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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.

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The formula of methylation entropy and the examples for genomic loci with various methylation entropies in a cell population. (A) The formula of methylation entropy. The determination of methylation entropy requires three parameters: the number of CpG sites, the total number of sequence reads generated and the occurrence of each methylation pattern. (B–E) Genomic loci with various methylation entropies.
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Figure 1: The formula of methylation entropy and the examples for genomic loci with various methylation entropies in a cell population. (A) The formula of methylation entropy. The determination of methylation entropy requires three parameters: the number of CpG sites, the total number of sequence reads generated and the occurrence of each methylation pattern. (B–E) Genomic loci with various methylation entropies.

Mentions: To calculate methylation entropy, the following parameters were introduced to the original entropy formula: (i) number of CpG sites in a given genomic locus; (ii) number of sequence reads generated for a genomic locus and (iii) frequency of each distinct DNA methylation pattern observed in a genomic locus, calculated based upon the sequence reads that were generated for the locus (Figure 1A). The probability of a given event in Shannon entropy equation was replaced with the frequency of a distinct methylation pattern observed for a genomic locus. The number of sequence reads generated was used to determine the frequency of a given methylation pattern. The number of CpG dinucleotides was used to normalize the increasing number of possible patterns resulting from the presence of additional CpG sites. The methylation entropy is minimal when DNA molecules in all cells share the same methylation pattern (Figure 1B and C), and is maximal when all possible DNA methylation patterns are equally represented in a population of cells (Figure 1E). Accordingly, genomic loci with the same methylation entropy might have different methylation levels on average (Figure 1B and C). In turn, genomic loci with different methylation entropies may share the same average level of methylation (Figure 1D and E). Since methylation entropy reflects the randomness in the distribution of DNA methylation patterns, it may serve as an indicator for stochastic methylation changes. Thus, methylation entropy analysis differs significantly from conventional methylation level-based analyses in that it enables assessment of methylation pattern stability and diversity.Figure 1.


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 formula of methylation entropy and the examples for genomic loci with various methylation entropies in a cell population. (A) The formula of methylation entropy. The determination of methylation entropy requires three parameters: the number of CpG sites, the total number of sequence reads generated and the occurrence of each methylation pattern. (B–E) Genomic loci with various methylation entropies.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 1: The formula of methylation entropy and the examples for genomic loci with various methylation entropies in a cell population. (A) The formula of methylation entropy. The determination of methylation entropy requires three parameters: the number of CpG sites, the total number of sequence reads generated and the occurrence of each methylation pattern. (B–E) Genomic loci with various methylation entropies.
Mentions: To calculate methylation entropy, the following parameters were introduced to the original entropy formula: (i) number of CpG sites in a given genomic locus; (ii) number of sequence reads generated for a genomic locus and (iii) frequency of each distinct DNA methylation pattern observed in a genomic locus, calculated based upon the sequence reads that were generated for the locus (Figure 1A). The probability of a given event in Shannon entropy equation was replaced with the frequency of a distinct methylation pattern observed for a genomic locus. The number of sequence reads generated was used to determine the frequency of a given methylation pattern. The number of CpG dinucleotides was used to normalize the increasing number of possible patterns resulting from the presence of additional CpG sites. The methylation entropy is minimal when DNA molecules in all cells share the same methylation pattern (Figure 1B and C), and is maximal when all possible DNA methylation patterns are equally represented in a population of cells (Figure 1E). Accordingly, genomic loci with the same methylation entropy might have different methylation levels on average (Figure 1B and C). In turn, genomic loci with different methylation entropies may share the same average level of methylation (Figure 1D and E). Since methylation entropy reflects the randomness in the distribution of DNA methylation patterns, it may serve as an indicator for stochastic methylation changes. Thus, methylation entropy analysis differs significantly from conventional methylation level-based analyses in that it enables assessment of methylation pattern stability and diversity.Figure 1.

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