Inorganic arsenic alters the development of dopaminergic neurons but not serotonergic neurons and induces motor neuron development via Sonic hedgehog pathway in zebrafish.

The mechanism of inorganic arsenic-induced neurotoxicity at the cellular level is not known. In zebrafish, teratological effects of inorganic arsenic have been shown at various concentrations. Here, we used similar concentrations of inorganic arsenic to evaluate the effects on specific neuron types. Exposure of zebrafish embryos at 5 h post fertilization (hpf) to sodium arsenite induced developmental toxicity (reduced body length) in 72 hpf larvae, beginning at a concentration of 300 mg/L concentration. Mortality or overt morphological deformity was detected at 500 mg/L sodium arsenite. While 200 mg/L sodium arsenite induced development of tyrosine hydroxylase-positive (dopaminergic) neurons, there was no significant effect on the development of 5-hydroxytryptamine (serotonergic) neurons. Sodium arsenite reduced acetylcholinesterase activity. In the hb9-GFP transgenic larvae, both 200 and 400 mg/L sodium arsenite produced supernumerary motor neurons in the spinal cord. Inhibition of the Sonic hedgehog (Shh) pathway that is essential for motor neuron development, by Gant61, prevented sodium arsenite-induced supernumerary motor neuron development. Inductively coupled plasma mass spectrometry (ICP-MS) revealed that with 200 mg/L and 400 mg/L sodium arsenite treatment, each larva had an average of 387.8 pg and 847.5 pg arsenic, respectively. The data show for the first time that inorganic arsenic alters the development of dopaminergic and motor neurons in the zebrafish larvae and the latter occurs through the Shh pathway. These results may help understand why arsenic-exposed populations suffer from psychiatric disorders and motor neuron disease and Shh may, potentially, serve as a plasma biomarker of arsenic toxicity.

Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food.

Persistent Organic Pollutants (POPs) are a serious food safety concern due to their persistence and toxic effects. To promote food safety and protect human health, it is important to understand the sources of POPs and how to minimize human exposure to these contaminants. The POPs Program within the U.S. Food and Drug Administration (FDA), manually evaluates congener patterns of POPs-contaminated samples and sometimes compares the finding to other previously analyzed samples with similar patterns. This manual comparison is time consuming and solely depends on human expertise. To improve the efficiency of this evaluation, we developed software to assist in identifying potential sources of POPs contamination by detecting similarities between the congener patterns of a contaminated sample and potential environmental source samples. Similarity scores were computed and used to rank potential source samples. The software has been tested on a diverse set of incurred samples by comparing results from the software with those from human experts. We demonstrated that the software provides results consistent with human expert observation. This software also provided the advantage of reliably evaluating an increased sample lot which increased overall efficiency.

QUICK: Quality and Usability Investigation and Control Kit for Mass Spectrometric Data from Detection of Persistent Organic Pollutants.

Persistent organic pollutants (POPs) cause a significant public and environmental health concern due to their toxicity, long-range transportability, persistence, and bioaccumulation. The US Food and Drug Administration (FDA) has a program to monitor POPs in human and animal foods at ultra-trace levels, using gas chromatography coupled with mass spectrometry (GC-MS). Stringent quality control procedures are practiced within this program, ensuring the reliability and accuracy of these POP results. Due to the complexity of this program's quality control (QC), the decision-making process for data usability was very time-consuming, upward of three analyst hours for a batch of six extracts. We significantly reduced this time by developing a software kit, written in Python, to evaluate instrument and sample QC, along with data usability. A diverse set of 45 samples were tested using our software, QUICK (Quality and Usability Investigation and Control Kit), that resulted in equivalent results provided by a human reviewer. The software improved the efficiency of the analytical process by reducing the need for user intervention, while simultaneously recognizing a 95% decrease in data reduction time, from 3 hours to 10 minutes.