In this week's PNAS Early Edition, researchers led by Pradeep Singh at the University of Washington write that sampling procedures can affect the accuracy of microbiome studies. They compared lung, throat, and sputum samples taken from patients with cystic fibrosis undergoing lung transplants. Using 16S amplicon pyrosequencing, they found that the lung and throat samples from the same patient were discordant for microorganism type and that sputum samples were similar to throat samples. Lung samples, they note, were dominated by a handful of species. "These ﬁndings suggest that oropharyngeal contamination could limit the accuracy of DNA-based measurements on upper-airway specimens," Singh and colleagues write. "This work highlights the importance of sampling procedures for microbiome studies and suggests that methods that account for contamination are needed when DNA-based methods are used on clinical specimens."
Elsewhere in PNAS, a team of researchers from the Indiana University School of Medicine reports that telomere fusions may be an important factor in the genomic instability in breast cancer. Using telomere-associated repeat, or TAR, fusion PCR, the team was able to uncover and analyze telomere dysfunction in breast tumors. "We discovered that telomere fusions are present at similar levels in DCIS and at the later invasive ductal carcinoma stage," the Indiana University group writes. "The presence of telomere end-to-end fusions is a fundamental indication of and a marker for loss of telomere function. This approach has allowed us to begin to elucidate mechanisms responsible for the origin of genomic instability involving defects in telomere maintenance from human tumor tissue."
Researchers led by Pascal Hersen from the Université Paris Diderot and the National University of Singapore report on their development of "a platform for real-time, closed-loop control of gene expression in yeast that integrates microscopy for monitoring gene expression at the cell level, microfluidics to manipulate the cells' environment, and original software for automated imaging, quantification, and model predictive control." They add that they expect "our platform will be used to complement and help the development of synthetic biology through the creation of hybrid systems resulting from the interconnection of in vivo and in silico computing devices."