6 research outputs found
Headspace-Free Setup of <i>in Vitro</i> Bioassays for the Evaluation of Volatile Disinfection By-Products
The
conventional setup of <i>in vitro</i> bioassays in microplates
does not prevent the loss of volatile compounds, which hampers the
toxicological characterization of waterborne volatile disinfection
by-products (DBPs). To minimize the loss of volatile test chemicals,
we adapted four <i>in vitro</i> bioassays to a headspace-free
setup using eight volatile organic compounds (four trihalomethanes,
1,1-dichloroethene, bromoethane, and two haloacetonitriles) that cover
a wide range of air–water partition coefficients. The nominal
effect concentrations of the test chemicals decreased by up to three orders
of magnitude when the conventional setup was changed to a headspace-free
setup for the bacterial cytotoxicity assay using bioluminescence inhibition
of <i>Vibrio fischeri</i>. The increase of apparent sensitivity
correlated significantly with the air–water partition coefficient.
Purge and trap GC/MS analysis revealed a reduced loss of dosed volatile
compounds in the headspace free setup (78–130% of nominal concentration)
compared to a substantial loss in the conventional set up (2–13%
of the nominal concentration). The experimental effect concentrations
converged with the headspace-free setup to the effect concentrations
predicted by a QSAR model, confirming the suitability of the headspace-free
approach to minimize the loss of volatile test chemicals. The analogue
headspace-free design of the bacterial bioassays for genotoxicity
(umuC assay) and mutagenicity (Ames fluctuation assay) increased the
number of compounds detected as genotoxic or mutagenic from one to
four and zero to two, respectively. In a bioassay with a mammalian
cell line applied for detecting the induction of the Nrf-2-mediated
oxidative stress response (AREc32 assay), the headspace-free setup
improved the apparent sensitivity by less than one order of magnitude,
presumably due to the retaining effect of the serum components in
the medium, which is also reflected in the reduced aqueous concentrations
of compounds. This study highlights the importance of adapting bioanalytical
test setups when volatile/semivolatile compounds are present in the
sample to avoid the loss of chemicals and thus to avoid underestimating
the toxicity of mixtures and complex environmental samples
Multiple linear regression models testing the effect of environmental parameters and contaminants on biotic response variables: the total number of individuals and taxa, Simpson and Shannon diversity, number of EPT taxa and the saprobic index.
<p>Given are df-, R<sup>2</sup>-, F- and p-values for full models after stepwise deletion of non-significant terms (n.s.) and of significant model parameters.</p>★<p>, p<0.05;</p>★★<p>, p<0.01;</p>★★★<p>, p<0.001; n.a., not available.</p
NMDS biplot of taxa and environmental variables.
<p>Displayed are variables with a significant impact (p<0.05) for sampling campaign in spring (A) and autumn (B). HM, components of the principal component analysis (PCA) with heavy metals; OC, components of the PCA with organic contaminants; structure, structural degradation. Spring: two convergent solutions, two dimensions, stress = 0.17; autumn: two convergent solutions, two dimensions, stress = 0.21).</p
Number of EPT taxa (Ephemeroptera, Plecoptera, Trichoptera) correlating with the first component of the PCA with organic contaminants (OC1).
<p>Displayed are results for sampling campaign in spring (A) and autumn (B). Please note different scaling of y-axes in A and B.</p
River systems with respective streams, abbreviations (abbr.), and number of sampling sites (no.).
<p>River systems with respective streams, abbreviations (abbr.), and number of sampling sites (no.).</p