Combination of yeast-based in vitro screens with high-performance thin-layer chromatography as a novel tool for the detection of hormonal and dioxin-like compounds

The combination of classic in vitro bioassays with high-performance thin-layer chromatography (HPTLC) is a promising technique to directly link chemical analysis of contaminants to their potential adverse biological effects. With respect to endocrine disruption, much work is focused on estrogenicity. While a direct combination of HPTLC and the yeast estrogen screen is already developed, it is well accepted that further endocrine effects are relevant for monitoring environmental wellbeing. Here we show that non-estrogenic specific biological endpoints, (partly) related to the endocrine system, can also be addressed by combining respective yeast reporter gene assays with HPTLC to support effect-directed analysis (EDA). These are: androgenicity (YAS), thyroidogenicity (YTS), dioxin-like effects (YDS), effects on the vitamin D (YVS) and the retinoic acid receptor (YRaS). A proof of principle is demonstrated within this study by the characterization of dose-dependent responses to different model compounds for the respective receptors with and without chromatographic development of the HPTLC-plate. Limits of quantification (LOQ) for several model compounds were determined, e.g. 37 pg for testosterone (p-YAS), 0.476 ng for β-naphthoflavone (p-YDS) and 1.02 ng for calcipotriol hydrate (p-YVS) with chromatographic development. The LOQ for p-YTS and p-YRaS were 10.16 pg for 3,3’,5-triiodothyroacetic acid (p-YTS) and 0.41 pg for tamibarotene (p-YRaS), without chromatographic separation. Furthermore, we challenged the developed methodology using environmental samples, demonstrating an elimination efficiency of androgenic activity from municipal wastewater by a wastewater treatment plant between 99.4 and 100%. We anticipate our methodology to substantially broaden the spectrum of specific endpoints combined with HPTLC for an efficient and robust screening of environmental samples to guide a subsequent in-depth EDA.