Connection Between Epigenome, Selective Mutability, Evolution, and Human Disease
Li, Harris et al., PLoS Genetics
Researchers at the Baylor College of Medicine and elsewhere propose a "connection between the epigenome, selective mutability, evolution, and human disease" based on the findings of their study on associations of structural mutability with germline DNA methylation and with non-allelic homologous recombination mediated by low-copy repeats. "Combined evidence from four human sperm methylome maps, human genome evolution, structural polymorphisms in the human population, and previous genomic and disease studies consistently points to a strong association of germline hypomethylation and genomic instability," the Baylor-led team writes.
This Week in the Journal of Molecular Diagnostics
In the Journal of Molecular Diagnostics, researchers led by Jeanne Jordon at George Washington University in DC compare different sample extraction techniques for PCR analysis of pathogens in blood. They used silica spin columns, phenol-chloroform, and an automated magnetic capture of polymer-complexed DNA to purify nucleic acids to prepare samples for TaqMan assays to detect Staphylococcus aureus. The researchers note that samples prepared using silica columns needed to be diluted to reduced the effect of sodium polyanetholsulfonate, which inhibits PCR, while the other methods needed little dilution. Further, phenol-chloroform and the magnetic capture approach could detect S. aureus two hours sooner than the silica approach could. "These findings highlight the importance of considering the mechanism when selecting a DNA extraction methodology," the researchers say.
In addition, Johns Hopkins University's James Eshleman and his colleagues report on their new software program called Pyromarker that simulates pyrosequencing results. "Simulated pyrograms can aid in the analysis of complex pyrosequencing results in which various hypothesized mutations can be tested, and the resultant pyrograms can be matched with the actual program," Eshelman and his colleagues write. They tested their program using KRAS and BRAF mutations, and find that it handles the identification of complex mutations efficiently.