Abstract
Dioxins and dioxin-like compounds are persistent, bio-accumulative, hydrophobic compounds
that are found all over de world. During the recent years, the levels have decreased, but it is still
important to monitor these compounds in the environment and in humans. In this PhD thesis, the
implementation of a biochemical technique (CALUX) for analyzing dioxin-like activity in
environmental and biological samples is investigated.
CALUX or Chemically Activated LUciferase gene eXpression is a technique to measure the
biologic activity of chemical pollutants by means of a receptor based activation of certain
enzymes in the cell. In the CALUX bioassay, the cells are genetically modified to produce the
enzyme luciferase instead of the naturally existing enzymes. The luciferase production in this
modified cells is quantified though the reaction with the substrate luciferin and the emission of
green light.
In this study, new chemical procedures (extraction and clean up) were developed for the analysis
of human milk and atmospheric deposition samples, while the procedure for the pre-treatment of
the human serum samples was optimized. Cell growth conditions and instrumental parameters
were optimized for each cell line and measurement protocols were developed (choice of dilution
factor for single point analysis versus full dose dilution curves) for the different matrices. Cell
growth conditions and instrumental parameters were optimized for the H1L6.1c3 and H1L7.5c3
mouse hepatoma cell lines and measurement protocols were developed (choice of dilution factor
for single point analysis versus full dose dilution curves) for the different matrices. Finally,
almost 800 serum samples, 100 human milk samples and 100 atmospheric deposition samples
were analyzed with the CALUX bioassay. The results from the biological samples were
compared with other national and international studies and determinants that could influence the
results (age, sex, lifestyle, etc.) were studied. CALUX results from the atmospheric deposition
samples were compared to GC-HRMS data to define the correlation between both techniques.