Stress-responses to single and mixed toxicants in trasgenic strains of the nematode Caenorhabditis elegans

Abstract

Organisms exposed to toxicants activate various defensive pathways such as the heat shock, xenobiotic, oxidative and metal stress responses. These sub-networks (pathways) involving groups of stress response genes act together in an integrated manner and hence constitute an overall Stress Response Network (SRN). My study aims at understanding this network of stress pathways by monitoring stress response reporter-gene outputs during exposure to single chemicals (11 heavy metals and 12 pesticides) and several mixtures, using 24 transgenic GFP reporter strains of the nematode Caenorhabditis elegans. Major stress responsive genes belonging to the heat-shock, metal-response and oxidative stress groups - plus selected genes from the xenobiotic response pathways, were selected as representative genes for this work. In addition, GFP transgenic strains for the key transcription factors that act in these stress pathways, along with the master stress regulators DAF-16 and CEP-1, were also studied. My study focused mainly on genes involved in the heat-shock stress and xenobiotic stress pathways, but responses involving other pathways are also reported, with appropriate attribution to the respective authors. Toxicant exposures of these arrays of transgenic strains were performed initially at 3 time-points on different dose ranges of single toxicants, selected on the basis of their known toxicity or widespread use (e.g. certain pesticides) in the environment. As part of a broader UKIERI project, the single toxicant exposure data were used in generating mathematical models of the stress-responsive sub-networks (performed by mathematicians Dr. Haque and Prof. King, School of Mathematical Sciences, University of Nottingham), which in turn were used to predict the likely outcome of exposures to simple toxicant mixtures. Organisms are often exposed to mixtures of toxicants in the environment and it is therefore important to understand and is possible predict the effects of toxicant mixtures. These models predicted different responses, e.g. additive effects for chemically similar (e.g. divalent) metals versus interfering effects for chemically dissimilar metals (e.g. divalent plus trivalent) in the case of the metallothionein (MTL) sub-network. By contrast, the heat-shock (HSP) sub- network model predicted only additive responses, irrespective of chemical similarity. Laboratory testing of simple binary mixtures using GFP reporter strains confirmed all of these model predictions. RNA interference was also used to knockdown key transcription factors in selected stress pathways (e.g. HSF-l, ELT-2, NHR-8 and DAF-16) in order to confirm the role of these factors in toxicant -induced gene expression. My study also investigated the effects of more complex chemical mixtures, such as soil water samples from a former mine site (P79) and two agricultural sites (P73 and P74), derived from the Spanish ECOMETRISK project. Our results identified responses to these complex mixtures that broadly agree (though only at certain time-points) with the mathematical model predictions for binary metal mixtures, in the case of soil sample P79. Interestingly, strong GFP induction was observed for several stress genes for the two agricultural soil samples and this was caused by the organic components present in these soil water samples rather than by metals. The source and nature of these organic contaminants in agricultural soils remains to be determined, though pesticide residues seem a likely culprit.EThOS - Electronic Theses Online ServiceGBUnited Kingdo