Long-term global gene expression patterns in irradiated human lymphocytes

Susann Fält1,3, Kerstin Holmberg1,2, Bo Lambert1 and Anders Wennborg1

1 Unit of Environmental Medicine, Center for Nutrition and Toxicology, Department of Biosciences at Novum, Karolinska Institutet, S-14157 Huddinge, Sweden and 2 Department of Clinical Genetics Karolinska Hospital, S-17176 Stockholm, Sweden


    Abstract
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Radiation-induced chromosomal instability has many features in common with genomic instability of cancer cells. In order to understand the delayed cellular response to ionizing radiation we have studied variations in the patterns of gene expression in primary human lymphocytes at various time points after gamma irradiation in vitro. Cells either exposed to 3 Gy of gamma rays in vitro or unexposed were subjected to long-term growth in bulk culture or as individual T-cell clones. Samples were taken at days 7, 17 or 55 from bulk cultures. The T-cell clones were harvested after 22–46 days. Total RNA was used to generate cDNA probes for hybridization to oligonucleotide arrays containing 12,625 gene templates (Affymetrix). The results showed that: (i) irradiation as well as culture time influence the gene expression patterns, (ii) the number of genes with increased or decreased expression in irradiated cells increases dramatically with increasing culture time, (iii) the changes of gene expression showed a significantly more diversified pattern in the irradiated T-cell clones than in non-irradiated clones. We conclude that the diversification of the transcriptome associated with radiation exposure reflects subtle changes of expression in many genes, rather than being the result of major changes in a few genes. Finally, (iv) we sorted out a set of genes whose change of expression correlates with radiation exposure in both bulk cultures and T-cell clones. Very few of these genes overlap with genes that change during the acute response to radiation. This set of genes may be regarded as a starting point for further studies of the cellular phenotype associated with radiation-induced genomic instability.

Abbreviations: MAS, Micro Array Suite Software


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Ionizing radiation has become a widely used tool for studying cellular and molecular responses to DNA damage. Genotoxic stress induced by ionizing radiation as well as by alkylating agents, triggers the activation of immediate stress response pathways resulting in the altered expression of a number of genes (1).

The acute changes in the expression of ionizing radiation-responsive genes have been studied extensively (1,2). Most changes appear to be temporary and transient, leading to cell-cycle arrest in G1 as well as G2, and to the activation of repair pathways or apoptosis. Although the details of these pathways have not yet been fully elucidated, several intermediate steps have been defined (1). The most likely triggers for the acute cellular responses to ionizing radiation are DNA damage due to oxygen radicals, and lipid peroxidation products (3).

Cells surviving the acute genotoxic stress have been shown to display delayed responses that can result in genomic instability and other persistent effects including: increased frequency of micronuclei (4,5), gene mutation (6), decreased clone forming ability (7), apoptosis (5) and neoplastic transformation (8). Attempts have been made to separate phenotypes associated with genomic instability into several different categories on the basis of endpoints and possible mechanisms, e.g. point mutations, aneuploidy, chromosomal translocations and gene amplification (9). However, the mechanisms responsible for these delayed effects in radiation-exposed cells are largely unknown. Because of the high percentage of cells that develop genomic instability after irradiation, it is unlikely that mutations in a few specific genes are the main cause of these effects. It seems more likely that changes in e.g. the expression of genes that are involved in replication and growth control are responsible for the instability. One aspect of genomic instability is the so called bystander effect, which can be observed as genetic alterations in cells that are not themselves irradiated but are in the neighbourhood of irradiated cells. The mechanism(s) underlying bystander effects have been the subject of several investigations, implicating gap junction intercellular communication (10) and secreted factors (1113).

We have shown previously that in primary human T-lymphocytes, ionizing radiation induces genomic instability expressed as an increased frequency of chromosomal aberrations many generations after the exposure (14,15). To reach a deeper knowledge of mechanisms and pathways involved in this delayed response to irradiation, we have performed a detailed comparison of gene expression patterns in irradiated and non-irradiated cells during long-term culture. The use of high-density oligonucleotide DNA micro-array has made it possible to study thousands of genes involved in various biologic functions (16). In this study we have used Affymetrix oligonucleotide arrays to identify genes whose expression may change at late time points after irradiation in {gamma}-irradiated lymphocyte bulk cultures and individual cell clones.


    Materials and methods
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Cells and {gamma}-irradiation
Peripheral blood mononuclear cells were obtained from freshly collected buffy coats by separation in Ficoll-Paque (Amersham Pharmacia Biotech AB, Sweden). Two separate experiments from different blood donors were performed for the bulk cultures. A third donor contributed to the cloning experiment. The cells were counted and resuspended in 100 ml phospho buffer saline (PBS) and transferred to two 50 ml Falcon tubes. One of the tubes was exposed to 3 Gy using a 137Cs-source at a dose rate of 282 Gy/h. The control cells were treated in the same way except for the irradiation step.

Long-term cell culturing
Bulk cultures
After irradiation, the cells were counted, centrifuged and resuspended in basic medium (BM) including 1% phytohaemagglutinin (PHA) (Gibco, Invitrogen, UK) (BM, which is RPMI 1640, Dutch modification, 2 mM L-glutamine, 1500 U/ml penicillin, 1500 µg/ml Streptomycin and 10% heat-inactivated foetal bovine serum, all from Gibco). Cells were incubated in 75 cm2-flasks (Corning, Corning, NY) in an upright position in a final cell concentration of 1 x 106 cells/ml. The cells were counted and part of the medium was changed every third day, to keep the cell concentration at approximately the same level. After day 3 the basic media was successively exchanged by growth medium (GM) (same as BM except for 20% conditioned medium, 5% foetal bovine serum, 5% heat-inactivated human serum and 0.3% PHA) and the cell concentration was kept at 0.5 x 106/ml.

Individual clones
The procedure for T-cell cloning was adopted from (17) with minor modifications according to (18). Irradiated and non-irradiated cells were resuspended in GM-medium and distributed into 96-well round bottom microtitre plates. Each well received 2–5 irradiated or 1–2 non-irradiated cells and 25,000 40 Gy-irradiated RJK 853 feeder cells. After 2 weeks, growing clones were scored and expanded individually. Cells were transferred from one well to a 96-well microtitre plate with 10,000 lethally irradiated feeder cells/well, and then to 25 cm2 flasks in GM-medium at a cell concentration of 0.5 x 106/ml.

RNA isolation, probe preparation and hybridization
Lymphocytes (20–50 x 106) were collected for RNA isolation from bulk cultures at 7 and 17 days for experiment 1 and for experiment 2 at 17 and 55 days. Cells from individual clones were collected at 22–46 days and stored in -20°C until RNA extraction was suitable. Cells were washed in PBS for isolation of total RNA. Total RNA was isolated with Rneasy Mini Kit (Qiagen, Valencia, CA). The RNA was checked qualitatively, on a 1% agarose gel, and quantitatively by spectrophotometry.

Sample preparation for hybridization was performed according to the recommendations from Affymetrix. Total RNA (8 µg) was used to generate first-strand cDNA by using a T7-linked oligo (dT) primer. After second-strand synthesis, in vitro transcription was performed with biotinylated UTP and CTP (Enzo Diagnostics, Farmingdale, NY). Biotinylated RNA (20 µg) was fragmented to 50–150-nt size and 15 µg of fragmented cRNA were hybridized overnight to Affymetrix (Santa Clara, CA) HG-U95Av2 arrays including 12,625 sequences. Arrays were subsequently developed with phycoerythrin-conjugated streptavidin and biotinylated antibody against streptavidin, and scanned to obtain quantitative gene expression levels.

Data analysis
Image analysis
For the pair-wise, probe overlap and marker analyses, image analysis was performed with Affymetrix Micro Array Suite Software (MAS) version 5.0 to analyse the scanned images, convert intensities to a numerical format and obtain a detection call. This call indicates whether a transcript is reliably detected (present) or not detected (absent). Additionally, a signal value is calculated for each probe on the array, which assigns a relative measure of abundance to the transcript. Target intensity values from each array were scaled/normalized to a value of 100. A detailed description of the statistical algorithms is presented in (19).

Pair-wise comparison
MAS 5.0 were used for pair-wise comparisons in both bulk cultures and individual clones. During a comparison analysis, each probe set on the experiment array is compared with its counterpart on the baseline array, and a change P-value is calculated indicating a difference call: ‘increase’, ‘marginal increase’, ‘decrease’, ‘marginal decrease’ or ‘no change’ in gene expression. The filters used were the same in all comparisons. A detection call, ‘present’ or ‘absent’ transcript, for each probe set was assigned. Then, probe signals that were ‘absent’, not detected, in both base line sample and experiment sample were excluded. Secondly, comparisons with a ‘no change’ call were removed. The third metric to sort gene expression data was to sort on the relative change, fold change, in transcript abundance. MAS 5.0 use ‘Signal Log Ratio’. A fold change of 2 for increases or decreases corresponds to a Signal Log Ratio of 1 or -1, respectively. In all presentation of analyses in the Results section, ‘2 fold’ corresponds to >=2-fold change.

Overlap between probe set
To identify the overlap of changed probes between comparisons in both bulk cultures and single clones, a custom- developed database in Microsoft Access was used.

Marker analysis
Marker analysis was performed using GeneCluster 2 (20) to identify genes correlated with particular class distinction, irradiated versus non-irradiated. First, the gene expression data were subjected to a variation filter that excluded genes showing minimal variation across the samples. We used the default settings for the filtering procedure as follows: genes were excluded if they exhibited <3-fold (max/min) and 100 U (max - min) absolute variation across the dataset after a threshold of 20 U and a ceiling of 16,000 U were applied. The ceiling of 16,000 was chosen because it is at this level that saturation of the scanner is observed; values above this cannot be reliably measured. The threshold of 20 U was set so to avoid missing any potentially informative marker genes. The dataset was normalized by standardizing each row (gene) to mean = 0 and variance = 1. To compare neighbours in the marker analysis, a class template, assigning class belongings irradiated or non-irradiated, was given. The number of markers to be considered for correlation is chosen by the user, 20 markers for each class were chosen for this analysis. The gene ranking method Signal to Noise was selected, which identifies the difference of means in each of the classes scaled by the sum of the standard deviations: (µ1 - µ2)/({sigma}1 + {sigma}2) where µ1 is the mean of class 1 and {sigma}1 is the standard deviation of class 1. The Signal to Noise statistics assign a lower ranking score to genes that have higher variance in each class more than those genes that have a high variance in one class and a low variance in another. The marker gene most correlated to one class will receive the best Signal to Noise score. An alternative method, t-test [], was used as a complement to derive the ranking score.

From probe identity to gene identity
For gene identification and annotation we used GeneWeaver beta 3 version (Affibody AB), which maps the Affymetrix probe identifiers to gene identities in gene databases through the corresponding accession numbers. All results were mapped to the Human UniGene Build 146 and the Human LocusLink (dated 2002–01–28) databases. As a rule, the number of genes identified by a given number of probes is approximately 4/5, e.g. 100 probes tend to correspond to roughly 80 genes. This is due to the fact that more than one probe on the Affymetrix array may identify the same gene, as defined in a given version of a gene database, and also that some of the probe identifiers have associated accession numbers, which are not listed in the database. Therefore, quantitative comparative analyses of expression levels were performed at the probe level, while qualitative comparisons of the number of identified differences between samples was done both at the probe level and at the gene level, as described in the Results section. First, analyses were performed on all probes that passed a filtering criterion of ‘not no change’ in their difference call, thus excluding all probes without change in the comparisons according to the pair-wise analysis in MAS 5.0.

Secondly, probe lists with the properties ‘all changed, increased or decreased’, ‘>=2-fold-change, increased or decreased’ and ‘>=4-fold-change, increased or decreased’, respectively, were obtained and the number of probes and genes in common between the groups were identified.


    Results
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Gene expression profiles in long-term cultured T-lymphocyte bulk cultures
We used Affymetrix oligonucleotide arrays, complementary to 12,625 sequences, to analyse the expression profile in long-term cultured T-cell bulk cultures of irradiated and non-irradiated cells. Gene expression studies were performed on total RNA that was isolated from two separate cultures from different donors. In one experiment the cultures were sampled at culture days 7 and 17, while in the other samples were collected at days 17 and 55.

Expression analysis showed that an average of ~39% (4900 ± 353) of transcripts were scored as present in the untreated bulk cultures, while in the irradiated samples an average of ~42% or 5329 ± 199 transcripts were present.

Pair-wise comparisons were made between all samples in one experiment at a time as shown in Figure 1A and B. These comparisons allow a dissection of the different patterns of global gene expression changes related to the irradiation event and the progression of the in vitro culture. The number of changed probes and genes (scored as either up- or down-regulated) in cells sampled from different cultures and at different time points will depend on the criteria chosen for scoring. A high threshold (>2-fold) will result in fewer indicated genes, but lower risk of false positives, while a low threshold (<2-fold) leads to the opposite considerations. Regardless of the threshold selected, two major trends were observable in the data. First, an increasing number of probes were differently expressed between the irradiated and non-irradiated cultures with increasing time of culture. With the criteria ‘2-fold or more’: data from experiment 1 showed that 22 probes were changed at day 7, but 285 at day 17. In experiment 2, 50 were changed at day 17, but 566 at day 55 (A1 and A2 in Figure 1A and B). Secondly, a larger number of probes were consistently changed with culture time than with irradiation status (compare A1 and A2 with B1 and B2 in Figure 1A and B).



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Fig. 1. Venn diagram representing the number of changed probes in bulk cultures, (A) experiment 1 at days 7 and 17, (B) experiment 2 at days 17 and 55. Pair-wise comparisons to identify probes were performed as in Materials and methods. Common probes were increased/increased or decreased/decreased in both pair-wise comparisons.

 
Using the ‘2-fold or more’ change criterion, the overlap in experiment 1 identified one single probe as consistently changed by irradiation at both days 7 and 17. In experiment 2, the number of overlapping irradiation related probes at days 17 and 55 were 26. Gene expression changes related to culture time in both irradiated and control cultures were 228 in experiment 1 and 139 in experiment 2. Thus, the effect of culture time in itself was manifested in consistent expression changes of more genes than was the case for irradiation.

Both cultures were sampled at 17 days, and these samples can therefore be considered as duplicates of one time point from different donors. The gene expression pattern differed largely between the two samplings. Accepting any change for a threshold, 75 probes were found to change with irradiation in both experiments (data not shown). When ‘2-fold or more’ was used as a threshold, only one probe, cd 4 antigen (probe id. 1146_at) was found to overlap between the two 17-day samples. It is notable, that in a direct pair-wise comparison of the two 17-day control cultures, as many as 30% (1742) of the transcripts scored as present displayed differences in gene expression at the ‘any change’ level (data not shown), indicating a considerable heterogeneity between cultures, even though the samples were taken at the same time point.

Given the availability of a set of comparisons at several different time points, it was of interest to investigate if there was a consistent pattern of change in the gene expression across all comparisons that could serve as a marker for the irradiation status, regardless of culture time. The number of probes changed at all time points were limited to 3, all of which showed increased expression and were related to cell–cell communication. Colony stimulating factor 2 receptor (probe id. 37493_at) and class I cytokine receptor (probe id. 38894_g_at) are both cytokine receptors and function in signal transduction and immune response. Small inducible cytokine subfamily C (probe id. 31495_at, 31496_g_at), member 2 acts as a cytokine in lymphocytes.

However, if a demand for at least a 2-fold change was used, no single gene was affected in all comparisons. Thus, there seems not to be one single gene, which is dramatically affected in all samples, but the effect is more subtle and not immediately apparent in the complex mixture of cell clones represented by the bulk cultures.

Gene expression profiles in individually expanded clones
In order to assess the clonal variability in gene expression patterns, gene expression analysis was performed on eight individual T-cell clones derived from primary human lymphocytes, which had been cultured between 22 and 46 days. The cells originated from the same donor and were irradiated at the same occasion. Four clones were from 3 Gy irradiated cells and four control clones from non-irradiated cells. Expression analysis showed that an average of ~43% (5419 ± 330 for non irradiated and 5380 ± 484 for irradiated) of transcripts were scored as present in both irradiated and non-irradiated clones.

With the hypothesis that irradiated cell clones may exhibit an intrinsically less stable genome, reflected in a more diversified transcriptome during clonal evolution in culture, we wished to assess the total number of changed genes within the two sample groups. First, we wanted to identify the number of probes that differed by a given measure within each group of clones. We used GeneCluster 2 to assess the overall signal variation for each probe across the samples within each group. Probes that exhibit variability will pass a filter measuring the ratio of the strongest to the weakest signal among the samples (max/min). The variation filter was the same as in the marker analysis (see Materials and Methods), except that we used a series of different numbers for max/min values: 1.5-, 2-, 3- and 5-fold. The probes that passed the filter were consistently more than twice as many in the irradiated clone group compared with the non-irradiated clones, as shown in Table I, comparing the number of changed probes at each max/min value. Taken together, the overall gene expression pattern appears to be clearly more diversified among the irradiated clones than the control clones.


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Table I. Expression variation within the irradiated (Xi) and non-irradiated (Ni) clone group

 
Secondly, we performed pair-wise comparisons between each sample to assess outlier clones that possibly could skew the data in either group. For this analysis the MAS was used, to count only ‘increased’, ‘marginal increased’ and ‘decreased’, ‘marginal decreased’ probes. Comparisons were performed, irradiated versus irradiated and non-irradiated versus non-irradiated, and the number of changed probes was counted.

When plotting the single data points for each comparison we observe a trend in the same direction as in the group comparisons, although one of the irradiation versus irradiation comparisons showed a lower number of changed probes than the majority of comparisons in this group (Figure 2). Thus, also this different approach based on pair-wise comparisons, indicates a larger heterogeneity in gene expression among the irradiated clones. The results were verified with a Mann–Whitney U-test, showing a significant difference at the 5% level.



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Fig. 2. Pair-wise comparisons of irradiated and non-irradiated clones. Diagram showing the number of changed probes passing an ‘any change’ criterion in pair-wise comparisons performed within each clone-group: irradiated versus irradiated (Xi) and non-irradiated versus non-irradiated (Ni). Observe that the y-axis starts at 900.

 
Marker gene analysis
In order to identify genes that were consistently differentially expressed between the two sample groups a marker analysis was performed with the software GeneCluster 2 (20). Of 12,625 expression values considered, 743 passed the variation filter and were used for marker analysis, using the ranking method Signal to Noise. Figure 3 shows the 20 best-correlated genes for each class, which were chosen for further consideration. Using the t-test statistics as ranking method, eight additional genes (six up-regulated and two down-regulated) were identified (data not shown). The combined data set of 26 up-regulated and 22 down-regulated marker genes were used for further analyses.



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Fig. 3. Results from a marker gene analysis, where the 20 genes best-correlated with either irradiated or non-irradiated clones are identified. The gene ranking method Signal to Noise is used. The matrix depicts the gene expression values of the individual samples, with columns representing individual clones and rows representing individual genes. Genes are ranked in a ‘best-correlated’ order. The colour scale identifies relative gene expression changes normalized by the SD, with 0 representing the mean expression level of a given gene across the panel. Gene names in parentheses were not identified with the t-test statistics.

 
Genes common in bulk culture and individual clones
Genes that were changed in both bulk cultures and clone groups were identified (Tables II and III). The comparisons were performed on two levels. First, probe identities for increased or decreased genes in both bulk cultures and clones were linked and common probe numbers were identified. At this stage only genes with the same probe identity in both cultures can be verified while genes that differ on the probe identity level will not be detected. In order to identify genes with different probe identity in the cultures the GeneWeaver software were used to link probe identities to the common UniGene clusters in the UniGene and LocusLink databases, where the gene identity could be verified. This two-step procedure was found to be necessary to verify all the genes that were common in both bulk cultures and individual clones.


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Table II. Analysis of probes that are up- or down-regulated in both bulk cultures and clones

 

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Table III. Gene identity of overlap in bulk culture and clones

 
The number of genes that overlapped was increasing with longer culture time for the bulk cultures: from three genes at day 7 to 14 at day 55. This trend was also seen when the overlap between bulk cultures and the 100 highest ranked marker genes where determined (data not shown). A number of the identified marker genes were changed in up to three of the four bulk comparisons: 2'-5'-oligoadenylate synthetase-like and T-cell receptor alpha delta locus were up-regulated at two and three time points, respectively.

Replication protein A3 (14 kDa), H4 histone family, member G and Human beta-2 integrin alpha D subunit (ITGAD) gene, exons 25–30 and partial cds were identified as decreased in three and two of the comparisons, respectively.

Overlapping genes that differed between bulk and clone, according to the probe identity number, were verified by alignment between probe sequences using the software Clustal V, EMBL-European Bioinformatics Institute (21).

Comparison of acute and persistent irradiation responses
A comparison was made between all candidate genes identified in this study and genes known from the literature to be involved in the acute irradiation response. In order to perform such a cross-method comparison, probes that correspond to the same gene on both arrays must be identified. We linked the corresponding Genbank accession numbers to gene identities in UniGene or LocusLink, using the GeneWeaver database and software. This comparison will by necessity not be quantitative, i.e. genes identified using a given numerical change threshold by one method can only be compared with another set derived similarly, but not exactly, by another method. The comparative results must therefore be regarded as qualitative and interpreted with some caution.

In a study using an array with approximately 6700 cDNA probes, Amundson et al. identified 379 up-regulated and 17 down-regulated genes in the acute radiation response of lymphocytes. A set of 74 genes was defined as ‘significantly induced’. We compared these three data sets to the different gene groups that were identified in the present study. Only one of the acute genes, topoisomerase II alpha 170 kDa (UniGene id Hs.156346), was found among the marker genes. Between 1 and 5 of the 74 ‘significantly induced’ genes were identified in one or more of the pair-wise comparisons with at least 2-fold change at days 7, 17 or 55 in our experiments (data not shown). It is apparent from these results that most of the acute response pattern is not maintained at the late culture time points analysed in our study.


    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
We have shown previously that exposure of primary human T-cells to ionizing radiation induces genomic instability expressed as a progressive appearance of single or multiple chromosomal translocations and deletions during clonal development (14,15). The mechanism(s) behind this effect is not known and it seems likely that changes in gene expression are responsible for propagating the instability (22). On the hypothesis that measurements of global gene expression patterns may give indications of cellular regulatory pathways that are involved in maintaining or responding to the radiation-induced chromosomal instability, we have performed such studies in bulk and clonal cultures of human T-lymphocytes. Although these cultures were not studied with regard to the presence of chromosome aberrations, the conclusion from previous studies in our own as well as other laboratories is that radiation-induced genomic instability is likely to be a general feature of irradiated T-cells, and is expressed through many different endpoints (23).

Both the bulk cultures and the individual T-cell clones in the present study showed a considerable heterogeneity with regard to gene expression regardless of irradiation status. The bulk culture constitutes a heterogeneous population of cells derived from clones and subclones of T-lymphocytes, which at the time of analysis have reached a different stage in their cell and life cycles. Likewise, an outgrown T-cell clone also contains a heterogeneous mixture of subclones, which differ with regard to cell stage and age. Thus, the differences with regard to gene expression that we have found between such cell populations are not surprising. It is also expected that the gene expression profiles change over time in culture, as proliferation rate changes, and cells age and die.

Our analysis shows that the heterogeneity is greater within irradiated than within non-irradiated cultures and clones, which is in accord with the heterogeneity observed in our previous cytogenetic analyses. In our comparison between irradiated and non-irradiated cultures and clones, we find that a number of genes show a consistent change that is associated with the radiation exposure. This occurs in a comparison involving three donors, and both bulk cultures and individual clones, and in spite of the extreme heterogeneity discussed above, and is therefore likely to be of biological interest.

We have made a number of potentially relevant observations. First, pronounced changes in the patterns of gene expression evolved during prolonged in vitro propagation of both irradiated and non-irradiated cells in bulk culture. Secondly, an increasing difference in the number of differentially expressed genes between irradiated and non-irradiated cells was observed with increasing culture time. Thirdly, the global differences in gene expression were more diversified among irradiated than among non-irradiated clones. Finally, by comparing the overlap between gene expression patterns in bulk cultures and individual clones, we identified a set of genes whose change of expression correlates with the irradiation status of the cells.

With regard to our first observation, the substantial differences in gene expression that were detected in both the irradiated and non-irradiated cultures when comparing early and late time points is likely to reflect the expected heterogeneity in clonal life-span and adjustment to culture conditions. A comparison of the two 17-day control samples, which can be considered as duplicates of one time point from different donors, revealed a considerable variation between them. It is in this heterogeneous mixture of cells and clones in various states of cell-cycle phase, growth and aging within each culture, that any radiation-induced changes in the global gene expression patterns have to be discerned.

Concerning our second observation, the comparisons between irradiated and control cultures showed that the differences in gene expression were initially small but increased with time. At day 7 the limited differences might indicate that the long-term effect of radiation was not yet established. Day 17 showed a notable difference in gene expression, more prominent in one of the experiments, and at day 55 a large number of genes were differentially expressed. In spite of this strong and time dependent manifestation of a radiation-effect on the global gene expression, the search for consistent changes between irradiated and non-irradiated samples yielded relatively few genes that were either up or down-regulated in all comparisons between irradiated and non-irradiated cultures. The gene expression pattern differed largely between the two 17-day samplings while a comparison demonstrated an overlap of only 1 probe when using a ‘2-fold or more’ threshold for pair-wise analysis of irradiated and control samples. Furthermore, no single gene was consistently changed >2-fold between irradiated and non-irradiated bulk cultures at the four samplings. When using an ‘any change criterion’, three genes were increased in all four comparisons. These relatively low numbers of genes showing a consistent change of gene expression is probably due to the complexity in the cultures with regard to cell composition. Differences in gene expression reflecting subtle changes in the growth or dynamic state of cells may be difficult to detect in complex systems such as mass cultures of cells of heterogeneous origin. Monoclonal cell cultures may offer more suitable samples for comparisons of delicate changes of gene expression. Therefore, a study of the variability between individual cell clones was performed.

Our third observation concerns the variability in gene expression patterns between individual T-cell clones. Gene expression analysis was performed in clones derived from primary human lymphocytes, which had been cultured between 22 and 46 days. Using two different assessment procedures, the variation in gene expression among the irradiated group of clones showed a larger heterogeneity than between control clones. Such diversification, but at the karyotype level, was also a major finding in the previous cytogenetic studies by Holmberg et al. (14,15). In those studies, cells belonging to a particular cell clone that expressed genomic instability and was derived from a single cell, displayed very heterogeneous karyotypes with sporadic chromosomal aberrations superimposed on clonal or subclonal karyotype abnormalities. In the present study, using total mRNA from a large number of cells in a cell clone, events at the individual cell level cannot be discerned. Nevertheless, the more variable transcriptomes among the irradiated clones as compared with the non-irradiated clones could very well reflect genomic instability at the transcriptome level.

It is notable that more than half of the genes that were varying in the control clones also occurred in the gene group that varied among the irradiated clones. Irradiation might thus extend the variability beyond a certain group of genes that naturally or due to culture time have a tendency to vary. Such patterns with different magnitudes of normal variability between individual genes have been observed when analysing the transcriptome in Saccharomyces cerevisiae (24). This finding may also suggest a link between irradiation-induced chromosomal instability, and genomic instability that is associated with cellular ageing.

Our fourth conclusion refers to the possible existence of specific genes whose expression is related to the radiation status of the cell clones. In order to detect such genes that may reflect disturbed metabolic or regulatory pathways in the irradiated cells, we used a marker analysis with two different statistics to obtain the 48 genes that were consistently differentially expressed between the two clone sample groups. A comparison of the genes indicated to be differentially expressed in the pair-wise bulk culture samples and in the clone analysis showed that a number of the clone marker genes were also changed in up to three of the four bulk comparisons. The number of genes that overlapped between bulk culture and clones was increasing with longer culture time, from three genes at day 7 to 14 at day 55. A similar accumulation of changes with increasing culture time was also seen by Holmberg et al. as a development of more complex chromosome aberrations with time in culture.

Using Gene Ontology Consortium annotations (25) we identified a number of ‘functional gene groups’ with regard to the molecular function and biological process for the candidate genes. Although no attempts have been made so far to further study these gene expression changes with alternative methods, the findings reported here constitute a base for further focused investigations of individual genes with other methods, i.e. real-time PCR and/or western blot. The up-regulated guanine nucleotide binding protein alpha 15 (26) and the down-regulated chemokine (C-X3-C) receptor 1 (27) are both involved in G-protein-coupled receptor protein signalling pathways. Johnston et al. has showed that the mouse ortholog to chemokine (C-X3-C) receptor 1 (83% sequence similarity), was increased up to 26 weeks after 12.5 Gy irradiation (28). However, the opposite change was seen in our study. Three down-regulated genes: H4 histone family, member G (29), replication protein A3 (14 kDa) (30) and topoisomerase II alpha (31) all act as DNA binding molecules in DNA metabolism. The up-regulated v-maf musculoaponeurotic fibrosarcoma (avian) oncogene homolog (32) and down-regulated musculin (activated B-cell factor-1) (33) function as transcription factors. The 2'-5'-oligoadenylate synthetase-like gene p59OASL (34) is related to 2'-5'-A synthetase, which also has a DNA binding function and acts as a ligand-dependent thyroid hormone receptor interactor. T-cell receptor alpha chain c region (35) was found to be up-regulated at two time points in the bulk cultures and down-regulated in one of the 17-day samples. This contradiction might be explained by a subclonal variation in the bulk cultures, where different subpopulations of clones are more dominant at certain time points.

Once the acute genotoxic stress response to irradiation has declined, the T-cell bulk cultures and clones grow without evidence of cell-cycle arrest or apoptosis. Our previous flow cytometric DNA analyses have shown that individual T-cell clones exhibit a diploid stem line and a regular cell-cycle profile (80, 12 and 7% of cells in G1, S and G2/M, respectively). Apoptotic cells occur at a very low percentage in these cultures, and are hardly detectable by flow cytometry (15). These results are supported by the present observations. When compared with data from the literature, only one of the marker genes was identical to genes identified previously in the acute radiation response, indicating that this response is not maintained at later culture times.

In summary, we have observed a tendency towards a more heterogeneous general gene expression pattern in irradiated cell clones. This may be a consequence of the genomic instability, where multiple chromosomal lesions lead to changes in the regulation of neighboring genes. Alternatively, the observed ‘transcriptomic diversification’ could be a manifestation of a de-regulated state of the cell with a globally relaxed control of normal regulatory constraints and therefore a more diversified evolution of subclones in the culture. These properties of the system under study may constitute a model for early events in carcinogenic progression (22). The dynamics of gene expression pattern changes in the irradiated clones compared with controls could permit a more comprehensive elucidation of such processes. Finally, we have indicated a number of individual genes that constitutes a starting point for further studies of their possible relevance for radiation-induced genomic instability.


    Notes
 
3 To whom correspondence should be addressed Email: susann.falt{at}cnt.ki.se Back


    Acknowledgments
 
This work was supported by the Swedish Cancer Society, the Swedish Radiation Protection Institute and European Union Contract no FIGH-CT-1999-00003 (RADINSTAB). A.W. is affiliated with Affibody AB, which provided beta versions of the GeneWeaver software system.


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

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Received May 23, 2003; revised July 8, 2003; accepted July 25, 2003.





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