Identification of RNA biomarkers for chemical safety screening in mouse embryonic stem cells using RNA deep sequencing analysis

Although it is not yet possible to replace in vivo animal testing completely, the need for a more efficient method for toxicity testing, such as an in vitro cell-based assay, has been widely acknowledged. Previous studies have focused on mRNAs as biomarkers; however, recent studies have revealed that non-coding RNAs (ncRNAs) are also efficient novel biomarkers for toxicity testing. Here, we used deep sequencing analysis (RNA-seq) to identify novel RNA biomarkers, including ncRNAs, that exhibited a substantial response to general chemical toxicity from nine chemicals, and to benzene toxicity specifically. The nine chemicals are listed in the Japan Pollutant Release and Transfer Register as class I designated chemical substances. We used undifferentiated mouse embryonic stem cells (mESCs) as a simplified cell-based toxicity assay. RNA-seq revealed that many mRNAs and ncRNAs responded substantially to the chemical compounds in mESCs. This finding indicates that ncRNAs can be used as novel RNA biomarkers for chemical safety screening

Classification of chemical compounds based on the correlation between \textit{in vitro} gene expression profiles

Toxicity evaluation of chemical compounds has traditionally relied on animal experiments;however, the demand for non-animal-based prediction methods for toxicology of compounds is increasing worldwide. Our aim was to provide a classification method for compounds based on \textit{in vitro} gene expression profiles. The \textit{in vitro} gene expression data analyzed in the present study was obtained from our previous study. The data concerned nine compounds typically employed in chemical management.We used agglomerative hierarchical clustering to classify the compounds;however, there was a statistical difficulty to be overcome.We needed to properly extract RNAs for clustering from more than 30,000 RNAs. In order to overcome this difficulty, we introduced a combinatorial optimization problem with respect to both gene expression levels and the correlation between gene expression profiles. Then, the simulated annealing algorithm was used to obtain a good solution for the problem. As a result, the nine compounds were divided into two groups using 1,000 extracted RNAs. Our proposed methodology enables read-across, one of the frameworks for predicting toxicology, based on \textit{in vitro} gene expression profiles.Comment: 13pages, 7 figure

Gated Silicon Drift Detector Fabricated from a Low-Cost Silicon Wafer

Inexpensive high-resolution silicon (Si) X-ray detectors are required for on-site surveys of traces of hazardous elements in food and soil by measuring the energies and counts of X-ray fluorescence photons radially emitted from these elements. Gated silicon drift detectors (GSDDs) are much cheaper to fabricate than commercial silicon drift detectors (SDDs). However, previous GSDDs were fabricated from $10$-k$\Omega \cdot$cm Si wafers, which are more expensive than $2$-k$\Omega \cdot$cm Si wafers used in commercial SDDs. To fabricate cheaper portable X-ray fluorescence instruments, we investigate GSDDs formed from $2$-k$\Omega \cdot$cm Si wafers. The thicknesses of commercial SDDs are up to $0.5$ mm, which can detect photons with energies up to $27$ keV, whereas we describe GSDDs that can detect photons with energies of up to $35$ keV. We simulate the electric potential distributions in GSDDs with Si thicknesses of $0.5$ and $1$ mm at a single high reverse bias. GSDDs with one gate pattern using any resistivity Si wafer can work well for changing the reverse bias that is inversely proportional to the resistivity of the Si wafer

Identification of RNA biomarkers for chemical safety screening in mouse embryonic stem cells using RNA deep sequencing analysis

<div><p>Although it is not yet possible to replace in vivo animal testing completely, the need for a more efficient method for toxicity testing, such as an in vitro cell-based assay, has been widely acknowledged. Previous studies have focused on mRNAs as biomarkers; however, recent studies have revealed that non-coding RNAs (ncRNAs) are also efficient novel biomarkers for toxicity testing. Here, we used deep sequencing analysis (RNA-seq) to identify novel RNA biomarkers, including ncRNAs, that exhibited a substantial response to general chemical toxicity from nine chemicals, and to benzene toxicity specifically. The nine chemicals are listed in the Japan Pollutant Release and Transfer Register as class I designated chemical substances. We used undifferentiated mouse embryonic stem cells (mESCs) as a simplified cell-based toxicity assay. RNA-seq revealed that many mRNAs and ncRNAs responded substantially to the chemical compounds in mESCs. This finding indicates that ncRNAs can be used as novel RNA biomarkers for chemical safety screening.</p></div

Genes upregulated in mouse embryonic stem cells exposed to benzene (Top 30).

<p>Genes upregulated in mouse embryonic stem cells exposed to benzene (Top 30).</p

GO terms for genes upregulated in mouse embryonic stem cells exposed to benzene (Top 30).

<p>GO terms for genes upregulated in mouse embryonic stem cells exposed to benzene (Top 30).</p

Chemical structures used in the present study.

<p>Chemical structures used in the present study.</p