Fenpropathrin induces neurotoxic effects in common carp (Cyprinus carpio L.).

Fenpropathrin (FEN) is a synthetic pyrethroid that has been frequently detected in aquatic environments, yet the neurotoxic impacts and underlying mechanisms on nontarget organisms are lacking. In this experiment, common carp were exposed to 0.45 and 1.35 µg/L FEN for 14 d and exhibited abnormal locomotor behaviour. Biochemical and molecular analysis results indicated that FEN altered the contents of tight junction proteins (claudin-1, occludin, and ZO-1), disturbed Na+-K+-ATPase and AChE activities, caused abnormal expression of neurotransmitters (ACh, DA, GABA, 5-HT, and glutamate) and caused histological damage in the brain, suggesting that FEN may damage the blood-brain barrier and induce neurotoxicity in carp. Furthermore, FEN also promoted an increase in ROS, changed SOD and CAT activities, and generally upregulated the contents of MDA, 8-OHdG, and protein carbonyl in the brain, indicating that FEN can induce oxidative stress and cause damage to lipids, DNA, and proteins. Moreover, inflammation-related indicators (TNF-α, IL-1ß, IL-6, and IL-10), mitophagy-related genes (PINK1, parkin, ulk1, beclin1, LC3, p62, tfeb, and atg5), and apoptosis-related parameters (p53, bax, bcl-2, caspase-3, caspase-8, and caspase-9) were also significantly changed, suggesting that inflammation, mitophagy, and apoptosis may participate in FEN-induced neurotoxicity in carp. This study refines the understanding of the toxicity mechanism of FEN and thus provides data support for the risk assessment of FEN.

Identification and analysis of key genes in osteosarcoma using bioinformatics.

Osteosarcoma (OS) is an invasive malignant neoplasm of the bones. The present study identified and analyzed key genes associated with OS. Expression profiling of the dataset GSE49003, which included 6 metastatic and 6 non-metastatic OS cell lines and was obtained from the Gene Expression Omnibus, was performed. Following data preprocessing, the differentially expressed genes (DEGs) were selected using the limma package in R. Subsequently, bidirectional hierarchical clustering using the pheatmap package in R and an unpaired Students' t-test was performed for the DEGs. Based on the Search Tool for the Retrieval of Interacting Genes database and Cytoscape software, a protein-protein interaction (PPI) network for the DEGs was constructed. Using Database for Annotation, Visualization and Integrated Discovery software and the Kyoto Encyclopedia of Genes and Genomes Orthology Based Annotation System server, functional and pathway enrichment analyses were performed for the DEGs corresponding to the proteins of the network. In addition, the transcription factors (TFs) and CpG islands of the gene promoter were searched for using the TRANSFAC database and CpG Island Searcher software, respectively. A total of 323 DEGs were identified between the metastatic and non-metastatic samples. In the PPI network, upregulated epidermal growth factor receptor (EGFR) exhibits a high degree and was therefore highly interconnected with other proteins. Enrichment analysis revealed that EGFR was enriched in cytoskeleton organization, organic substance response and the signaling pathway of focal adhesion. The TFs early growth response 1, nuclear factor-κB complex subunits, peroxisome proliferator activated receptor α, signal transducer and activator of transcription 3 and MYC proto-oncogene were identified in the EGFR promoter region. Furthermore, multiple CpG islands, starting from the 400 bp of the EGFR promoter sequence, were predicted. Methylated modification of the CpG islands in the EGFR promoter may help to regulate EGFR expression. The TFs identified in the EGFR promoter may function in the progression of OS.

Identification of key gene networks associated with fracture healing using αSMA­labeled progenitor cells.

The aim of the present study was to investigate the key gene network in fracture healing. The dataset GSE45156 was downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using the linear models for microarray data package of Bioconductor. Subsequently, Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted for DEGs in day 2 and 6 fractured samples via the Database for Annotation, Visualization and Integrated Discovery. Furthermore, protein­protein interactions (PPIs) of DEGs were analyzed and a PPI network was constructed. A total of 774 and 1,172 DEGs were identified in day 2 and 6 fractured samples, respectively, compared with unfractured controls. Of the DEGs in day 2 and 6 fractured samples, various upregulated DEGs, including protein kinase C α (Prkca) and B­cell lymphoma antagonist/killer 1 were significantly enriched in GO terms associated with cell death, and certain downregulated DEGs, including fms­related tyrosine kinase 1 (Flt1), nitric oxide synthase 3 (Nos3), bone morphogenetic protein 4 (Bmp4) and Notch1 were enriched in GO terms associated with angiogenesis. Furthermore, a series of downregulated DEGs were enriched in the Notch signaling pathway, including hes family bHLH transcription factor 1 and Notch1. Certain DEGs had a high degree and interacted with each other, including Flt1, Nos3, Bmp4 and Notch1, and Prkca and ras­related C3 botulinum toxin substrate 3. The up and downregulated DEGs may exert critical functions by interactively regulating angiogenesis or apoptosis.

Screening for metastatic osteosarcoma biomarkers with a DNA microarray.

OBJECTIVE: The aim of this study was to screen for possible biomarkers of metastatic osteosarcoma (OS) using a DNA microarray. METHODS: We downloaded the gene expression profile GSE49003 from Gene Expression Omnibus database, which included 6 gene chips from metastatic and 6 from non-metastatic OS patients. The R package was used to screen and identify differentially expressed genes (DEGs) between metastatic and non-metastatic OS patients. Then we compared the expression of DEGs in the two groups and sub-grouped into up-regulated and down-regulated, followed by functional enrichment analysis using the DAVID system. Subsequently, we constructed an miRNA-DEG regulatory network with the help of WebGestalt software. RESULTS: A total of 323 DEGs, including 134 up-regulated and 189 down-regulated, were screened out. The up-regulated DEGs were enriched in 14 subcategories and most significantly in cytoskeleton organization, while the down-regulated DEGs were prevalent in 13 subcategories, especially wound healing. In addition, we identified two important miRNAs (miR-202 and miR-9) pivotal for OS metastasis, and their relevant genes, CALD1 and STX1A. CONCLUSIONS: MiR-202 and miR-9 are potential key factors affecting the metastasis of OS and CALD1 and STX1A may be possible targets beneficial for the treatment of metastatic OS. However, further experimental studies are needed to confirm our results.