Whole genome microarray analysis of neural progenitor C17.2 cells during differentiation and validation of 30 neural mRNA biomarkers for estimation of developmental neurotoxicity

<div><p>Despite its high relevance, developmental neurotoxicity (DNT) is one of the least studied forms of toxicity. Current guidelines for DNT testing are based on <i>in vivo</i> testing and they require extensive resources. Transcriptomic approaches using relevant <i>in vitro</i> models have been suggested as a useful tool for identifying possible DNT-generating compounds. In this study, we performed whole genome microarray analysis on the murine progenitor cell line C17.2 following 5 and 10 days of differentiation. We identified 30 genes that are strongly associated with neural differentiation. The C17.2 cell line can be differentiated into a co-culture of both neurons and neuroglial cells, giving a more relevant picture of the brain than using neuronal cells alone. Among the most highly upregulated genes were genes involved in neurogenesis (CHRDL1), axonal guidance (BMP4), neuronal connectivity (PLXDC2), axonogenesis (RTN4R) and astrocyte differentiation (S100B). The 30 biomarkers were further validated by exposure to non-cytotoxic concentrations of two DNT-inducing compounds (valproic acid and methylmercury) and one neurotoxic chemical possessing a possible DNT activity (acrylamide). Twenty-eight of the 30 biomarkers were altered by at least one of the neurotoxic substances, proving the importance of these biomarkers during differentiation. These results suggest that gene expression profiling using a predefined set of biomarkers could be used as a sensitive tool for initial DNT screening of chemicals. Using a predefined set of mRNA biomarkers, instead of the whole genome, makes this model affordable and high-throughput. The use of such models could help speed up the initial screening of substances, possibly indicating alerts that need to be further studied in more sophisticated models.</p></div

PCA plot of independent experimental seed-outs.

<p>The data clusters according to the different contrasts, i.e. 10 days vs 5 days of differentiation, 10 days vs undifferentiated, 5 days vs undifferentiated, showing robustness of the cell model as well as technical reproducibility. The first two principal components explained 72.5% of the information (variation) of the dataset (for PC1: 55.7%, for PC2: 16.8%).</p

Mapping of the 30 genes selected as important for neural differentiation of the C17.2 cell line.

<p>a) Heatmap of the 30 selected genes for the contrasts 10 days of differentiation (Day 10) vs undifferentiated cells (Day 0), 5 days of differentiation (Day 5) vs undifferentiated and 10 days of differentiation vs 5 days of differentiation are illustrated. Genes are ordered according to average log2(fold change) in the contrast Day 10 vs Day 0. b) Map displaying the biological pathways/networks that the selected genes are involved in according to the IPA database as well as after manual review of published literature.</p

Cell viability of the C17.2 cells during exposure of a wide range of concentrations for four different compounds.

<p>The IC10 concentration was calculated and was further used to validate proof of concept of the 30 selected genes. Cells exposed to a) D-mannitol (negative control) b) acrylamide c) methylmercury chloride d) valproic acid sodium salt. The data are presented as the mean of 3 independent experiments preformed in hexaplicates. Results were analyzed using two-way ANOVA followed by Dunnett’s multiple comparisons test. The bars represent the mean ± SEM. *<i>p</i> ≤ 0.05, **<i>p</i> ≤ 0.01, ***<i>p</i> ≤ 0.001 compared to control (cells exposed to only cell medium). The inhibitory concentration 10% (IC10) was determined from nonlinear regression to fit the data to the log(inhibitor) vs response(variable slope) curve using the Hill slope (slope factor), equation Y = Bottom + (Top-Bottom)/(1+10^((LogIC10-X)*HillSlope)) (GraphPad Prism 7.02).</p

The effect of D-mannitol (negative control), acrylamide (ACR), methylmercury chloride (MeHg) and valproic acid sodium salt (VPA) on gene expression, the number of neurons and neurites per cell in differentiating C17.2 cells.

<p>a) RT-qPCR of all 30 genes after 10 days of differentiation and exposure to the IC10 of said compounds (70 μM of ACR, 90 nM of MeHg and 100 μM of VPA. D-mannitol did not show any cytotoxicity for the concentrations used, and 1 mM was chosen for cellular exposure) b) Heatmap of the 30 genes expression during exposure to the 4 compounds. The log2(fold change) for the contrasts as compared to the control (unexposed) are illustrated c) the number of neurons and the number of neurites per cell decreased after exposure to all 3 neurotoxic compounds. The data are presented as the mean of 3 independent experiments performed in duplicates. Results were analyzed using two-way ANOVA followed by Dunnett’s multiple comparisons test. The bars represent the mean ± SEM. *<i>p</i> ≤ 0.05, **<i>p</i> ≤ 0.01, ***<i>p</i> ≤ 0.001 compared to control (cells exposed to only cell medium) or between the 3 different compounds (ACR, MeHg and VPA).</p

Canonical pathway analysis of differentially expressed genes using IPA.

<p>a) Top 20 Canonical pathways as per p-value b) Top 20 as per z-score (a measure of the predicted direction of the pathway activity).</p

Volcano plot showing genes in and outside of cutoff values for differentially expressed genes (i.e. adjusted p-value ≤ 0.05 and absolute log2(fold change) >1).

<p>Red dots represent genes outside of the cutoff values and green dots represents differentially expressed genes at a) 5 days of differentiation vs undifferentiated cells b) 10 days of differentiation vs undifferentiated cells c) 10 days vs 5 days of differentiation d) Venn diagram showing overlap of differentially expressed genes between the different time points.</p

RT-qPCR validation of the 30 selected genes important for differentiation of the C17.2 cell line.

<p>The data are presented as the mean of 3 independent experiments. Results were analyzed using two-way ANOVA followed by Dunnett’s multiple comparisons test. The bars represent the mean ± SEM. *<i>p</i> ≤ 0.05, **<i>p</i> ≤ 0.01, ***<i>p</i> ≤ 0.001 compared to undifferentiated cells (unfilled bar).</p