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Technical Briefs |
Institute of Clinical Chemistry, University of Regensburg, Regensburg, Germany;
aaddress correspondence to this author at: Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Franz-Josef-Strauss-Allee 11, D-93053 Regensburg, Germany; fax 49-941-944-6202, e-mail thomas.langmann{at}klinik.uni-regensburg.de
Abstract
Background: ATP-binding cassette (ABC) transporters cause various diseases and regulate many physiologic processes, such as lipid homeostasis, iron transport, and immune mechanisms. Several ABC transporters are involved in bile acid, phospholipid, and sterol transport, and their expression is itself controlled by lipids. In addition, ABC proteins mediate drug export in tumor cells and promote the development of multidrug resistance.
Methods: We created an ABC Transporter TaqMan Low-Density Array based on an Applied Biosystems 7900HT Micro Fluidic Card. We used a 2-µL reaction well with 2 ng of sample. To evaluate this method for lipidomic research and to characterize expression patterns of ABC transporters in cells relevant for atherosclerosis research, we monitored mRNA expression in human primary monocytes, in vitro–differentiated macrophages, and cells stimulated with the liver-X-receptor and retinoid-X-receptor agonists T0901317 and 9-cis retinoic acid, mimicking sterol loading.
Results: The method enabled simultaneous analysis of 47 human ABC transporters and the reference gene 18S rRNA in 2 replicates of 4 samples per run.
Conclusions: The new system uses only 2 ng of sample and small volumes of reagent, and the precaptured primers and probes avoided labor-intensive pipetting steps. The ABC Transporter TaqMan Low-Density Array may be a useful tool to monitor dysregulated ABC transporter mRNA profiles in human lipid disorders and cancer-related multidrug resistance and to analyze the pharmacologic and metabolic regulation of ABC transporter expression important for drug development in large-scale screening approaches.
ATP-binding cassette (ABC) transporters cause various human monogenic and polygenic diseases and regulate many physiologic processes, such as lipid homeostasis, iron transport, and immune mechanisms (1). Numerous ABC transporters are involved in bile acid, phospholipid, and sterol transport (2), and expression of the genes that encode these transporters is itself controlled by lipids. Several ABC proteins mediate drug export in compound-treated tumor cells and thereby promote the development of multidrug resistance (3). Thus, ABC transporter mRNA profiling may become an integral part of lipidomics, molecular clinical diagnosis, and monitoring of drug effects in cancer patients (4).
We recently described a real-time reverse transcription-PCR method for detection and quantification of 47 of the 48 currently known members of the ABC transporter superfamily (5). This analysis was based on relative quantification using the Standard Curve Method and allowed monitoring of RNA samples in a 384-well format with only 50 ng of total RNA. We have successfully used the method to quantify ABC transporter mRNA in 20 different human tissues.
We have now extended, improved, and simplified the quantification of all 47 ABC transporters by creating a Human ATP-Binding Cassette Transporter TaqMan® Low-Density Array based on an Applied Biosystems 7900HT Micro Fluidic Card. The method allows simultaneous analysis of 47 human ABC transporters and the reference gene 18S rRNA in 2 replicates of 4 different samples per run. The 2-µL small-volume design of each reaction well substantially decreases sample and reagent consumption, and the precaptured primers and probes save time by reducing labor-intensive pipetting steps.
To evaluate this method for lipidomic research and to further characterize the patterns of ABC transporters produced in cells relevant for atherosclerosis research, we monitored expression of mRNA in human primary monocytes, in vitro–differentiated macrophages, and cells stimulated with the liver-X-receptor (LXR) and retinoid-X-receptor (RXR) agonists T0901317 and 9-cis retinoic acid (RA) to mimic sterol loading and foam cell formation.
Human monocytes were obtained from 3 healthy donors by leukapheresis and counterflow elutriation. The cells were cultured on plastic petri dishes in macrophage serum-free growth medium (Gibco BRL) and allowed to differentiate for 5 days in the presence of 50 µg/L recombinant human macrophage colony-stimulating factor (R&D Systems) in a 5% CO2 atmosphere at 37 °C. Macrophages were stimulated for 24 h with a 5 µmol/L combination of the LXR ligand T09013179 (Sigma) and the RXR ligand 9-cis RA (Sigma). Control macrophages were kept 24 h in the presence of solvent (ethanol).
Harvesting of cells, RNA extraction, and DNase digestion were carried out with the RNeasy Midi Kit (Qiagen), according to the manufacturer’s instructions. RNA purity and integrity were assessed on the Agilent 2100 bioanalyzer with the RNA 6000 Nano LabChip® reagent set (Agilent Technologies). The RNA was quantified spectrophotometrically and then stored at –80 °C.
cDNA synthesis was performed with the Reverse Transcription System from Promega. The master mixture contained 5 mmol/L MgCl2, 1x Reverse Transcription Buffer, 1 mmol/L deoxynucleotide triphosphate mixture, 1 unit/µL recombinant RNasin® ribonuclease inhibitor, 0.75 U/µL AMV Reverse Transcriptase, and 1 µg of Random Hexamer Primers. We added 1 µg of total RNA and sterile H2O to a final volume of 20 µL. The reaction mixture was incubated at 42 °C for 60 min, followed by heat inactivation of the enzyme at 95 °C for 5 min. After cooling on ice for 5 min, the cDNA was stored at –20 °C.
The TaqMan probe and primer sets for 47 human ABC transporters and 18S rRNA as a reference gene were carefully selected from predesigned TaqMan Gene Expression Assays (Applied Biosystems). The minor groove binding probes were 5'-labeled with a 6-carboxyfluorescein dye and a nonfluorescent quencher at the 3' end. The exact locations and the sequences of the oligonucleotides used in all assays can be downloaded from the Applied Biosystems website (https://myscience.appliedbiosystems.com) by selecting the Assays IDs (see Table 1 in the Data Supplement that accompanies the online version of this Technical Brief at http://www.clinchem.org/content/vol52/issue2/). All ABC transporter assays span exonintron boundaries and cover the major transcript forms. The sets were factory-loaded into the 384-well plate to create the TaqMan Low-Density Array (see Fig. 1 in the online Data Supplement). The specific configuration presented here is available as a Human ATP-Binding Cassette Transporter TaqMan Low-Density Array.
We mixed 2 µL of single-stranded cDNA (equivalent to 100 ng of total RNA) with 48 µL of nuclease-free water and 50 µL of TaqMan Universal PCR Master Mix. After we loaded 100 µL of the sample-specific PCR mixture into 1 sample port, the cards were centrifuged twice for 1 min at 280g and sealed to prevent well-to-well contamination. The cards were placed in the Micro Fluidic Card Sample Block of an ABI Prism 7900 HT Sequence Detection System (Applied Biosystems). The thermal cycling conditions were 2 min at 50 °C and 10 min at 95 °C, followed by 40 cycles of 30 s at 97 °C and 1 min at 59.7 °C. Each Micro Fluidic Card has a unique barcode, and Sequence Detection System plate documents store information on the plate type, detector, sample/target gene configurations, thermal cycling conditions, data collection, and raw fluorescence data at each cycle.
Micro Fluidic Cards were analyzed with RQ documents and the RQ Manager Software for automated data analysis. Experiments for 3 different donor cells, carried out in duplicate, were analyzed together as 1 relative quantity (RQ) study. Expression values for target genes were normalized to the concentration of 18S rRNA, which showed the least variation among reference genes in our monocyte/macrophage/foam cell model. Gene expression values were calculated based on the comparative threshold cycle (Ct) method (6), in which RNA samples were designated as calibrators to which the other samples were compared. In short, the Ct data for all human ABC transporters and 18S rRNA in each sample were used to create 
Ct values (CtABC transporter – Ct18S rRNA). Thereafter, Ct values were calculated by subtracting the Ct of the calibrator from the Ct value of each target. The RQs were calculated with the equation: RQ = 2–Ct. For calculating the RQ of ABC transporter mRNA in macrophages compared with monocytes, monocyte RNAs were designated as calibrators. To analyze the RQ amounts of ABC transporters as an effect of 9-cis RA/TO901317 stimulation, macrophage RNAs were used as calibrators. The SDs for Ct and Ct values were calculated from the single Ct values with the equation: SDCt = 
(SD12 + SD22). The Ct values (SD) for the expression of all ABC transporter genes in monocytes are given in Table 1
(also see Table 1 in the online Data Supplement). We selected Ct values >25 as the cutoff for absence of expression. The mean RQs of ABC transporter mRNA in macrophages relative to monocytes and of 9-cis RA/TO901317–treated cells relative to control–treated macrophages of 3 different donors were combined and are listed in Table 1
. The ranges of the RQ values were calculated by use of the equation: RQ = 2–Ct, with Ct + SD and Ct – SDs. The Micro Fluidic Cards detect a 2-fold difference in gene expression at the 99.7% confidence level. The efficiencies of all target and reference amplifications were nearly identical as analyzed by serial dilutions using 2, 1, 0.5, 0.25, and 0.125 ng of calibrator cDNA. When we plotted the log input amount of cDNA vs the Ct values, the slope was <0.1. The correlation coefficients were always >0.95, which is comparable to our previously established method (5). We assessed the within-run and day-to-day imprecision of our assay by measuring 4 identical samples in duplicate; the CVs were <1.6% and <2.9%, respectively.
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As can be seen in Table 1
, 30 ABC transporters displayed detectable mRNA expression in human primary monocytes, based on Ct values >25. Twenty-six genes were induced more than 2-fold during macrophage differentiation, and 12 genes were sensitive to LXR/RXR agonist incubation, with 10 up-regulated ABC transporters and only 2 down-regulated genes. Although the differentiation-dependent and sterol-regulated induction of ABCA1 and ABCG1 is well established (7), parallel transcript profiling, using our Human ABC Transporter TaqMan Low-Density Array, revealed several additional differentiation-dependent ABC transporters and novel LXR/RXR-regulated ABC transporters, including ABCB1 (MDR1), ABCB9, ABCB11 (BSEP), ABCC2 (MRP2), ABCC5 (MRP5), ABCD1 (ALD), ABCD4, and ABCG2. Despite the large dynamic range of gene regulation (e.g., a 24.5-fold induction of ABCA1 by 9-cis RA/T0901317), the range of differential transcript expression between 3 different donors was quite narrow (21.9–27.4). This implies that the interindividual differences in response are much smaller than the differentiation-dependent and lipid-regulated effects, allowing screening processes with a limited number of probands.
These findings are particularly relevant for lipidomics and cardiovascular research, as these ABC transporters are novel candidates for lipid disorders and pharmacologic targets for lipid-modulating drugs.
In summary, we have developed a Human ABC Transporter TaqMan Low-Density Array based on the TaqMan chemistry, the Micro Fluidic Card, and the 7900HT Sequence Detection System. Compared with standard TaqMan reverse transcription-PCR methods, the Micro Fluidic Card requires less sample material (2 ng instead of 50 ng of total RNA-equivalents per gene), only one-tenth the volume of TaqMan Universal Master Mix, and much less hands-on time. This assay could be a useful tool for monitoring dysregulated ABC transporter mRNA profiles in human lipid disorders and cancer-related multidrug resistance. Furthermore, the pharmacologic and metabolic regulation of ABC transporter gene expression important for drug development could be analyzed in large screening approaches using this Human ABC Transporter TaqMan Low-Density Array.
Acknowledgments
We thank Ernst Arnoldi, Astrid Potratz, and Andrea Geiger for support and discussions, and Manfred Haas for excellent technical assistance. This study was funded by grants from the Deutsche Forschungsgemeinschaft (SFB585-02/A1) and was supported by Applied Biosystems.
References
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