* Environmental Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709; Integrated Laboratory Systems, Inc., Research Triangle Park, North Carolina 27709;
Constella Group, Inc., Research Triangle Park, North Carolina 27709;
Paradigm Array Labs, A service unit of Icoria, Inc., Research Triangle Park, North Carolina 27709; and ¶ Battelle Science and Technology International, Columbus, Ohio, 43201
1 To whom correspondence should be addressed at the National Institute of Environmental Heath Sciences, MD B3-08, Environmental Toxicology Program, P.O. Box 12233, 111 T. W. Alexander Drive, Research Triangle Park, NC 27709. Fax: (919) 541-4714. E-mail: boorman{at}niehs.nih.gov.
Received February 10, 2005; accepted March 29, 2005
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ABSTRACT |
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Key Words: liver; rat; differential gene expression; microarray; transcriptome; circadian; clock; Per1; Per2; Arntl; Bmal1; Nr1d1.
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INTRODUCTION |
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Many peripheral tissues exhibit tissue-specific rhythms regulated by many of the same clock genes and transcriptional feedback loops as the master clock of the SCN. However, many of the clock-controlled genes are specific to each tissue and, thus, likely linked to the physiological function of the individual tissue.
The daily profile of gene expression in the mouse liver is strongly influenced by the circadian cycle (Akhtar et al., 2002; Duffield, 2003
; Panda et al., 2002
; Storch et al., 2002
; Ueda et al., 2002
). The circadian cycling of the rat transcriptome has not been studied as extensively as that of the mouse. In comparing across studies, it is often useful to use circadian time (CT), where CT0 represents light on and CT12 represents light off in a 12-h light-on/12-h light-off study. Kita et al. (2002)
compared the transcript profile of the livers of Dahl salt-sensitive rats collected 12 h apart (2 h after light on and 2 h after light off or CT2 and CT14) to maximize the possibility of detecting genes whose transcript levels vary over the circadian interval. They found diurnal differences of expression in approximately 7% of the genes on their microarray (Kita et al., 2002
). Many of the transcripts exhibited a ten-fold expression differential between night and day including known period genes (Kita et al., 2002
). In contrast, Desai et al. compared the transcript profile of livers from 1-year-old F344 rats sampled at four time points (4 and 9 h after light on [CT4 and CT9] and 3 and 9 h after light off [CT15 and CT21]) to a pooled control consisting of equal portions of transcripts from each of the four collection times. In their analysis, they identified only two genes that exhibited greater than a two-fold difference in expression over the period examined, and for most genes the expression differences were less than 1.5-fold (Desai et al., 2004
). Surprisingly, circadian genes were not identified as differentially expressed in this study (Desai et al., 2004
). Because of the difference in animal models and study design, it is not possible to determine the basis for the differing results obtained by Kita and Desai. Since the rat remains the dominant model system for drug discovery, toxicology, and pharmacokinetic studies, we felt it was important to resolve the apparent discrepancy between the two studies.
To accomplish this objective we have used microarrays to perform direct day/night 12-h offset comparisons of rat hepatic transcripts similar to the design of Kita et al. (2002) and also compared transcripts from livers collected at different times of day (CT4, CT10, CT16, CT22) against a universal control similar to the design of Desai et al. (2004)
. These studies were done with young F344 rats, a common model for toxicology evaluations. Quantitative RT-PCR (qRT-PCR) was used to verify the expression level for selected genes involved in circadian and metabolic pathways.
Our results indicate that the circadian cycle has a significant effect on the rat hepatic transcriptome. Additionally, by qRT-PCR we demonstrate marked gene expression differences across a 6-h period during the day, a common period for tissue collections in toxicology studies. Further, we identify differential expression for several circadian genes not previously reported in the rat liver. These results have important implications for the design and interpretation of rat toxicogenomic studies as well as a better understanding of the circadian gene expression in the rat liver.
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MATERIALS AND METHODS |
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Animal-handling procedures during the dark were accomplished under a dim red light (<0.2 lux with a wavelength of greater than 650 nm). The rats from the dark were moved to the necropsy area in a cage covered by a hood, and the necropsies took place within 1 h. The rats from the day groups were kept in a cage until necropsy.
RNA isolation.
The left hepatic lobe was cut into 0.5-cm cubes or smaller and immersed in RNALater® (Ambion, Austin, TX) within 4 min of necropsy. The tissues were stored in RNALater® overnight at 4 ± 3°C, then stored at 20 ± 1°C until RNA isolation (within 60 days). Details of the RNA isolation procedures have been previous published (Boorman et al., 2005). Briefly, the RNA samples were frozen at 70°C and shipped to the National Toxicology Program (NTP) repository until transfer to Paradigm Array Labs (Icoria, Inc., RTP, NC) for microarray analysis. RNA was isolated from the twelve individual rats at each of four time points (CT4, CT10, CT16, CT22) and was used for microarray and qRT-PCR. In addition, equal amounts of RNA from six rats were used to form two composite pools at each time point for pooled comparisons.
Microarray hybridizations.
One µg of total RNA from either an individual rat or from a pooled sample was amplified and labeled with a fluorescent dye (either Cy3 or Cy5) using the Low RNA Input Linear Amplification Labeling kit (Agilent Technologies, Palo Alto, CA) following the manufacturer's protocol. The amount and quality of the resulting fluorescently labeled cRNA was assessed using a Nanodrop ND-100 spectrophometer and an Agilent Bioanalyzer. Equal amounts of Cy3- or Cy5-labeled cRNA were hybridized to the Agilent Rat Oligo Microarray (Agilent Technologies, Inc., Palo Alto, CA) for 17 h, prior to washing and scanning. Data was extracted from the resulting images using Agilent's Feature Extraction Software (Agilent Technologies, Inc., Palo Alto, CA). For day/night comparison, hepatic RNA samples from three individual day rats collected 10 h after light on (CT10) were hybridized against a pooled RNA sample composed of equal amounts of RNA from the livers of six night rats collected 10 h after lights off (CT22); and RNA samples from three individual night rats collected at CT22 were hybridized against a pooled RNA sample composed of equal amounts of RNA from the livers of six day rats collected at CT10. For each comparison a dye reversal hybridization was also performed. This was replicated in a second study for a total of 24 hybridizations of 12 h offset samples for the CT10/CT22 times only.
In a second comparison, two pools of RNA from the livers of six rats collected at each of four different times of the circadian day (CT4, CT10, C16, and CT22) were hybridized against a Universal Rat Reference RNA Standard (Stratagene, La Jolla, CA).
Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis of gene expression.
Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed on hepatic RNA samples from six individual rats randomly selected from twelve rats at each of the four time points. Since the selection was random, there was some but not complete overlap with the rats used in the microarray studies. In addition, qRT-PCR was performed on the same pools of RNA (n = 8) used in the microarray hybridizations. This resulted in eight qRT-PCR measurements (six individuals and two pools) for each gene at each time point. The qRT-PCR reactions were performed in duplicate.
RNA was reversed transcribed into first strand cDNA using the High-Capacity cDNA Archive kit (Applied Biosystems, Foster City, CA). For each sample, 2.5 µg RNA in volume of 50 µl was combined with an equal volume of the 2x RT Master-Mix (Applied Biosystems, Foster City, CA), containing random primers, dNTP mixture, and Multiscribe RT enzyme in 96-well reaction plate. The plate was incubated for 10 min at 25°C and then at 37°C for 2 h in a 9700 ABI Thermocycler. The cDNA was stored at 20°C until further use. The cDNA was amplified using primer and probe sets (more details in Supplementary Data) from Assays on Demand (Applied Biosystems, Foster City, CA) on an ABI 7900 Sequence Detection System (Applied Biosystems, Foster City CA). Universal Master-Mix (Applied Biosystems, Foster City, CA) with the specified Taqman® Primer Probe set was added to each well on a 384-well reaction plate (Table 1, Taqman Primer Probe sets). Fifty ng of each cDNA was added to the master mix for a final volume of 20 µl. The samples were amplified by incubation for 2 min at 50°C, then 10 min at 95°C, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. SDS Software version 2.1 and Microsoft Excel software were used for analysis of the resulting data. Automatic threshold values were used, and expression of each gene was normalized to Rpl18, a rat housekeeping gene, at each time point. The expression of Rpl18 did not vary significantly across the four time points studied (ANOVA; p > 0.05). The coefficient of variation across all four times was 1.9%. The t-test, assuming unequal variances, was used to assess differences between time points for the qRT-PCR data.
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A Fourier transform, as used to identify periodically expressed genes in the human cell cycle (Whitfield et al., 2002), was applied to the pooled sample data from the four time points. In our implementation the Fourier transform and calculation of D was performed though the following series of equations (Eq. 13):
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The estimated cycle time (T) was set to 24 h for all experiments, and represents the time increment of a single experiment. The first difference in our implementation is the estimation of an offset of cycle time. The offset value (
in Eqs. 12) was previously used to estimate variability due to synchronization of the cell cycle; however, this factor is not appropriate in our experiment, and
was set to 0 for all analyses. Calculation of the periodicity score was performed after weighting D by the maximal correlation with one of six representations of circadian or metabolic cycle. This weighting is performed because many of the genes do not perfectly match with sine or cosine curves. The six vectors used for this weighting correspond to the expression values of Per1, Per2, Fasn, Avpr1a, Slc22a5, and Bhlhb3. The final step of Whitfield et al. (2002)
consisted of an autocorrelation calculation to remove genes that did not show consistent cycling across multiple rounds of the cell cycle. Our data prohibited this calculation, because data was collected for a single 24-h cycle. Genes were selected when the resulting periodicity score was greater than 1.5 SD above the mean.
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RESULTS |
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SAM analysis (1% FDR) of replicate number one identified 799 genes with increased expression during the night and 679 genes with decreased nighttime expression for a total of 1478 (7.3% of the genes on the array) genes whose expression exhibited a day/night difference. A similar analysis of the replicate study identified 1607 (867 increased and 740 decreased) genes (7.9% of the genes on the array). There were 972 (4.8%) differentially expressed genes (470 increased, 502 decreased) common to both replicates, and these are shown in decreasing order of expression level (Fig. 1). The list includes clock genes, clock-controlled genes, as well as genes involved with normal intermediary metabolism. We found 102 genes in common with the 597 genes identified in the Kita study (Kita et al., 2002).
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Similar to the Desai et al. study (2004), RNA from the livers of six rats collected at each time point (CT4, CT10, CT16, and CT22) was pooled, (with two replicates at each time) and hybridized against a common reference standard. While Desai et al. used a pool of all sixteen rats as a reference, in the present study a universal rat reference standard was used. Using SAM multi-class analysis and a 1% FDR, no genes were identified whose expression differed significantly across the four times examined. Even at a SAM FDR of 25%, only 57 genes were identified as significantly different across the four time points.
These results and those of Desai (Desai et al., 2004) suggest that the use of ANOVA-based methods are unsuited for analyzing transcript levels that follow a cyclic expression over time. We therefore applied the Fourier Transform approach used by Whitfield et al. (2002)
to the common reference data to identify genes periodically expressed over a 24-h cycle. This analysis identified 1300 transcripts (6% of the genes on our array) whose levels varied periodically over the circadian cycle. There were 200 genes in common with the 12-h offset list, and numerous circadian genes were found (Fig. 2). The small amount of overlap between the two results is expected, since we used SAM to identify highly significant differences between a single day and night time point, while the Fourier approach identifies periodicity of expression over the four time points based on relative expression. The difference in the SAM approach and the Fourier Transform approach is presented in the Discussion.
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There was little variability in the qRT-PCR replicate analysis and excellent correlation between qRT-PCR results from the pooled samples and the six individual animals selected at random from the pools. The majority of circadian (Fig. 3) and metabolic (Fig. 4) transcripts predicted to follow a diurnal pattern from microarray data were shown by qRT-PCR to vary across the four time points examined. Of particular interest was the observation that Clock expression that has been reported to be stable in the SCN over the circadian day, decreased from CT4 to CT 10 in the rat liver.
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Genes coding for enzymes involved in lipogenesis (fatty acid elongation) and cholesterol synthesis (fatty acid synthase) were up-regulated during the dark phase (CT16 and CT22) when animals are actively feeding. During the light phase (CT4 and CT10), genes associated with fatty acid oxidation (Cpt1a) and ß-oxidation (Amacr), were up-regulated as well as genes associated with processing of stored energy (Hao3 and Bdh), (see Supplementary Data for a more complete list of metabolic genes that were differentially expressed). Furthermore, there is excellent concordance of the experimental measurements generated from microarray and qRT-PCR (Fig. 5).
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DISCUSSION |
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Importantly we observed patterns of expression for genes involved in both the circadian cycle and normal metabolic pathways similar to Kita et al. (2002). Common circadian genes included Arntl (or Bmal1), Period 1 and Period homolog 2, plus three circadian output genes including D site albumin binding protein (Dbp), arginine vasopressin receptor 1A (Avpr1a), and ubiquitin specific protease 2 (Usp2). Genes in metabolic pathways that showed increased expression at night include fatty acid synthase, fatty acid elongase 2, and glucokinase as would be expected for nocturnal feeders. The similarities between the present study and that of Kita is not unexpected, since comparing gene expression between livers whose collection is offset by 12 h is more likely to identify the extremes of differential gene expression (Akhtar et al., 2002
).
Comparing gene expression from tissues collected at multiple time points against a common control provides a better indication of the pattern of expression over the circadian day but is less sensitive because of not directly comparing the extremes of gene expression (Akhtar et al., 2002). Desai et al. collected livers from four rats at four times and hybridized individual rats against a pool comprised of all 16 rats (Desai et al., 2004
). A rat oligonucleotide array with 3096 known genes was used, and 67 genes were found to be significant in one of two tests (it should be noted that 59 significant genes are expected with the null hypothesis at the level of significance that was used). Unexpectedly, no circadian genes appeared in their list. In part, this may have been due to sample size, or as shown in this study, the ANOVA method used by Desai is not the most sensitive approach for determining periodicity in gene expression.
In the current study, we hybridized RNA from eight pools to a commercially available universal reference sample. The four time points (CT4, CT10, CT16, CT22) are similar to the time points (CT4, CT9, CT15, CT21) from the Desai et al. study. Using SAM analysis, we had to use a 25% FDR to identify 57 significant genes, a result not unlike the findings of Desai et al. (2004).
The limited findings in both experiments may be due to the choice of analysis method. SAM analysis in this study and the Desai (Desai et al., 2004) analysis are ANOVA-based methods that appear to be less sensitive for detecting cyclic rhythms in gene expression. Additionally, the ANOVA methods are limited in that the increased number of dependent variables, or groups, requires a larger sample size relative to the 12-h offset analysis (only two groups). Therefore, the limited samples available in both this study and the Desai et al. study resulted in limited results with high false discovery rates when using variants of the ANOVA method.
Although limited sample size effecting statistical power is one problem, a more important issue is that the question asked by ANOVA methods is not appropriate when looking for cyclic patterns. ANOVA methods look for genes with low variability within a group and high variability across groups. This ignores our knowledge of the ordering of groups across time, and that trends or cycles exist over time. Fourier transform methods are fast, efficient approaches to decomposition of time series data to identify these embedded rhythms (Straume, 2004). This approach has been used to analyze blood pressure data that shows a characteristic rise and fall during the day (Rodda et al., 1996
), to identify genes periodically expressed in the cell cycle (Whitfield et al., 2002
), and for assessment of circadian rhythms (Ceriani et al., 2002
; Panda et al., 2002
). When we implemented this method for finding cyclic patterns, we were able to identify 1300 differentially expressed genes including many of the canonical circadian genes. This indicates that the minimal results in Desai's experiment and our multi-class SAM analysis may not be due to limited data, but a result of the method of analysis.
We felt it was important to evaluate diurnal patterns in the rat because of the differences between and the limitations of the Kita et al. and Desai et al. studies. The Kita et al. study involved only a total of 10 Dahl salt-sensitive rats, six of which had been fasted (Kita et al., 2002). The Dahl salt-sensitive rat is not a common model for toxicology studies nor are 1-year-old rats as used in the Desai et al. study (Desai et al., 2004
). In the current study, we used 12-week-old F344/N rats and repeated the study. Replicate experiments with twelve rats at each time help limit the impact of technical and individual animal variability, providing greater confidence in the results.
Using qRT-PCR, we evaluated transcript levels of Dec1, Dec2, Per2, and Cry2 whose products provide both positive and negative feedback on the circadian cycle. By both microarray and qRT-PCR analyses, Dec1 and Dec2 showed higher expression at CT10 than CT22, with Dec1 peaking at CT16. Dec1 and Dec2 are increased during the day in the mouse SCN (Honma et al., 2002). In contrast to the present study, rat hepatic Dec1 and Dec2 expression were reported to be higher at night by both Northern blot analysis and by qRT-PCR (Noshiro et al., 2004
). One possible explanation for the difference between the studies is that in the present study the rats were acclimated for 2 weeks prior to study start with acclimation to the light/dark cycle confirmed by assessing melatonin levels (Boorman et al., 2005
). Noshiro et al. (2004)
acclimated rats for 3 days, but it takes approximately a week to set the circadian cycle of the liver (Schibler et al., 2003
).
We found Per2 expression rising during the day and peaking at CT16 using qRT-PCR. Our Per2 gene expression data are consistent with other rodent liver analyses (Akhtar et al., 2002; Kita et al., 2002
; Oishi et al., 2003
; Storch et al., 2002
; Ueda et al., 2002
). We also found Cry2 expression rising during the day, with a plateau from CT10 to CT22. To our knowledge, rhythmic gene expression pattern for Cry2 has not been reported before in the rodent liver.
The importance of circadian genes for the study of liver toxicity is their impact on daily oscillations in liver function and responses to xenobiotics. The protein product of clock-controlled genes in turn act as transcription factors for the many genes that are crucial for liver function, including genes involved in xenobiotic metabolism (Oishi et al., 2003; Reppert and Weaver, 2002
).
Clock and Bmal1 protein products form a heterodimer that is the transcriptional driver for the circadian cycle (Maywood et al., 2003). Bmal1 but not Clock was identified in our microarray data as differentially expressed. The qRT-PCR data show highest Bmal1 levels at CT4, lowest levels at CT10, with expression rising during the dark consistent with other rodent liver data (Akhtar et al., 2002
; Kita et al., 2002
; Oishi et al., 2003
; Storch et al., 2002
; Ueda et al., 2002
). Clock mRNA levels do not oscillate in the suprachiasmatic nuclei (Dunlap, 1999
; Reppert and Weaver, 2001
) or liver (Duffield, 2003
). Thus the difference in Clock gene expression between CT4 and CT10 was unexpected (see Fig. 3). Twelve qRT-PCR reactions from six individual animals were performed at each time, and the differences between CT4 and CT10 were highly significant (p < 0.001). A review of our microarray data also showed the highest Clock expression at CT4; however Clock was not identified as differentially expressed, because it did not meet our fold change criteria. Variation in Clock expression has not been reported previously in the rat or the mouse liver to our knowledge.
Microarray technology is a powerful tool for identifying the multitude of genes with varying expression over time (Duffield, 2003; Reppert and Weaver, 2002
). In the present study, we demonstrated that the rat transcriptome exhibits significant circadian variation in expression. The qRT-PCR and microarray data show a marked difference in gene expression in livers collected 6 h apart during the light period of the circadian day. It will be critical to consider these daily gene variations in both the dosing and collection of tissues for toxicogenomic studies. It appears that time is an important potential confounder, and we suggest that light/dark cycles, food accessibility, dosing times, and collection times of tissues/biofluids need to be part of the supporting data for all toxicogenomic studies.
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SUPPLEMENTARY DATA |
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ACKNOWLEDGMENTS |
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