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Sun-Chong Wang

Publications and source records attributed to Sun-Chong Wang.

2 recordsLinked to original sources

Intra- and interindividual epigenetic variation in human germ cells.

Epigenetics represents a secondary inheritance system that has been poorly investigated in human biology. The objective of this study was to perform a comprehensive analysis of DNA methylation variation between and within the germlines of normal males. First, methylated cytosines were mapped using bisulphite modification-based sequencing in the promoter regions of the following disease genes: presenilins (PSEN1 and PSEN2), breast cancer (BRCA1 and BRCA2), myotonic dystrophy (DM1), and Huntington disease (HD). Major epigenetic variation was detected within samples, since the majority of sperm cells of the same individual exhibited unique DNA methylation profiles. In the interindividual analysis, 41 of 61 pairwise comparisons revealed distinct DNA methylation profiles (P=.036 to 6.8 x 10(-14)). Second, a microarray-based epigenetic profiling of the same sperm samples was performed using a 12,198-feature CpG island microarray. The microarray analysis has identified numerous DNA methylation-variable positions in the germ cell genome. The largest degree of variation was detected within the promoter CpG islands and pericentromeric satellites among the single-copy DNA fragments and repetitive elements, respectively. A number of genes, such as EED, CTNNA2, CALM1, CDH13, and STMN2, exhibited age-related DNA methylation changes. Finally, allele-specific methylation patterns in CDH13 were detected. This study provides evidence for significant epigenetic variability in human germ cells, which warrants further research to determine whether such epigenetic patterns can be efficiently transmitted across generations and what impact inherited epigenetic individuality may have on phenotypic outcomes in health and disease.

Confounding Factors, Epidemiologic↗

Reconstructing genetic networks from time ordered gene expression data using Bayesian method with global search algorithm.

Different genes of an organism are expressed to different levels at different times during the life cycle and in response to various environmental stresses. Elucidating the network of gene-gene interactions responsible for the expression helps understand living processes. Microarray technology allows concurrent genomic scale measurement of an organism's mRNA levels. We describe a power-law formalism to model the combinatorial effect of regulators on gene transcription. The dynamic model allows delayed transcription. We employ a principled network reconstruction approach that accounts for the high noise and low replicate characteristics of present day microarray data. An important feature of our approach is that the detail of the reconstructed network is limited to the noise level of the data. We apply the methodology to a microarray dataset of yeast cells grown in glucose and experiencing a diauxic transition upon glucose depletion. The reconstructed transcriptional regulations of yeast glycolytic genes are consistent with published findings.

Algorithms↗