RESUMO
Four phenylpropanoids and a thapsigargin analogue have been isolated from the fruits of Thapsia garganica. A spectroscopic method for elucidating the relative stereochemistry at the two pairs of stereogenic centers in the phenylpropanoids has been developed. The phenylpropanoids were found to be potent cytotoxins.
Assuntos
Fenilpropionatos/isolamento & purificação , Thapsia/química , Tapsigargina/química , Animais , Antineoplásicos Fitogênicos/química , Antineoplásicos Fitogênicos/isolamento & purificação , Antineoplásicos Fitogênicos/farmacologia , Apoptose/efeitos dos fármacos , Linhagem Celular Tumoral , Sobrevivência Celular/efeitos dos fármacos , Humanos , Espectroscopia de Ressonância Magnética , Camundongos , Estrutura Molecular , Fenilpropionatos/química , Fenilpropionatos/farmacologia , Extratos Vegetais/química , Extratos Vegetais/isolamento & purificação , Extratos Vegetais/farmacologia , Coelhos , ATPases Transportadoras de Cálcio do Retículo Sarcoplasmático/antagonistas & inibidores , ATPases Transportadoras de Cálcio do Retículo Sarcoplasmático/metabolismo , Tapsigargina/isolamento & purificação , Tapsigargina/farmacologiaRESUMO
High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called network component analysis, for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. The a priori network structure information is first tested for compliance with a set of identifiability criteria. For networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory networks.