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. 2016 Nov;90(11):2745-2761.
doi: 10.1007/s00204-015-1621-7. Epub 2015 Nov 2.

Use of High-Throughput RT-qPCR to Assess Modulations of Gene Expression Profiles Related to Genomic Stability and Interactions by Cadmium

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Free PMC article

Use of High-Throughput RT-qPCR to Assess Modulations of Gene Expression Profiles Related to Genomic Stability and Interactions by Cadmium

Bettina Maria Fischer et al. Arch Toxicol. .
Free PMC article

Abstract

Predictive test systems to assess the mode of action of chemical carcinogens are urgently required. Within the present study, we applied the Fluidigm dynamic array on the BioMark™ HD System for quantitative high-throughput RT-qPCR analysis of 95 genes and 96 samples in parallel, selecting genes crucial for maintaining genomic stability, including stress response as well as DNA repair, cell cycle control, apoptosis and mitotic signaling. The specificity of each individually designed sequence-specific primer pair and their respective target amplicons were evaluated via melting curve analysis as part of qPCR and size verification via agarose gel electrophoresis. For each gene, calibration curves displayed high efficiencies and correlation coefficients in the identified linear dynamic range as well as low intra-assay variations. Data were processed via Fluidigm real-time PCR analysis and GenEx software, and results were depicted as relative gene expression according to the ΔΔC q method. Subsequently, gene expression analyses were conducted in cadmium-treated adenocarcinoma A549 and epithelial bronchial BEAS-2B cells. They revealed distinct dose- and time-dependent and also cell-type-specific gene expression patterns, including the induction of genes coding for metallothioneins, the oxidative stress response, cell cycle control, mitotic signaling and apoptosis. Interestingly, while genes coding for the DNA damage response were induced, distinct DNA repair genes were down-regulated at the transcriptional level. Thus, this approach provided a comprehensive overview on the interaction by cadmium with distinct signaling pathways, also reflecting molecular modes of action in cadmium-induced carcinogenicity. Therefore, the test system appears to be a promising tool for toxicological risk assessment.

Keywords: Cadmium; Fluidigm dynamic array; Gene expression profiling; Genomic stability; High-throughput RT-qPCR.

Conflict of interest statement

Compliance with ethical standard Conflict of interest The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Evaluation of primer specificity. a Example of agarose gel electrophoresis analyses for the genes NFKB2, OGG1, PMAIP1, PRDX1 and RRM2B (and included NTCs) with corresponding size of the specific target amplicons. b Example of melting curve analyses (BioMark™ HD System) for the genes OGG1, PMAIP1 and RRM2B with corresponding melting temperatures of the specific target amplicons
Fig. 2
Fig. 2
Performance of primer efficiency. Examples of the amplification curves from the calibration performed with Fluidigm dynamic array for a GAPDH, b JUN and c SIRT2. Six serial dilutions (1–200-fold) of standard cDNA are shown. Plot of calibration curves (x-axis log10 of relative template concentration, y-axis C q value of template concentration) with linear regression trend line and correlation coefficient for d GAPDH, e JUN and f SIRT2. Primer efficiency can be calculated from the slope of the calibration curve
Fig. 3
Fig. 3
Impact of cadmium on gene expression related to uptake and oxidative stress response. BEAS-2B cells (a) or A549 cells (b) were treated with CdCl2 for 24 h. Shown are mean values of four determinations derived from two independent experiments ± SD. Statistically significant different from control: **p ≤ 0.01, ***p ≤ 0.001 (ANOVA–Dunnett’s T test)
Fig. 4
Fig. 4
Impact of cadmium on gene expression related to the anti-oxidative defense system. BEAS-2B cells were treated with CdCl2 for 8 or 24 h. Shown are mean values of four determinations derived from two independent experiments ± SD. Statistically significant different from control: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 (ANOVA–Dunnett’s T test)
Fig. 5
Fig. 5
Impact of cadmium on gene expression related to cell cycle regulation and proliferation. BEAS-2B cells were treated with CdCl2 for 8 or 24 h. Shown are mean values of four determinations derived from two independent experiments ± SD. Statistically significant different from control: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 (ANOVA–Dunnett’s T test)
Fig. 6
Fig. 6
Impact of cadmium on gene expression related to apoptosis. BEAS-2B cells (a) or A549 cells (b) were treated with CdCl2 for 8 or 24 h. Shown are mean values of four determinations derived from two independent experiments ± SD. Statistically significant different from control: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 (ANOVA–Dunnett’s T test)
Fig. 7
Fig. 7
Impact of cadmium on gene expression related to the DNA damage response. BEAS-2B cells (a) or A549 cells (b) were treated with CdCl2 for 8 or 24 h. Shown are mean values of four determinations derived from two independent experiments ± SD. Statistically significant different from control: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 (ANOVA–Dunnett’s T test)
Fig. 8
Fig. 8
Impact of cadmium on gene expression related to the DNA repair system. BEAS-2B cells (a) or A549 cells (b) were treated with CdCl2 for 8 or 24 h. Shown are mean values of four determinations derived from two independent experiments ± SD. Statistically significant different from control: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 (ANOVA–Dunnett’s T test)

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