Transgenic nematodes as biosensors for metal stress in soil pore water samples - Anbalagan, Charumathi; Lafayette, Ivan; Antoniou-Kourounioti, Melissa; Haque, Mainul; King, John; Johnsen, Bob; Baillie, David; Gutierrez, Carmen; Martin, Jose A. Rodriguez; de Pomerai, David (Springer Verlag, 2012)
Caenorhabditis elegans strains carrying stress-reporter green fluorescent protein transgenes were used to explore patterns of response to metals. Multiple stress pathways were induced at high doses by most metals tested, including members of the heat shock, oxidative stress, metallothionein (mtl) and xenobiotic response gene families. A mathematical model (to be published separately) of the gene regulatory circuit controlling mtl production predicted that chemically similar divalent metals (classic inducers) should show additive effects on mtl gene induction, whereas chemically dissimilar metals should show interference. These predictions were verified experimentally; thus cadmium and mercury showed additive effects, whereas ferric iron (a weak inducer) significantly reduced the effect of mercury. We applied a similar battery of tests to diluted samples of soil pore water extracted centrifugally after mixing 20% w/w ultrapure water with air-dried soil from an abandoned lead/zinc mine in the Murcia region of Spain. In addition, metal contents of both soil and soil pore water were determined by ICP-MS, and simplified mixtures of soluble metal salts were tested at equivalent final concentrations. The effects of extracted soil pore water (after tenfold dilution) were closely mimicked by mixtures of its principal component ions, and even by the single most prevalent contaminant (zinc) alone, though other metals modulated its effects both positively and negatively. In general, mixtures containing similar (divalent) metal ions exhibited mainly additive effects, whereas admixture of dissimilar (e.g. trivalent) ions often resulted in interference, reducing overall levels of stress-gene induction. These findings were also consistent with model predictions.