Effects of cylindrospermopsin, chlorpyrifos and their combination in a SH-SY5Y cell model concerning developmental neurotoxicity.

The cyanotoxin cylindrospermopsin (CYN) has been postulated to cause neurotoxicity, although the studies in this concern are very few. In addition, some studies in vitro indicate its possible effects on development. Furthermore, pesticides can be present in the same environmental samples as cyanotoxins. Therefore, chlorpyrifos (CPF) has been one of the most common pesticides used worldwide. The aim of this report was to study the effects of CYN, isolated and in combination with CPF, in a developmental neurotoxicity in vitro model. The human neuroblastoma SH-SY5Y cell line was exposed during 6 days of differentiation to both toxics to study their effects on cell viability and neurite outgrowth. To further evaluate effects of both toxicants on cholinergic signaling, their agonistic and antagonistic activities on the α7 homomeric nicotinic acetylcholine receptor (nAChR) were studied upon acute exposure. Moreover, a transcriptomic analysis by qPCR was performed after 6 days of CYN-exposure during differentiation. The results showed a concentration-dependent decrease on both cell viability and neurite outgrowth for both toxics isolated, leading to effective concentration 20 (EC20) values of 0.35 µM and 0.097 µM for CYN on cell viability and neurite outgrowth, respectively, and 100 µM and 58 µM for CPF, while the combination demonstrated no significant variations. In addition, 95 µM and 285 µM CPF demonstrated to act as an antagonist to nicotine on the nAChR, although CYN up to 2.4 µM had no effect on the efficacy of these receptors. Additionally, the EC20 for CYN (0.097 µM) on neurite outgrowth downregulated expression of the 5 genes NTNG2 (netrin G2), KCNJ11 (potassium channel), SLC18A3 (vesicular acetylcholine transporter), APOE (apolipoprotein E), and SEMA6B (semaphorin 6B), that are all important for neuronal development. Thus, this study points out the importance of studying the effects of CYN in terms of neurotoxicity and developmental neurotoxicity.

Evaluation of mRNA markers in differentiating human SH-SY5Y cells for estimation of developmental neurotoxicity.

Current guidelines for developmental neurotoxicity (DNT) evaluation are based on animal models. These have limitations so more relevant, efficient and robust approaches for DNT assessment are needed. We have used the human SH-SY5Y neuroblastoma cell model to evaluate a panel of 93 mRNA markers that are frequent in Neuronal diseases and functional annotations and also differentially expressed during retinoic acid-induced differentiation in the cell model. Rotenone, valproic acid (VPA), acrylamide (ACR) and methylmercury chloride (MeHg) were used as DNT positive compounds. Tolbutamide, D-mannitol and clofibrate were used as DNT negative compounds. To determine concentrations for exposure for gene expression analysis, we developed a pipeline for neurite outgrowth assessment by live-cell imaging. In addition, cell viability was measured by the resazurin assay. Gene expression was analyzed by RT-qPCR after 6 days of exposure during differentiation to concentrations of the DNT positive compounds that affected neurite outgrowth, but with no or minimal effect on cell viability. Methylmercury affected cell viability at lower concentrations than neurite outgrowth, hence the cells were exposed with the highest non-cytotoxic concentration. Rotenone (7.3 nM) induced 32 differentially expressed genes (DEGs), ACR (70 µM) 8 DEGs, and VPA (75 µM) 16 DEGs. No individual genes were significantly dysregulated by all 3 DNT positive compounds (p < 0.05), but 9 genes were differentially expressed by 2 of them. Methylmercury (0.8 nM) was used to validate the 9 DEGs. The expression of SEMA5A (encoding semaphorin 5A) and CHRNA7 (encoding nicotinic acetylcholine receptor subunit α7) was downregulated by all 4 DNT positive compounds. None of the DNT negative compounds dysregulated any of the 9 DEGs in common for the DNT positive compounds. We suggest that SEMA5A or CHRNA7 should be further evaluated as biomarkers for DNT studies in vitro since they also are involved in neurodevelopmental adverse outcomes in humans.

Detection of vancomycin resistance genes combined with typing of Enterococci by means of multiplex PCR amplification and multiple primer DNA sequencing.

A multiplex PCR assay for the detection of vancomycin resistance (van) genes in enterococci was established. Primers targeting the 16S rRNA gene were included in the reaction mixture. Multiple-primer DNA sequencing of the PCR products provided species identification through partial nucleotide sequences of 16S rRNA genes, as well as confirmation of the correct identification of vanA, vanB, vanC-1, and vanC-2/3 genotypes. Thirty-nine enterococcal clinical isolates and type strains were examined for the presence of vancomycin resistance determinants. Twelve other isolates from a clinical reference collection (some of them having vanA, vanB, vanC-1, or vanC-2/3 genotypes) were used as controls. Hybridization and partial DNA sequence analysis of multiplex PCR products revealed that none of the clinical isolates had a vanA genotype and only one had a vanB genotype. vanC-1 was found in three clinical isolates, and vanC-2/3 in one. Results obtained with the reference and type strains were in agreement with earlier results.

A program for sampling, assessment of stationarity and variability analysis of neuronal spike trains, run under control of a time-sharing system.

A system has been developed for off-line analysis of discharge characteristics of continuously firing nerve cells. The programs, which are mainly written in FORTRAN IV, determine mean firing rates, variability and other parameters of the impulse activity, and construct interspike interval histograms. They are run under time-sharing on a small laboratory computer. To disclose possible non-stationarities in the data all interspike interval samples are analysed with the non-parametric Kolmogorov-Smirnov test. This test is very powerful in detecting any differences between two samples and is still very easy to implement in programming. Raw data and computed statistics are stored in random access data files.