This is a case example on the concept of personalized precision medicine in a surgical setting and demonstrates how 3D-printed, patient-specific implants may bring individualized solutions to rare problems wherein restoration of the specific anatomy of each patient is a key prognostic factor.

No abstract

The recent finding that reprogrammed human pluripotent stem cells can be derived by nuclear transfer into human oocytes as well as by induced expression of defined factors has revitalized the debate on whether one approach might be advantageous over the other. Here we compare the genetic and epigenetic integrity of human nuclear-transfer embryonic stem cell (NT-ESC) lines and isogenic induced pluripotent stem cell (iPSC) lines, derived from the same somatic cell cultures of fetal, neonatal, and adult origin. The two cell types showed similar genome-wide gene expression and DNA methylation profiles. Importantly, NT-ESCs and iPSCs had comparable numbers of de novo coding mutations, but significantly more than parthenogenetic ESCs. As iPSCs, NT-ESCs displayed clone- and gene-specific aberrations in DNA methylation and allele-specific expression of imprinted genes. The occurrence of these genetic and epigenetic defects in both NT-ESCs and iPSCs suggests that they are inherent to reprogramming, regardless of derivation approach.

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Phosphorylation is an important type of protein post-translational modification. Identification of possible phosphorylation sites of a protein is important for understanding its functions. Unbiased screening for phosphorylation sites by in vitro or in vivo experiments is time consuming and expensive; in silico prediction can provide functional candidates and help narrow down the experimental efforts. Most of the existing prediction algorithms take only the polypeptide sequence around the phosphorylation sites into consideration. However, protein phosphorylation is a very complex biological process in vivo. The polypeptide sequences around the potential sites are not sufficient to determine the phosphorylation status of those residues. In the current work, we integrated various data sources such as protein functional domains, protein subcellular location and protein-protein interactions, along with the polypeptide sequences to predict protein phosphorylation sites. The heterogeneous information significantly boosted the prediction accuracy for some kinase families. To demonstrate potential application of our method, we scanned a set of human proteins and predicted putative phosphorylation sites for Cyclin-dependent kinases, Casein kinase 2, Glycogen synthase kinase 3, Mitogen-activated protein kinases, protein kinase A, and protein kinase C families (avaiable at http://cmbi.bjmu.edu.cn/huphospho). The predicted phosphorylation sites can serve as candidates for further experimental validation. Our strategy may also be applicable for the in silico identification of other post-translational modification substrates.