 53 inducible nuclear protein 1





RIKEN cDNA 2600001B17 gene





MOB1, Mps One Binder kinase activator-like 1A (yeast)





interferon dependent positive acting transcription factor 3 gamma





fibroblast growth factor receptor 1





DEAH (Asp-Glu-Ala-His) box polypeptide 34





hypothetical protein





spectrin beta 2





recombination activating gene 2





expressed in non-metastatic cells 2, protein





RIKEN cDNA 2310005N03 gene





Not found





Zinc fingerprotein 618





RIKEN cDNA C130090K23 gene





RIKEN cDNA 1700088E04 gene





guanylate nucleotide binding protein 4





protein tyrosine phosphatase 4a3





NADH dehydrogenase subunit 6





cyclin-dependent kinase inhibitor 2A





signal transducer and activator of transcription 1





metallophosphoesterase domain containing 2





neuraminidase 3





RIKEN cDNA 1300010M03 gene





interleukin 17 receptor D





selenoprotein P, plasma, 1





procollagen, type VI, alpha 1





EGF-like repeats and discoidin I-like domains 3





RIKEN cDNA A330103N21 gene





ras homolog gene family, member V





natriuretic peptide precursor type A





cytochrome b





cadherin 2





hexamethylene bis-acetamide inducible 1





protocadherin beta 4





RIKEN cDNA 2010317E24 gene





adducin 3 (gamma)





guanidinoacetate methyltransferase





Eph receptor A7





TAP binding protein





interferon activated gene 202B





retinol dehydrogenase 12





adenosine A3 receptor





LIM domain only 2





laminin, beta 2





protein tyrosine phosphatase, receptor type, C





RIKEN cDNA 1500001H12 gene





protein phosphatase 2, regulatory subunit B (B56), beta isoform





interferon activated gene 203





olfactory receptor 1457





E26 avian leukemia oncogene 1, 5' domain





transforming growth factor, beta receptor II





proline-rich Gla (G-carboxyglutamic acid) polypeptide 2





hexamethylene bis-acetamide inducible 1





small nuclear ribonucleoprotein D1





outer dense fiber of sperm tails 4





T-cell receptor gamma, constant region





tousled-like kinase 2 (Arabidopsis)





RIKEN cDNA 5830458K16 gene





histocompatibility 2, T region locus 24





sprouty homolog 4 (Drosophila)





homeo box C6





glycophorin A





GLIS family zinc finger 1










tribbles homolog 1 (Drosophila)





tripartite motif protein 30





X-linked lymphocyte-regulated complex





CD302 antigen





interferon-induced protein 35





AT rich interactive domain 5A (Mrf1 like)





chondroitin sulfate proteoglycan 2





G protein-coupled receptor, family C, group 5, member C





RIKEN cDNA 5730405I09 gene





sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3B





interferon regulatory factor 4





platelet derived growth factor receptor, alpha polypeptide





GATA binding protein 6

Note: Three unknown genes are not included. * indicates the gene is located on chromosome 11.

Pathway analysis of the significant genes

The selected genes were analyzed for their functions using IPA that is integrated with ArrayTrack. Among the 90 genes, 50 genes were mapped to the IPA database and were used for functional analysis. The 50 genes were mapped to four different networks in the IPA database (Table 2). The scores for all of the mapped networks were higher than 19, indicating that the networks selected were not due to random chance alone. (A score of 3 or greater was considered significant at p < 0.001 level). These networks were associated with the following pathways: gene expression, cell signaling, lymphatic system development and function, cellular growth and proliferation, cell death and cancer.
Table 2

List of Ingenuity networks generated by mapping the focus genes that were differentially expressed between large and small colony mutants.




Focus Genes

Top Functions


Add3, Adora3, Agt, Asf1b, Cdkn1b, Cdkn2a, Cks1b, Col6a1, Col6a2, Cspg2, Ctcf, Cyp11b2, Egfr, Eps8, Erbb2, Fbn1, Fgfr1, Gbp4, Gpc1, Hmga2, Il17rd, Irf4, Mell1, Nfatc2, Nfib, Nppa, Npr3, P4ha2, Ptrf, Rag2, Tgfbr2, Tlk2, Top1, Tp53inp1, Wap



Cellular Growth and Proliferation, Cancer, Cell Death


Acsl1, Angpt1, Btg1, Btg2, Ccnt1, Ccnt2, Cdk9, Cebpa, Chrm1, Dgkz, Efna1, Elf1, Epha7, Fbl, Grn, His1, Ifi203, Lamb2, Mapk1, Mmp17, Myc, Nme2, Oas1, Ptprc, Ptx3, Rasgrp1, Rn7sk, Rock1, Slpi, Spry4, Sytl1, Tapbp, Tdrd7, Tnf, Trib1



Gene Expression, Cellular Development, Cell-To-Cell Signaling and Interaction


Alox15, Btg1, Cdkn1a, Cks1b, Col6a1, Col6a2, Col6a3, Ctcf, Ebi3, Fgf13, Gata6, Gvin1, Ifi35, Ifi202b, Ifna, Ifne1, Ifng, Il15, Il12rb1, Il17f, Il27ra, Isgf3g, Mylk, Nd6, Ndufs4, Pdgfc, Pdgfra, S100a11, Sema3b, Sepp1, Stat1, Stat4, Tgfb1, Trim30, Znf467



Cancer, Cell Death, Cellular Growth and Proliferation


Casp3, Cckbr, Cd1d, Cdh2, Ctcf, Ctnna2, Ctnnb1, Ctnnd2, Cyp24a1, Edil3, Ets1, Gypa, Il2, Invs, Lmo2, Mdm4, Mgat5, Neu3, Nf2, Pag, Pitx2, Ppp2ca, Ppp2cb, Ppp2r2c, Ppp2r5b, Ptn, Ptp4a3, Ptprc, Scn5a, Slc4a1, Snrpd1, Sptbn1, Tbxas1, Tp53, Tuba1



Cellular Development, Hematological System Development and Function, Immune and Lymphatic System Development and Function

Note: * Bold genes are those mapped by the significant genes. # A score of 3 or greater was considered significant (p < 0.001)

Significant genes located on chromosome 11

The mouse lymphoma mutants are identified and isolated because they have mutation(s) that involve the Tk gene that is located at the distal end of chromosome 11. The mutation can occur solely within the Tk gene or it may also involve other genes on chromosome 11. While the underlying difference between the small and large colony mutants has remained elusive, it is clear that the slow growth of the small colony mutants must be caused by an alteration in the expression of one or more genes related to cell growth. It is expected that one or more genes located near the Tk gene on chromosome 11 may have differential expression in large and small colony mutants because of an alteration of the DNA in the region around the Tk gene. The Gene Library feature of ArrayTrack was used to identify the significant genes located on chromosome 11. Among 90 significant genes, 10 of them were found to be located on chromosome 11 (about 11%). Interestingly, all these genes had a higher gene expression in small colony mutants than in large colony mutants (Table 1 and Figure 6). Among the total genes (20,000) in the array, about 5.6% of them are located on chromosome 11 and no bias for up or down-regulation of genes was found.
Figure 6
Figure 6

Map for chromosome 11 genes whose expression was significantly altered. The position of Tk1 gene in red is also shown for comparison. The right ruler indicates distance in cM (centimorgan) from top of the chromosome.


The large and small colony Tk mutants differ in their growth kinetics. The cells from the small colony mutants grow slowly while the cells from large colony mutants grow at normal rates [5, 9]. The relative proportion of these two classes of mutants is mutagen dependent and relates to the clastogenic, anuploidogenic and recombinogenic potential of the chemical. Despite extensive molecular genetic and cytogenetic evaluation of a large number of mutants, the fundamental mechanistic difference(s) between the two phenotypes is not known. To explore the biological difference between the two types of Tk mutants, four large colony mutant samples with an average doubling time of 9.6 ± 0.2 hours and four small colony mutant samples with an average doubling time of 17.5 ± 0.4 hours were examined by microarray gene expression analysis.

PCA and HCA of the gene expression profiles from these mutant samples showed that the samples were clearly separated into two groups based on their phenotype of colony sizes. Given that both PCA and HCA were conducted using all of the genes on the microarray (20K) without filtering, this finding is very significant, indicating a high quality of the microarray experiment and strong biological relevance. The results can be interpreted as evidence for the biological similarity within each size group and biological differences between the two different size colony mutants. Also the results from PCA (Figure 3) showed that the gene expression patterns in the large colony group of mutants were very homogeneous while the expression patterns from the small colony group of mutants were relatively heterogeneous. These patterns reflect the variability of the colony size in the two groups. Generally, large colony mutants are similar in size to wild-type colonies; the growth rate of these cells is also similar. The growth rate of cells in the small colony mutants is slower and more variable than wild-type cells. The standard deviation of the growth rate of cells from the small colony mutant samples was greater than that for cells from the large colony mutants.

Expression of 90 genes was significantly altered between large and small colony mutants. When comparing the expression of large colony mutants to small colony mutants, 15 genes were up-regulated and 75 genes were down-regulated. This bias might result from the characteristics of these genes. Most of these genes are involved in regulation of growth. Twenty-two of 50 genes mapped into IPA database were involved in the regulation of apoptosis. They are Cdh2, Cdkn2A, Chrm1, Cspg2, Ets1, Fgfr1, Gata6, Grn, Ifi202b, Il17rd, Irf2, Lamb2, Neu3, Nppa, Pdgfra, Ptn, Ptprc, Rag2, Sema3b, Stat1, Tgfbr2, and Tp53inp1. Expression of 18 genes among them was higher in small colony mutants than large colony mutants. This pattern of expression suggests that more apoptosis occurred in the small colony mutants, leading to the slow growth of the cells.

Consistent with the growth rate phenotype is the finding that nearly all significant genes that can be found in IPA database were mapped into networks that were related to cell growth (see Table 2).

Genes of particular interest in this group are recombination activating gene 2 (Rag2) and cyclin-dependent kinase inhibitor 2A (Cdkn2a). Because a large portion of Tk mutants may result from recombinational events, we have proposed that the mouse lymphoma cells have a high recombinase activity. The Rag1 and Rag2 genes are necessary for V(D)J recombination [24, 25]. Also, Rag2 is necessary for lymphocyte maturation and growth [26, 27]. A 2.21-fold lower activity of Rag2 gene expression in small colony mutant (Table 1) would restrict the cell growth of small colony mutants. Because the large colony cells grow at a similar growth rate as the wild type mouse lymphoma cells, the high expression of Rag2 in large colony cells might suggest that mouse lymphoma cells have an elevated level of the recombinase activity and these cells might be particularly sensitive to recombination and therefore particularly effective in detecting chemicals that induce recombination. Cdkn2a is known to be an important tumor suppressor gene [28] and generates several transcript variants that function as inhibitors of CDK4 kinase [28, 29]. This protein also sequesters MDM1, a protein involved in the degradation of p53, and thus may serve to stabilize p53 [30]. Therefore, this protein functions in cell cycle control and it is frequently mutated or deleted in a wide variety of tumors [3135]. A 2.02-fold higher expression activity of this tumor suppressor gene in small colony mutants (Table 1) would favor a slower growth of those cells than the large colony cells.

Of particular interest, the significant genes were disproportionally distributed in the vicinity of the Tk gene on chromosome 11 with a higher expression level in small mutant colony mutants than in large colony mutants. Ten of the significant genes were located on chromosome 11. However, if the genes were distributed on the chromosomes proportionally, there would have been about 2 genes with up-regulation of expression in the small colony mutants on chromosome 11. Moreover, 7 of the 10 genes were located within 20 cM of the Tk gene (Figure 6). This gene distribution pattern may be explained by chromosomal location rather than by gene function, suggesting a regional change in gene expression. Further, this suggests that the type of DNA alteration that inactivates the Tk gene in the small colony mutants is responsible for this regional change in gene activity. Although the microsatellite LOH pattern in the D and E bands of chromosome 11 (Figure 2 and Figure 6) was the same for all the mutants, the gene expression in this region appeared significantly different between the large and small colony mutants.


The PCA and HCA showed that the gene expression profiles from the mutant samples were clearly separated into two groups based on their colony sizes. This cluster pattern indicates biological similarity within each mutant group and biological difference between the two different colony size mutants. Statistical and functional analysis of the profiles identified a set of genes whose expression was differentially altered between large and small colony mutants. Most of these genes are responsible for regulation of cell growth that would be expected to influence the cell growth rate and the colony sizes. In addition, we found a number of significant genes that were disproportionally concentrated on an area of chromosome 11 where there was loss of heterozygosity. These findings suggest that the Tk chromosome mutations in the small Tk colony mutants result in alterations in gene expression for genes located near the Tk gene. This same altered gene expression does not occur in the large colony mutants. This finding is particularly interesting because the microsatellite LOH analysis indicates no difference between the small and large colony mutants. Our analysis demonstrates that the utility of microarray analysis using ArrayTrack provides a gene level analysis of the two cellular phenotypes. Using this tool, we have identified genes that appear to be related to the colony size phenotype. The analysis of additional mutants would be expected to provide further information and may elucidate the underlying difference between the small and large colony Tk mutants.
Table 3

Location and function of the significant genes located on chromosome 11.

Gene Name


Gene product and function



Hypothetical protein LOC268490 with unknown function



Hypothetical protein LOC67998 with unknown function



Interferon-induced protein with unknown function



Cleavage and polyadenylation factor CF I component involved in pre-mRNA 3'-end processing



G protein-coupled receptor, family C, group 5, member C with unknown function



Granulin that acts as inhibitors, stimulators, or have dual actions on cell growth.



Expressed in non-metastatic cells 2, protein involves in cell proliferation.



Outer dense fiber of sperm tails 4 with unknown function.



Spectrin involving disruption, proliferation of cells.



Tousled-like kinase 2 that function as regulation of chromatin assembly or disassembly; protein amino acid phosphorylation; response to DNA damage stimulus; cell cycle; intracellular signaling cascade; chromatin modification



The authors thank Mr. William S. Branham for fabricating the high-quality microarrays used in this study. The views presented in this article do not necessarily reflect those of the Food and Drug Administration.

Authors’ Affiliations

Division of Systems Toxicology, National Center for Toxicological Research, U.S. FDA, Jefferson, USA
Division of Genetic and Reproductive Toxicology, National Center for Toxicological Research, U.S. FDA, Jefferson, USA


  1. Dearfield KL, Auletta AE, Cimino MC, Moore MM: Considerations in the U.S. Environmental Protection Agency's testing approach for mutagenicity. Mutat Res 1991, 258: 259–283.View ArticlePubMedGoogle Scholar
  2. Department of Health and Human Services: Genotoxicity: A Standard Battery for Genotoxicity Testing of Pharmaceuticals. International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use. Food and Drug Administration, Rockville, MD; 1997.Google Scholar
  3. Muller L, Kikuchi Y, Probst G, Schechtman L, Shimada H, Sofuni T, Tweats D: ICH-harmonised guidances on genotoxicity testing of pharmaceuticals: evolution, reasoning and impact. Mutat Res 1999, 436(3):195–225. 10.1016/S1383-5742(99)00004-6View ArticlePubMedGoogle Scholar
  4. Chen T, Moore MM: Screening for chemical mutagens using the mouse lymphoma assay. In Optimization in Drug Discovery: In-vitro Methods. Edited by: Yan Z, Caldwell GW. Totowa, New Jersey: Humana Press; 2004:337–352.View ArticleGoogle Scholar
  5. Moore MM, Clive D, Howard BE, Batson AG, Turner NT: In situ analysis of trifluorothymidine-resistant (TFTr) mutants of L5178Y/TK+/- mouse lymphoma cells. Mutat Res 1985, 151(1):147–159.View ArticlePubMedGoogle Scholar
  6. Moore MM, Brock KH, Doerr CL, DeMarini DM: Mutagenesis of L5178Y/TK(+/-)-3.7.2C mouse lymphoma cells by the clastogen ellipticine. Environ Mutagen 1987, 9(2):161–170.View ArticlePubMedGoogle Scholar
  7. Chen T, Harrington-Brock K, Moore MM: Mutant frequency and mutational spectra in the Tk and Hprt genes of N-ethyl-N-nitrosourea-treated mouse lymphoma cellsdagger. Environ Mol Mutagen 2002, 39(4):296–305. 10.1002/em.10075View ArticlePubMedGoogle Scholar
  8. Chen T, Harrington-Brock K, Moore MM: Mutant frequencies and loss of heterozygosity induced by N-ethyl-N-nitrosourea in the thymidine kinase gene of L5178Y/TK(+/-)-3.7.2C mouse lymphoma cells. Mutagenesis 2002, 17(2):105–109. 10.1093/mutage/17.2.105View ArticlePubMedGoogle Scholar
  9. Moore MM, Clive D, Hozier JC, Howard BE, Batson AG, Turner NT, Sawyer J: Analysis of trifluorothymidine-resistant (TFTr) mutants of L5178Y/TK+/- mouse lymphoma cells. Mutat Res 1985, 151(1):161–174.View ArticlePubMedGoogle Scholar
  10. Hozier J, Sawyer J, Clive D, Moore MM: Chromosome 11 aberrations in small colony L5178Y TK-/- mutants early in their clonal history. Mutat Res 1985, 147(5):237–242.View ArticlePubMedGoogle Scholar
  11. Applegate ML, Moore MM, Broder CB, Burrell A, Juhn G, Kasweck KL, Lin PF, Wadhams A, Hozier JC: Molecular dissection of mutations at the heterozygous thymidine kinase locus in mouse lymphoma cells. Proc Natl Acad Sci USA 1990, 87(1):51–55. 10.1073/pnas.87.1.51PubMed CentralView ArticlePubMedGoogle Scholar
  12. Moore MM, Doerr CL: Comparison of chromosome aberration frequency and small-colony TK-deficient mutant frequency in L5178Y/TK(+/-)-3.7.2C mouse lymphoma cells. Mutagenesis 1990, 5(6):609–614.View ArticlePubMedGoogle Scholar
  13. Hozier J, Sawyer J, Moore M, Howard B, Clive D: Cytogenetic analysis of the L5178Y/ TK +/- leads to TK -/- mouse lymphoma mutagenesis assay system. Mutat Res 1981, 84(1):169–181.View ArticlePubMedGoogle Scholar
  14. Liechty MC, Scalzi JM, Sims KR, Crosby H Jr, Spencer DL, Davis LM, Caspary WJ, Hozier JC: Analysis of large and small colony L5178Y tk-/- mouse lymphoma mutants by loss of heterozygosity (LOH) and by whole chromosome 11 painting: detection of recombination. Mutagenesis 1998, 13(5):461–474.View ArticlePubMedGoogle Scholar
  15. Honma M, Momose M, Sakamoto H, Sofuni T, Hayashi M: Spindle poisons induce allelic loss in mouse lymphoma cells through mitotic non-disjunction. Mutat Res 2001, 493(1–2):101–114.View ArticlePubMedGoogle Scholar
  16. Weinert TA, Hartwell LH: The RAD9 gene controls the cell cycle response to DNA damage in Saccharomyces cerevisiae. Science 1988, 241(4863):317–322.View ArticlePubMedGoogle Scholar
  17. Lock RB, Ross WE: Inhibition of p34cdc2 kinase activity by etoposide or irradiation as a mechanism of G2 arrest in Chinese hamster ovary cells. Cancer Res 1990, 50(12):3761–3766.PubMedGoogle Scholar
  18. Kowara R, Karaczyn A, Cheng RY, Salnikow K, Kasprzak KS: Microarray analysis of altered gene expression in murine fibroblasts transformed by nickel(II) to nickel(II)-resistant malignant phenotype. Toxicol Appl Pharmacol 2005, 205(1):1–10. 10.1016/j.taap.2004.10.006View ArticlePubMedGoogle Scholar
  19. Nishizuka S, Winokur ST, Simon M, Martin J, Tsujimoto H, Stanbridge EJ: Oligonucleotide microarray expression analysis of genes whose expression is correlated with tumorigenic and non-tumorigenic phenotype of HeLa × human fibroblast hybrid cells. Cancer Lett 2001, 165(2):201–209. 10.1016/S0304-3835(01)00437-2View ArticlePubMedGoogle Scholar
  20. Ciro M, Bracken AP, Helin K: Profiling cancer. Curr Opin Cell Biol 2003, 15(2):213–220. 10.1016/S0955-0674(03)00007-3View ArticlePubMedGoogle Scholar
  21. Hanash S: Integrated global profiling of cancer. Nat Rev Cancer 2004, 4(8):638–644. 10.1038/nrc1414View ArticlePubMedGoogle Scholar
  22. Tong W, Harris S, Cao X, Fang H, Shi L, Sun H, Fuscoe J, Harris A, Hong H, Xie Q, et al.: Development of public toxicogenomics software for microarray data management and analysis. Mutat Res 2004, 549(1–2):241–253. []View ArticlePubMedGoogle Scholar
  23. Wang J, Chen L, Chen T, Moore MM: Molecular analysis of spontaneous and 3'-azido-3'-deoxythymidine – induced L5178y Tk -/- mouse lymphoma cell mutants. The Toxicologist 2005, 84(S-1):454.Google Scholar
  24. Knecht H, Brousset P, Bachmann E, Pallesen G, Odermatt BF: Expression of human recombination activating genes (RAG-1 and RAG-2) in lymphoma. Leuk Lymphoma 1994, 15(5–6):399–403.View ArticlePubMedGoogle Scholar
  25. Oettinger MA, Schatz DG, Gorka C, Baltimore D: RAG-1 and RAG-2, adjacent genes that synergistically activate V(D)J recombination. Science 1990, 248(4962):1517–1523.View ArticlePubMedGoogle Scholar
  26. Ohmori H, Hikida M: Expression and function of recombination activating genes in mature B cells. Crit Rev Immunol 1998, 18(3):221–235.View ArticlePubMedGoogle Scholar
  27. Swat W, Shinkai Y, Cheng HL, Davidson L, Alt FW: Activated Ras signals differentiation and expansion of CD4+8+ thymocytes. Proc Natl Acad Sci USA 1996, 93(10):4683–4687. 10.1073/pnas.93.10.4683PubMed CentralView ArticlePubMedGoogle Scholar
  28. Herzog CR, You M: Sequence variation and chromosomal mapping of the murine Cdkn2a tumor suppressor gene. Mamm Genome 1997, 8(1):65–66. 10.1007/s003359900352View ArticlePubMedGoogle Scholar
  29. Biden K, Young J, Buttenshaw R, Searle J, Cooksley G, Xu DB, Leggett B: Frequency of mutation and deletion of the tumor suppressor gene CDKN2A (MTS1/p16) in hepatocellular carcinoma from an Australian population. Hepatology 1997, 25(3):593–597. 10.1002/hep.510250317View ArticlePubMedGoogle Scholar
  30. Stott FJ, Bates S, James MC, McConnell BB, Starborg M, Brookes S, Palmero I, Ryan K, Hara E, Vousden KH, et al.: The alternative product from the human CDKN2A locus, p14(ARF), participates in a regulatory feedback loop with p53 and MDM2. Embo J 1998, 17(17):5001–5014. 10.1093/emboj/17.17.5001PubMed CentralView ArticlePubMedGoogle Scholar
  31. Bortolotto S, Chiado-Piat L, Cavalla P, Bosone I, Chio A, Mauro A, Schiffer D: CDKN2A/p16 inactivation in the prognosis of oligodendrogliomas. Int J Cancer 2000, 88(4):554–557. 10.1002/1097-0215(20001115)88:4<554::AID-IJC6>3.0.CO;2-QView ArticlePubMedGoogle Scholar
  32. Bortolotto S, Chiado-Piat L, Cavalla P, Bosone I, Mauro A, Schiffer D: CDKN2A/p16 in ependymomas. J Neurooncol 2001, 54(1):9–13. 10.1023/A:1012537105775View ArticlePubMedGoogle Scholar
  33. Hashemi J, Platz A, Ueno T, Stierner U, Ringborg U, Hansson J: CDKN2A germ-line mutations in individuals with multiple cutaneous melanomas. Cancer Res 2000, 60(24):6864–6867.PubMedGoogle Scholar
  34. Schlegel J, Piontek G, Kersting M, Schuermann M, Kappler R, Scherthan H, Weghorst C, Buzard G, Mennel H: The p16/Cdkn2a/Ink4a gene is frequently deleted in nitrosourea-induced rat glial tumors. Pathobiology 1999, 67(4):202–206. 10.1159/000028073View ArticlePubMedGoogle Scholar
  35. Suzuki H, Zhou X, Yin J, Lei J, Jiang HY, Suzuki Y, Chan T, Hannon GJ, Mergner WJ, Abraham JM, et al.: Intragenic mutations of CDKN2B and CDKN2A in primary human esophageal cancers. Hum Mol Genet 1995, 4(10):1883–1887.View ArticlePubMedGoogle Scholar


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