ABSTRACT
Arsenite is one of the most toxic chemical substances known and is assumed to exert detrimental effects on viability even at lowest concentrations. By contrast and unlike higher concentrations, we here find that exposure to low-dose arsenite promotes growth of cultured mammalian cells. In the nematode C. elegans, low-dose arsenite promotes resistance against thermal and chemical stressors and extends lifespan of this metazoan, whereas higher concentrations reduce longevity. While arsenite causes a transient increase in reactive oxygen species (ROS) levels in C. elegans, co-exposure to ROS scavengers prevents the lifespan-extending capabilities of arsenite, indicating that transiently increased ROS levels act as transducers of arsenite effects on lifespan, a process known as mitohormesis. This requires two transcription factors, namely DAF-16 and SKN-1, which employ the metallothionein MTL-2 as well as the mitochondrial transporter TIN-9.1 to extend lifespan. Taken together, low-dose arsenite extends lifespan, providing evidence for nonlinear dose-response characteristics of toxin-mediated stress resistance and longevity in a multicellular organism.
Subject(s)
Arsenites/pharmacology , Caenorhabditis elegans/drug effects , Hormesis , Longevity/drug effects , Mitochondria/drug effects , Teratogens/pharmacology , 3T3 Cells , Animals , Caenorhabditis elegans/metabolism , Caenorhabditis elegans Proteins/metabolism , Cell Line , DNA-Binding Proteins/metabolism , Forkhead Transcription Factors , Hep G2 Cells , Humans , Metallothionein/metabolism , Mice , Mitochondria/metabolism , Oxidative Stress/drug effects , Reactive Oxygen Species , Superoxide Dismutase/metabolism , Transcription Factors/metabolism , Transcription, GeneticABSTRACT
INTRODUCTION: The pollution of soil and environment as a result of human activity is a major problem. Nowadays, the determination of local contaminations is of interest for environmental remediation. These hotspots can have various toxic effects on plants, animals, humans, and the whole ecological system. However, economical and juridical consequences are also possible, e.g., high costs for remediation measures. MATERIALS AND METHODS: In this study three sampling strategies (simple random sampling, stratified sampling, and systematic sampling) were applied on randomly distributed hotspot contaminations to prove their efficiency in term of finding hotspots. The results were used for the validation of a computerized simulation. RESULTS AND CONCLUSION: This application can simulate the contamination on a field, the sampling pattern, and a virtual sampling. A constant hit rate showed that none of the sampling patterns could reach better results than others. Furthermore, the uncertainty associated with the results is described by confidence intervals. It is to be considered that the uncertainty during sampling is enormous and will decrease slightly, even the number of samples applied was increased to an unreasonable amount. It is hardly possible to identify the exact number of randomly distributed hotspot contaminations by statistical sampling. But a range of possible results could be calculated. Depending on various parameters such as shape and size of the area, number of hotspots, and sample quantity, optimal sampling strategies could be derived. Furthermore, an estimation of bias arising from sampling methodology is possible. The developed computerized simulation is an innovative tool for optimizing sampling strategies in terrestrial compartments for hotspot distributions.
Subject(s)
Computer Simulation , Environmental Monitoring/methods , Environmental Pollution/analysis , Soil Pollutants/analysis , Confidence Intervals , Environmental Restoration and Remediation/methods , UncertaintyABSTRACT
The purpose of detecting trace concentrations of analytes often is hindered by occurring noise in the signal curves of analytical methods. This is also a problem when different arsenic species (inorganic As(III) and As(V) as well as organic dimethylarsinic acid and arsenobetaine) are to be determined in food and feeding stuff by HPLC-ICP-MS, which is the basis of this work. In order to improve the detection power, methods of signal treatment may be applied. We show a comparison of convolution with Gaussian distribution curves, Fourier transform, and wavelet transform. It is illustrated how to estimate decisive parameters for these techniques. All methods result in improved limits of detection. Furthermore, applying baselines and evaluating peaks thoroughly is facilitated. However, there are differences. Convolution with Gaussian distribution curves may be applied, but Fourier transform shows better results of improvement. The best of the three is wavelet transform, whereby the detection power is improved by factors of about 6.
