Limits...
Genome-wide quantitative assessment of variation in DNA methylation patterns.

Xie H, Wang M, de Andrade A, Bonaldo Mde F, Galat V, Arndt K, Rajaram V, Goldman S, Tomita T, Soares MB - Nucleic Acids Res. (2011)

Bottom Line: However, little is known about genome-wide variation of DNA methylation patterns.We further identified 12 putative allelic-specific methylated genomic loci, including four Alu elements and eight promoters.Lastly, using subcloned normal fibroblast cells, we demonstrated the highly variable methylation patterns are resulted from low fidelity of DNA methylation inheritance.

View Article: PubMed Central - PubMed

Affiliation: Falk Brain Tumor Center, Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago IL 60614-3394, USA. hxie@childrensmemorial.org

ABSTRACT
Genomic DNA methylation contributes substantively to transcriptional regulations that underlie mammalian development and cellular differentiation. Much effort has been made to decipher the molecular mechanisms governing the establishment and maintenance of DNA methylation patterns. However, little is known about genome-wide variation of DNA methylation patterns. In this study, we introduced the concept of methylation entropy, a measure of the randomness of DNA methylation patterns in a cell population, and exploited it to assess the variability in DNA methylation patterns of Alu repeats and promoters. A few interesting observations were made: (i) within a cell population, methylation entropy varies among genomic loci; (ii) among cell populations, the methylation entropies of most genomic loci remain constant; (iii) compared to normal tissue controls, some tumors exhibit greater methylation entropies; (iv) Alu elements with high methylation entropy are associated with high GC content but depletion of CpG dinucleotides and (v) Alu elements in the intronic regions or far from CpG islands are associated with low methylation entropy. We further identified 12 putative allelic-specific methylated genomic loci, including four Alu elements and eight promoters. Lastly, using subcloned normal fibroblast cells, we demonstrated the highly variable methylation patterns are resulted from low fidelity of DNA methylation inheritance.

Show MeSH

Related in: MedlinePlus

The distribution of methylation entropy for simulation results. For a genomic locus with four CpG dinucleotides and average methylation level as 50%, 10 000 methylation data sets were generated. Each data set comprised of 16 sequence reads with four CpG sites per read. The dashed curve represents simulation result for stochastic methylation event. For 10 000 data sets, the methylation entropy ranged from 0.54 to 0.97 with average as 0.80. The solid curve represents simulation result for allelic-specific methylation as an example of deterministic methylation event. For 10 000 data sets, the methylation entropy ranged from 0.24 to 0.52 with average as 0.35.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3105398&req=5

Figure 2: The distribution of methylation entropy for simulation results. For a genomic locus with four CpG dinucleotides and average methylation level as 50%, 10 000 methylation data sets were generated. Each data set comprised of 16 sequence reads with four CpG sites per read. The dashed curve represents simulation result for stochastic methylation event. For 10 000 data sets, the methylation entropy ranged from 0.54 to 0.97 with average as 0.80. The solid curve represents simulation result for allelic-specific methylation as an example of deterministic methylation event. For 10 000 data sets, the methylation entropy ranged from 0.24 to 0.52 with average as 0.35.

Mentions: We exploited simulations to provide statistical assessment for methylation entropy, thus enabling determination of statistical significance for a stochastic methylation variation observed in a locus. Similarly to the methylation entropy determinations made based on the actual sequence data, those utilizing simulated data take into consideration the average methylation level, the number of CpG dinucleotides, and the sequence reads generated. For example, to determine whether or not the methylation patterns shown in Figure 1D were stochastic, we randomly generated 10 000 data sets by simulation. Each data set exhibited an average methylation level of 50%, and comprised 16 random methylation patterns representing 16 sequences with 4 CpG dinucleotides per read. The distribution of methylation entropies of these 10 000 random data sets indicated that a genomic region associated with stochastic methylation change would have a methylation entropy of approximately 0.80, and a minimum methylation entropy of 0.54 (Figure 2). Based on such distribution, we may conclude that the formation of the methylation patterns depicted in Figure 1D, with a calculated methylation entropy of 0.1875, must not be stochastic (lower than the minimum methylation entropy 0.54 observed in 10 000 simulations; P < 0.0001).Figure 2.


Genome-wide quantitative assessment of variation in DNA methylation patterns.

Xie H, Wang M, de Andrade A, Bonaldo Mde F, Galat V, Arndt K, Rajaram V, Goldman S, Tomita T, Soares MB - Nucleic Acids Res. (2011)

The distribution of methylation entropy for simulation results. For a genomic locus with four CpG dinucleotides and average methylation level as 50%, 10 000 methylation data sets were generated. Each data set comprised of 16 sequence reads with four CpG sites per read. The dashed curve represents simulation result for stochastic methylation event. For 10 000 data sets, the methylation entropy ranged from 0.54 to 0.97 with average as 0.80. The solid curve represents simulation result for allelic-specific methylation as an example of deterministic methylation event. For 10 000 data sets, the methylation entropy ranged from 0.24 to 0.52 with average as 0.35.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3105398&req=5

Figure 2: The distribution of methylation entropy for simulation results. For a genomic locus with four CpG dinucleotides and average methylation level as 50%, 10 000 methylation data sets were generated. Each data set comprised of 16 sequence reads with four CpG sites per read. The dashed curve represents simulation result for stochastic methylation event. For 10 000 data sets, the methylation entropy ranged from 0.54 to 0.97 with average as 0.80. The solid curve represents simulation result for allelic-specific methylation as an example of deterministic methylation event. For 10 000 data sets, the methylation entropy ranged from 0.24 to 0.52 with average as 0.35.
Mentions: We exploited simulations to provide statistical assessment for methylation entropy, thus enabling determination of statistical significance for a stochastic methylation variation observed in a locus. Similarly to the methylation entropy determinations made based on the actual sequence data, those utilizing simulated data take into consideration the average methylation level, the number of CpG dinucleotides, and the sequence reads generated. For example, to determine whether or not the methylation patterns shown in Figure 1D were stochastic, we randomly generated 10 000 data sets by simulation. Each data set exhibited an average methylation level of 50%, and comprised 16 random methylation patterns representing 16 sequences with 4 CpG dinucleotides per read. The distribution of methylation entropies of these 10 000 random data sets indicated that a genomic region associated with stochastic methylation change would have a methylation entropy of approximately 0.80, and a minimum methylation entropy of 0.54 (Figure 2). Based on such distribution, we may conclude that the formation of the methylation patterns depicted in Figure 1D, with a calculated methylation entropy of 0.1875, must not be stochastic (lower than the minimum methylation entropy 0.54 observed in 10 000 simulations; P < 0.0001).Figure 2.

Bottom Line: However, little is known about genome-wide variation of DNA methylation patterns.We further identified 12 putative allelic-specific methylated genomic loci, including four Alu elements and eight promoters.Lastly, using subcloned normal fibroblast cells, we demonstrated the highly variable methylation patterns are resulted from low fidelity of DNA methylation inheritance.

View Article: PubMed Central - PubMed

Affiliation: Falk Brain Tumor Center, Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago IL 60614-3394, USA. hxie@childrensmemorial.org

ABSTRACT
Genomic DNA methylation contributes substantively to transcriptional regulations that underlie mammalian development and cellular differentiation. Much effort has been made to decipher the molecular mechanisms governing the establishment and maintenance of DNA methylation patterns. However, little is known about genome-wide variation of DNA methylation patterns. In this study, we introduced the concept of methylation entropy, a measure of the randomness of DNA methylation patterns in a cell population, and exploited it to assess the variability in DNA methylation patterns of Alu repeats and promoters. A few interesting observations were made: (i) within a cell population, methylation entropy varies among genomic loci; (ii) among cell populations, the methylation entropies of most genomic loci remain constant; (iii) compared to normal tissue controls, some tumors exhibit greater methylation entropies; (iv) Alu elements with high methylation entropy are associated with high GC content but depletion of CpG dinucleotides and (v) Alu elements in the intronic regions or far from CpG islands are associated with low methylation entropy. We further identified 12 putative allelic-specific methylated genomic loci, including four Alu elements and eight promoters. Lastly, using subcloned normal fibroblast cells, we demonstrated the highly variable methylation patterns are resulted from low fidelity of DNA methylation inheritance.

Show MeSH
Related in: MedlinePlus