Progress toward understanding the pathogenesis of cystic fibrosis (CF) and developing effective therapies has been hampered by lack of a relevant animal model. CF mice fail to develop the lung and pancreatic disease that cause most of the morbidity and mortality in patients with CF. Pigs may be better animals than mice in which to model human genetic diseases because their anatomy, biochemistry, physiology, size, and genetics are more similar to those of humans. However, to date, gene-targeted mammalian models of human genetic disease have not been reported for any species other than mice. Here we describe the first steps toward the generation of a pig model of CF. We used recombinant adeno-associated virus (rAAV) vectors to deliver genetic constructs targeting the CF transmembrane conductance receptor (CFTR) gene to pig fetal fibroblasts. We generated cells with the CFTR gene either disrupted or containing the most common CF-associated mutation (ΔF508). These cells were used as nuclear donors for somatic cell nuclear transfer to porcine oocytes. We thereby generated heterozygote male piglets with each mutation. These pigs should be of value in producing new models of CF. In addition, because gene-modified mice often fail to replicate human diseases, this approach could be used to generate models of other human genetic diseases in species other than mice.
Christopher S. Rogers, Yanhong Hao, Tatiana Rokhlina, Melissa Samuel, David A. Stoltz, Yuhong Li, Elena Petroff, Daniel W. Vermeer, Amanda C. Kabel, Ziying Yan, Lee Spate, David Wax, Clifton N. Murphy, August Rieke, Kristin Whitworth, Michael L. Linville, Scott W. Korte, John F. Engelhardt, Michael J. Welsh, Randall S. Prather
Usage data is cumulative from October 2019 through October 2020.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.