A study comparing five different cAMP detection technologies in terms of sensitivity, robustness, and feasibility for HTS is presented. In this report, the following methods are described: a nonhomogeneous DELFIA, and the homogeneous methods based on time-resolved fluorescence (HTRF), luminescent singlet oxygen channeling or ALPHAScreen, FP, and high-affinity enzyme complementation. DELFIA had the highest sensitivity, whereas ALPHAScreen and HTRF shared several advantages, including high sensitivity, broad dynamic range, and minimal reagent addition steps. For G(s)-coupled antagonist screens, we found HTRF and ALPHAScreen the more sensitive and HTS-compatible techniques.
High-content screening (HCS) is a powerful technique for monitoring phenotypic responses to treatments on a cellular and subcellular level. Cellular phenotypes can be characterized by multivariate image readouts such as shape, intensity, or texture. The corresponding feature vectors can thus be defined as HCS fingerprints that serve as a powerful biological compound descriptor. Therefore, clustering or classification of HCS fingerprints across compound treatments allows for the identification of similarities in protein targets or pathways. We developed an HCS-based profiling panel that serves as basis for characterizing the mode of action of compounds. This panel measures phenotypic effects in six different compartments of U-2OS cells, namely the nucleus, the cytoplasm, the endoplasmic reticulum, the Golgi apparatus, and the cytoskeleton. We profiled a set of 2,725 well-annotated compounds and clustered their corresponding HCS fingerprints to establish links between predominant cellular phenotypes and cellular processes and protein targets. We found various different clusters enriched for individual targets (e.g., HDAC, HSP90, TOP1, HMGCR, TUB), signaling pathways (e.g., PIK3/AKT/mTOR), or gene sets associated with diseases (e.g., psoriasis, leukemia). Based on this clustering we were able to identify novel compound-target associations for selected compounds such as a submicromolar inhibitory activity of Silmitasertib (a casein kinase inhibitor) on PI3K and mTOR.
bSystemic life-threatening fungal infections represent a significant unmet medical need. Cell-based, phenotypic screening can be an effective means of discovering potential novel antifungal compounds, but it does not address target identification, normally required for compound optimization by medicinal chemistry. Here, we demonstrate a combination of screening, genetic, and biochemical approaches to identify and characterize novel antifungal compounds. We isolated a set of novel non-azole antifungal compounds for which no target or mechanism of action is known, using a screen for inhibition of Saccharomyces cerevisiae proliferation. Haploinsufficiency profiling of these compounds in S. cerevisiae suggests that they target Erg11p, a cytochrome P450 family member, which is the target of azoles. Consistent with this, metabolic profiling in S. cerevisiae revealed a buildup of the metabolic intermediates prior to Erg11p activity, following compound treatment. Further, human cytochrome P450 is also inhibited in in vitro assays by these compounds. We modeled the Erg11p protein based on the human CYP51 crystal structure, and in silico docking of these compounds suggests that they interact with the heme center in a manner similar to that of azoles. Consistent with these docking observations, Candida strains carrying azole-resistant alleles of ERG11 are also resistant to the compounds in this study. Thus, we have identified non-azole Erg11p inhibitors, using a systematic approach for ligand and target characterization.
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