RESUMEN
Modern genomics technologies generate huge data sets creating a demand for systems level, experimentally verified, analysis techniques. We examined the transcriptional response to DNA damage in a human T cell line (MOLT4) using microarrays. By measuring both mRNA accumulation and degradation over a short time course, we were able to construct a mechanistic model of the transcriptional response. The model predicted three dominant transcriptional activity profiles-an early response controlled by NFkappaB and c-Jun, a delayed response controlled by p53, and a late response related to cell cycle re-entry. The method also identified, with defined confidence limits, the transcriptional targets associated with each activity. Experimental inhibition of NFkappaB, c-Jun and p53 confirmed that target predictions were accurate. Model predictions directly explained 70% of the 200 most significantly upregulated genes in the DNA-damage response. Genome-wide transcriptional modelling (GWTM) requires no prior knowledge of either transcription factors or their targets. GWTM is an economical and effective method for identifying the main transcriptional activators in a complex response and confidently predicting their targets.
Asunto(s)
Genoma Humano/genética , Modelos Genéticos , Transcripción Genética/genética , Línea Celular , Análisis por Conglomerados , Biología Computacional , Daño del ADN/genética , Perfilación de la Expresión Génica , Humanos , Análisis de Secuencia por Matrices de Oligonucleótidos , Estabilidad del ARN/efectos de la radiación , ARN Mensajero/genética , ARN Mensajero/metabolismo , Radiación Ionizante , Reproducibilidad de los Resultados , Factores de Tiempo , Factores de Transcripción/metabolismo , Transcripción Genética/efectos de la radiación , Proteína p53 Supresora de Tumor/metabolismo , Regulación hacia Arriba/genética , Regulación hacia Arriba/efectos de la radiaciónRESUMEN
BACKGROUND: Gene expression microarray data is notoriously subject to high signal variability. Moreover, unavoidable variation in the concentration of transcripts applied to microarrays may result in poor scaling of the summarized data which can hamper analytical interpretations. This is especially relevant in a systems biology context, where systematic biases in the signals of particular genes can have severe effects on subsequent analyses. Conventionally it would be necessary to replace the mismatched arrays, but individual time points cannot be rerun and inserted because of experimental variability. It would therefore be necessary to repeat the whole time series experiment, which is both impractical and expensive. RESULTS: We explain how scaling mismatches occur in data summarized by the popular MAS5 (GCOS; Affymetrix) algorithm, and propose a simple recursive algorithm to correct them. Its principle is to identify a set of constant genes and to use this set to rescale the microarray signals. We study the properties of the algorithm using artificially generated data and apply it to experimental data. We show that the set of constant genes it generates can be used to rescale data from other experiments, provided that the underlying system is similar to the original. We also demonstrate, using a simple example, that the method can successfully correct existing imbalances in the data. CONCLUSION: The set of constant genes obtained for a given experiment can be applied to other experiments, provided the systems studied are sufficiently similar. This type of rescaling is especially relevant in systems biology applications using microarray data.
Asunto(s)
Algoritmos , Perfilación de la Expresión Génica , Análisis de Secuencia por Matrices de Oligonucleótidos , Línea Celular , Perfilación de la Expresión Génica/métodos , Regulación de la Expresión Génica , Humanos , Modelos Genéticos , Modelos Estadísticos , Análisis de Secuencia por Matrices de Oligonucleótidos/métodos , Reproducibilidad de los Resultados , Proyectos de Investigación , Linfocitos T/metabolismo , Linfocitos T/efectos de la radiaciónRESUMEN
Full exploitation of microarray data requires hidden information that cannot be extracted using current analysis methodologies. We present a new approach, hidden variable dynamic modeling (HVDM), which derives the hidden profile of a transcription factor from time series microarray data, and generates a ranked list of predicted targets. We applied HVDM to the p53 network, validating predictions experimentally using small interfering RNA. HVDM can be applied in many systems biology contexts to predict regulation of gene activity quantitatively.