Targeted next-generation sequencing of dedifferentiated chondrosarcoma in the skull base reveals combined TP53 and PTEN mutations with increased proliferation index, an implication for pathogenesis

Dedifferentiated chondrosarcoma (DDCS) is a rare disease with a dismal prognosis. DDCS consists of two morphologically distinct components: the cartilaginous and noncartilaginous components. Whether the two components originate from the same progenitor cells has been controversial. Recurrent DDCS commonly displays increased proliferation compared with the primary tumor. However, there is no conclusive explanation for this mechanism. In this paper, we present two DDCSs in the sellar region. Patient 1 exclusively exhibited a noncartilaginous component with a TP53 frameshift mutation in the pathological specimens from the first surgery. The tumor recurred after radiation therapy with an exceedingly increased proliferation index. Targeted next-generation sequencing (NGS) revealed the presence of both a TP53 mutation and a PTEN deletion in the cartilaginous and the noncartilaginous components of the recurrent tumor. Fluorescence in situ hybridization and immunostaining confirmed reduced DNA copy number and protein levels of the PTEN gene as a result of the PTEN deletion. Patient 2 exhibited both cartilaginous and noncartilaginous components in the surgical specimens. Targeted NGS of cells from both components showed neither TP53 nor PTEN mutations, making Patient 2 a naïve TP53 and PTEN control for comparison. In conclusion, additional PTEN loss in the background of the TP53 mutation could be the cause of increased proliferation capacity in the recurrent tumor

Survivin overexpression is potentially associated with pituitary adenoma invasiveness

Background and objective Survivin is an inhibitor of apoptosis. Its role in guiding the treatment of neoplasms, making diagnosis and predicting prognosis has been reported. However, there is little information on the implications and uses of survivin in predicting pituitary adenoma (PA) invasiveness. Existing information is unclear and controversial. We thus conducted this meta-analysis to explore whether the surviving expression levels in invasive PAs (IPA) and regular PAs are different or not. We considered both non-secreting and secreting tumors together. Methods: A global search strategy was systematically applied among five databases including Cochrane Library, Embase, PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI) up to June 18th, 2017. With a specially designed form including PAs’ invasive features, etc., data was collected. The included studies should present the data representing the surviving levels in IPA groups and regular PA groups, respectively. Differences were expressed as standard mean differences (SMDs) or odds ratios (ORs) with 95% confidence interval (CI). To estimate the heterogeneities, I2 test, Cochran's Q-test and Galbr figure were all conducted. A sensitivity-analysis and potential-publication bias were also performed. Results: In the present meta-analysis, 9 studies containing 489 patients were included. Seven studies with dichotomous-data showed that survivin over-expression in PA tissue was closely associated with a high invasive tendency (OR 6.226, 95% CI 3.970, 9.765; P<0.001), but 2 continuous-data studies revealed that there was no significant association (SMD −5.043, 95% CI-10.965, 0.878; p=0.095). A sensitivity-analysis suggested a statistically stable result. We did not find publication bias. Conclusion: We suggest that survivin overexpression is potentially associated with PA invasiveness. More research based on medical big data is needed to confirm this finding

Structural and mechanistic insights into the biosynthesis of CDP-archaeol in membranes

The divergence of archaea, bacteria and eukaryotes was a fundamental step in evolution. One marker of this event is a major difference in membrane lipid chemistry between these kingdoms. Whereas the membranes of bacteria and eukaryotes primarily consist of straight fatty acids ester-bonded to glycerol-3-phosphate, archaeal phospholipids consist of isoprenoid chains ether-bonded to glycerol-1-phosphate. Notably, the mechanisms underlying the biosynthesis of these lipids remain elusive. Here, we report the structure of the CDP-archaeol synthase (CarS) of Aeropyrum pernix (ApCarS) in the CTP- and Mg(2+)-bound state at a resolution of 2.4 Å. The enzyme comprises a transmembrane domain with five helices and cytoplasmic loops that together form a large charged cavity providing a binding site for CTP. Identification of the binding location of CTP and Mg(2+) enabled modeling of the specific lipophilic substrate-binding site, which was supported by site-directed mutagenesis, substrate-binding affinity analyses, and enzyme assays. We propose that archaeol binds within two hydrophobic membrane-embedded grooves formed by the flexible transmembrane helix 5 (TM5), together with TM1 and TM4. Collectively, structural comparisons and analyses, combined with functional studies, not only elucidated the mechanism governing the biosynthesis of phospholipids with ether-bonded isoprenoid chains by CTP transferase, but also provided insights into the evolution of this enzyme superfamily from archaea to bacteria and eukaryotes.Cell Research advance online publication 29 September 2017; doi:10.1038/cr.2017.122

A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers

Study of interface residue pairs is important for understanding the interactions between monomers inside a trimer protein&ndash;protein complex. We developed a two-layer support vector machine (SVM) ensemble-classifier that considers physicochemical and geometric properties of amino acids and the influence of surrounding amino acids. Different descriptors and different combinations may give different prediction results. We propose feature combination engineering based on correlation coefficients and F-values. The accuracy of our method is 65.38% in independent test set, indicating biological significance. Our predictions are consistent with the experimental results. It shows the effectiveness and reliability of our method to predict interface residue pairs of protein trimers

Multiple dimensional space for protein interface residue characterization

Proteins interact to perform biological functions through specific interface residues. Correctly understanding the mechanisms of interface recognition and prediction are important for many aspects of life science studies. Here, we report a novel architecture to study protein interface residues. In our method, multiple dimensional space was built on some meaningful features. Then we divided the space and put all the surface residues into the regions according to their features’ values. Interestingly, interface residues were found to prefer some grids clustered together. We obtained excellent result on a public and verified data benchmark. Our approach not only opens up a new train of thought for interface residue prediction, but also will help to understand proteins interaction more deeply

Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions

Intrinsically Disordered Proteins (IDPs) and Regions (IDRs) exist widely. Although without well-defined structures, they participate in many important biological processes. In addition, they are also widely related to human diseases and have become potential targets in drug discovery. However, there is a big gap between the experimental annotations related to IDPs/IDRs and their actual number. In recent decades, the computational methods related to IDPs/IDRs have been developed vigorously, including predicting IDPs/IDRs, the binding modes of IDPs/IDRs, the binding sites of IDPs/IDRs, and the molecular functions of IDPs/IDRs according to different tasks. In view of the correlation between these predictors, we have reviewed these prediction methods uniformly for the first time, summarized their computational methods and predictive performance, and discussed some problems and perspectives

Maximizing multiple influences and fair seed allocation on multilayer social networks.

The dissemination of information on networks involves many important practical issues, such as the spread and containment of rumors in social networks, the spread of infectious diseases among the population, commercial propaganda and promotion, the expansion of political influence and so on. One of the most important problems is the influence-maximization problem which is to find out k most influential nodes under a certain propagate mechanism. Since the problem was proposed in 2001, many works have focused on maximizing the influence in a single network. It is a NP-hard problem and the state-of-art algorithm IMM proposed by Youze Tang et al. achieves a ratio of 63.2% of the optimum with nearly linear time complexity. In recent years, there have been some works of maximizing influence on multilayer networks, either in the situation of single or multiple influences. But most of them study seed selection strategies to maximize their own influence from the perspective of participants. In fact, the problem from the perspective of network owners is also worthy of attention. Since network participants have not had access to all information of the network for reasons such as privacy protection and corporate interests, they may have access to only part of the social network. The owners of networks can get the whole picture of the networks, and they need not only to maximize the overall influence, but also to consider allocating seeds to their customers fairly, i.e., the Fair Seed Allocation (FSA) problem. As far as we know, FSA problem has been studied on a single network, but not on multilayer networks yet. From the perspective of network owners, we propose a multiple-influence diffusion model MMIC on multilayer networks and its FSA problem. Two solutions of FSA problem are given in this paper, and we prove theoretically that our seed allocation schemes are greedy. Subsequent experiments also validate the effectiveness of our approaches