4 research outputs found

    Metilación del ADN en posiciones individuales del genoma humano dentro de contextos epigenéticos regionales

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    [spa] La metilación del ADN es una de las modificaciones epigenéticas más estudiadas y cuyo mecanismo de acción es más conocido. Consiste en la transferencia enzimática de un grupo metilo a la posición 5' de la citosina. Resulta la principal modificación epigenética en mamíferos, donde se encuentra restringida principalmente a la citosina del dinucleótido CpG, y está relacionada con el silenciamiento transcripcional. Tiene un papel clave en la regulación epigenética y por tanto, su análisis es de gran interés para entender procesos biológicos tan importantes como la replicación del DNA, la expresión génica, la diferenciación celular o las bases moleculares de muchas enfermedades, incluyendo el cáncer. Por este motivo, se han desarrollado numerosas técnicas para el estudio de la metilación del ADN, tanto a nivel regional como global y genómico. No obstante, el análisis masivo del genoma humano presenta un alto coste y dificultad, por lo que la mayoría de estudios a escala genómica utilizan diseños que reducen la complejidad del análisis. Para ello, se analiza únicamente una pequeña porción de las regiones genómicas de interés, una o unas pocas CpGs, y se extrapolan los resultados. Con la aparición de los primeros datos de células humanas obtenidos mediante Whole Genome Bisulfite Sequencing es posible evaluar la exactitud de los diferentes abordajes de complejidad reducida. En el presente trabajo se ha determinado la concordancia entre la metilación de las dianas HpaII y las islas CpG que las contienen. Para ello, se han utilizado datos públicos de dos estudios WGBS de muestras humanas. La diana HpaII se ha seleccionado dada su presencia en el 94% de las islas CpG del genoma. No en vano, la diana de restricción de HpaII (CCGG) es la más utilizada en estudios de metilación del ADN. Nuestros análisis muestran que la metilación de aquellas dianas HpaII que están dentro de islas CpG, resultan excelentes indicadores de la metilación global de las islas. Las dianas HpaII tienen un alto valor predictivo a nivel cualitativo asignando correctamente a la isla CpG como metilada o desmetilada con un alto porcentaje de acierto. Además, cuando las islas CpG disponían de dos o más dianas HpaII las predicciones resultaban extremadamente exactas para el valor de metilación de la isla. Estos resultados proporcionan una validación global de las estrategias basadas en el uso de la enzima de restricción sensible a metilación HpaII. Esta validación puede extenderse a otras aproximaciones de reducción de la complejidad similares. Su principal ventaja radica en la drástica reducción de costes, tanto del trabajo experimental como del análisis computacional. A pesar de la alta homogeneidad existente en la metilación de las islas CpG existen otros escenarios donde no es raro observar diferencias de metilación importantes entre dos CpGs próximas. La disponibilidad de un creciente número de metilomas ha permitido identificar un conjunto de zonas con patrones de metilación diferenciales, DMRs. Estas regiones se han encontrado asociadas a cambios observados en la expresión génica entre estos tipos celulares. En el presente trabajo se han analizado posiciones individuales con una metilación discordante respecto a su entorno. Se han evaluado aquellas que se mantienen desmetiladas cuando su entorno está totalmente metilado. Dada la metilación general del genoma, estos patrones representan anomalías que difícilmente puedan ser azarosas. Así, se han encontrado secuencias anómalas que presentan características asociadas a zonas de alta actividad transcripcional y pueden representar puntos de regulación génica. Además, algunas de ellas se sitúan cerca de genes importantes en la regulación de enfermedades, como el cáncer. Aunque aún son necesarias validaciones experimentales, aproximaciones in silico como la aquí presentada permiten encontrar posiciones de discordancia epigenética que pueden representar dominios funcionales putativos.[eng] DNA methylation consists in the enzymatic transference of a methyl group to the 5’ position of a cytosine. In humans, it mostly occurs in the CpG dinucleotide. It is related to transcriptional silencing and it has a key role in biological processes such as DNA replication, genetic expression or the molecular basis of a lot of diseases, including cancer. For these reasons, many techniques have been developed to analyze it. However, the whole genomic determination of this epigenetic mark results in difficult and expensive analysis. To circumvent this issue, many techniques use a few positions and extrapolate the results. However, no global validation of this hypothesis has been performed. We have determined the correlation between the methylation of HpaII restriction sites and the CpG islands that contain them. We have used Whole Genome Bisulfite Sequencing data from human samples. Our results show that the methylation of the HpaII sites within CpG islands are excellent reporters of the global CpG island methylation, at both the quantitative and qualitative level. The prediction values were improved when the island contained more than one HpaII site. Our results represent a global validation for methylation analysis techniques based on the use of HpaII restriction enzyme. Although the high observed homogeneity in DNA methylation, there are some scenarios where it is not uncommon to observe important methylation differences between two close CpG positions. Many of these regions have been found to be associated to differences in gene expression among different cell types. We have analyzed individual positions with a discordant methylation compared to its closest neighbor CpGs. Using them as indicators, we have found sequences that have characteristics associated to high transcriptional activity and they can represent genetic regulation points. Some of them are close to important disease-associated genes. These positions can represent putative functional domains

    Statistical Quantification of Methylation Levels by Next-Generation Sequencing

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    BACKGROUND/AIMS: Recently, next-generation sequencing-based technologies have enabled DNA methylation profiling at high resolution and low cost. Methyl-Seq and Reduced Representation Bisulfite Sequencing (RRBS) are two such technologies that interrogate methylation levels at CpG sites throughout the entire human genome. With rapid reduction of sequencing costs, these technologies will enable epigenotyping of large cohorts for phenotypic association studies. Existing quantification methods for sequencing-based methylation profiling are simplistic and do not deal with the noise due to the random sampling nature of sequencing and various experimental artifacts. Therefore, there is a need to investigate the statistical issues related to the quantification of methylation levels for these emerging technologies, with the goal of developing an accurate quantification method. METHODS: In this paper, we propose two methods for Methyl-Seq quantification. The first method, the Maximum Likelihood estimate, is both conceptually intuitive and computationally simple. However, this estimate is biased at extreme methylation levels and does not provide variance estimation. The second method, based on bayesian hierarchical model, allows variance estimation of methylation levels, and provides a flexible framework to adjust technical bias in the sequencing process. RESULTS: We compare the previously proposed binary method, the Maximum Likelihood (ML) method, and the bayesian method. In both simulation and real data analysis of Methyl-Seq data, the bayesian method offers the most accurate quantification. The ML method is slightly less accurate than the bayesian method. But both our proposed methods outperform the original binary method in Methyl-Seq. In addition, we applied these quantification methods to simulation data and show that, with sequencing depth above 40-300 (which varies with different tissue samples) per cleavage site, Methyl-Seq offers a comparable quantification consistency as microarrays

    MetMap Enables Genome-Scale Methyltyping for Determining Methylation States in Populations

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    The ability to assay genome-scale methylation patterns using high-throughput sequencing makes it possible to carry out association studies to determine the relationship between epigenetic variation and phenotype. While bisulfite sequencing can determine a methylome at high resolution, cost inhibits its use in comparative and population studies. MethylSeq, based on sequencing of fragment ends produced by a methylation-sensitive restriction enzyme, is a method for methyltyping (survey of methylation states) and is a site-specific and cost-effective alternative to whole-genome bisulfite sequencing. Despite its advantages, the use of MethylSeq has been restricted by biases in MethylSeq data that complicate the determination of methyltypes. Here we introduce a statistical method, MetMap, that produces corrected site-specific methylation states from MethylSeq experiments and annotates unmethylated islands across the genome. MetMap integrates genome sequence information with experimental data, in a statistically sound and cohesive Bayesian Network. It infers the extent of methylation at individual CGs and across regions, and serves as a framework for comparative methylation analysis within and among species. We validated MetMap's inferences with direct bisulfite sequencing, showing that the methylation status of sites and islands is accurately inferred. We used MetMap to analyze MethylSeq data from four human neutrophil samples, identifying novel, highly unmethylated islands that are invisible to sequence-based annotation strategies. The combination of MethylSeq and MetMap is a powerful and cost-effective tool for determining genome-scale methyltypes suitable for comparative and association studies
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