ALBERT

Description: A model for 3:2 high-frequency quasi-periodic oscillations (HFQPOs) with 3:2 pairs observed in four black hole X-ray binaries (BHXBs) is proposed by invoking the epicyclic resonances with the magnetic connection (MC) between a spinning black hole (BH) with a relativistic accretion disc. It turns out that the MC can be worked out due to Poynting–Robertson cosmic battery, and the 3:2 HFQPO pairs associated with the steep power-law states can be fitted in this model. Furthermore, the severe damping problem in the epicyclic resonance model can be overcome by transferring energy from the BH to the inner disc via the MC process for emitting X-rays with sufficient amplitude and coherence to produce the HFQPOs. In addition, we discuss the important role of the magnetic field in state transition of BHXBs.

Description: Motivation: Ovarian cancer is the fifth leading cause of cancer deaths in women in the western world for 2013. In ovarian cancer, benign tumors turn malignant, but the point of transition is difficult to predict and diagnose. The 5-year survival rate of all types of ovarian cancer is 44%, but this can be improved to 92% if the cancer is found and treated before it spreads beyond the ovary. However, only 15% of all ovarian cancers are found at this early stage. Therefore, the ability to automatically identify and diagnose ovarian cancer precisely and efficiently as the tissue changes from benign to invasive is important for clinical treatment and for increasing the cure rate. This study proposes a new ovarian carcinoma classification model using two algorithms: a novel discretization of food sources for an artificial bee colony (DfABC), and a support vector machine (SVM). For the first time in the literature, oncogene detection using this method is also investigated. Results: A novel bio-inspired computing model and hybrid algorithms combining DfABC and SVM was applied to ovarian carcinoma and oncogene classification. This study used the human ovarian cDNA expression database to collect 41 patient samples and 9600 genes in each pathological stage. Feature selection methods were used to detect and extract 15 notable oncogenes. We then used the DfABC-SVM model to examine these 15 oncogenes, dividing them into eight different classifications according to their gene expressions of various pathological stages. The average accuracyof the eight classification experiments was 94.76%. This research also found some oncogenes that had not been discovered or indicated in previous scientific studies. The main contribution of this research is the proof that these newly discovered oncogenes are highly related to ovarian or other cancers. Availability and implementation: http://mht.mis.nchu.edu.tw/moodle/course/view.php?id=7 Contact: mychen@nutc.edu.tw

Description: Familial exudative vitreoretinopathy (FEVR) belongs to a group of genetically and clinically heterogeneous disorders in retinal vascular development. To date, in approximately 50% of patients with FEVR, pathogenic mutations have been detected in FZD4 , LRP5 , TSPAN12 , NDP and ZNF408 . In this study, we identified two heterozygous frameshift mutations in RCBTB1 from three Taiwanese cases through exome sequencing. In patient-derived lymphoblastoid cell lines (LCLs), the protein level of RCBTB1 is approximately half that of unaffected control LCLs, which is indicative of a haploinsufficiency mechanism. By employing transient transfection and reporter assays for the transcriptional activity of β-catenin, we demonstrated that RCBTB1 participates in the Norrin/FZD4 signaling pathway and that knockdown of RCBTB1 by shRNA significantly reduced nuclear accumulation of β-catenin under Norrin and Wnt3a treatments. Furthermore, transgenic fli1 :EGFP zebrafish with rcbtb1 knockdown exhibited anomalies in intersegmental and intraocular vessels. These results strongly support that reduced RCBTB1 expression may lead to defects in angiogenesis through the Norrin-dependent Wnt pathway, and that RCBTB1 is a putative genetic cause of vitreoretinopathies.

Description: Influenza A virus (IAV) has been raising public health and safety concerns worldwide. Cyanovirin-N (CVN) is a prominent anti-IAV candidate, but both cytotoxicity and immunogenicity have hindered the development of this protein as a viable therapy. In this article, linker-CVN (LCVN) with a flexible and hydrophilic polypeptide at the N-terminus was efficiently produced from the cytoplasm of Escherichia coli at a 〉15-l scale. PEGylation at the N-terminal α-amine of LCVN was also reformed as 20 kDa PEGylated linkered Cyanovirin-N (PEG 20k –LCVN). The 50% effective concentrations of PEG 20k –LCVN were 0.43 ± 0.11 µM for influenza A/HK/8/68 (H3N2) and 0.04 ± 0.02 µM for A/Swan/Hokkaido/51/96 (H5N3), dramatically lower than that of the positive control, Ribavirin (2.88 ± 0.66 x 10 3 µM and 1.79 ± 0.62 x 10 3 µM, respectively). A total of 12.5 µM PEG 20k –LCVN effectively inactivate the propagation of H3N2 in chicken embryos. About 2.0 mg/kg/day PEG 20k –LCVN increased double the survival rate (66.67%, P = 0.0378) of H3N2 infected mice, prolonged the median survival period, downregulated the mRNA level of viral nuclear protein and decreased (attenuated) the pathology lesion in mice lung. A novel PEGylated CVN derivative, PEG 20k –LCVN, exhibited potent and strain-dependent anti-IAV activity in nanomolar concentrations in vitro, as well as in micromolar concentration in vivo .

Description: Overexpression of Oct4, a stemness gene encoding a transcription factor, has been reported in several cancers. However, the mechanism by which Oct4 directs transcriptional program that leads to somatic cancer progression remains unclear. In this study, we provide mechanistic insight into Oct4-driven transcriptional network promoting drug-resistance and metastasis in lung cancer cell, animal and clinical studies. Through an integrative approach combining our Oct4 chromatin-immunoprecipitation sequencing and ENCODE datasets, we identified the genome-wide binding regions of Oct4 in lung cancer at promoter and enhancer of numerous genes involved in critical pathways which promote tumorigenesis. Notably, PTEN and TNC were previously undefined targets of Oct4. In addition, novel Oct4-binding motifs were found to overlap with DNA elements for Sp1 transcription factor. We provided evidence that Oct4 suppressed PTEN in an Sp1-dependent manner by recruitment of HDAC1/2, leading to activation of AKT signaling and drug-resistance. In contrast, Oct4 transactivated TNC independent of Sp1 and resulted in cancer metastasis. Clinically, lung cancer patients with Oct4 high, PTEN low and TNC high expression profile significantly correlated with poor disease-free survival. Our study reveals a critical Oct4-driven transcriptional program that promotes lung cancer progression, illustrating the therapeutic potential of targeting Oc4 transcriptionally regulated genes.

Description: In light of limited research into the relationship between the macroscale distribution and dynamic changes of microplankton in the shallow Bohai Sea, here we used flow cytometry to analyse samples collected from the Bohai Sea channel in winter and summer. Results showed that the average of both viral abundance (VA) and bacterial abundance (BA) were lower in winter (3.61  x  10 7 and 1.84  x  10 6 cells/mL, respectively) than in summer (7.47  x  10 7 and 5.05  x  10 6 cells/mL, respectively). At all 16 stations, VA was one order of magnitude greater than BA, with a positive relationship between one another. In the horizontal distribution, variations in VA and BA followed a similar trend, and both were obviously higher near-shore than offshore. In the vertical distribution, variations in both VA and BA did not show a clear relationship with water depth. VA and BA in summer were 2.1 and 2.7 times those in winter, respectively. Spearman correlation analysis showed that both VA and BA were correlated with the concentration of PO 4 -P in winter (positive) and NO 3 -N in summer (negative). Additionally, BA showed a negative correlation with salinity. It is clear that the macroscale distribution of these two kinds of microbes in the Bohai Sea is related to seasonal variation and nutrient availability.

Description: Several genome-wide association studies (GWAS) have demonstrated the association between genetic variants in the major histocompatibility complex (MHC) region and chronic hepatitis B (CHB) virus infection, but it is still unknown about the disease-causing loci and potential mechanisms owing to the complicated linkage disequilibrium for this region. To systematically characterize the MHC variations in relation to the CHB infection, we fine mapped the MHC region on our existing GWAS data with SNP2HLA taken the Pan-Asian panel as reference and finally identified four independent associations. The HLA-DPβ1 amino acid positions 84–87, which drove the effect of reported single nucleotide polymorphisms rs9277535 and rs3077, showed the most significant association (OR = 0.65, P = 2.03 x 10 –8 ). The Leu-15 of HLA-C, conferring the effect of rs3130542, increased the risk of CHB infection independently (OR = 1.61, P = 3.42 x 10 –7 ). The HLA-DRβ1*13 , in perfect LD with glutamic at site 71, and rs400488, an expression quantitative trait locus for HLA-J , were newly identified to be associated with CHB infection independently (OR = 1.84, P = 3.84 x 10 –9 ; OR = 0.28, P = 6.27 x 10 –7 , respectively). HLA-DPβ1 positions 84–87 and HLA-DRβ1 position 71 implicated the P1 and P4 in the antigen-binding groove, whereas HLA-C position 15 affected the signal peptide. These four independent loci together can explain ~6% of the phenotypic variance for CHB infection, accounting for 72.94% of that explained by known genetic variations. We fine mapped the MHC region and identified four loci that independently drove the chronic HBV infection. The results provided a deeper understanding of the GWAS signals and identified additional susceptibility loci which were missed in previous association studies.

Description: Williams syndrome (WS) is a neurodevelopmental disorder caused by a genomic deletion of ~28 genes that results in a cognitive and behavioral profile marked by overall intellectual impairment with relative strength in expressive language and hypersocial behavior. Advancements in protocols for neuron differentiation from induced pluripotent stem cells allowed us to elucidate the molecular circuitry underpinning the ontogeny of WS. In patient-derived stem cells and neurons, we determined the expression profile of the Williams–Beuren syndrome critical region-deleted genes and the genome-wide transcriptional consequences of the hemizygous genomic microdeletion at chromosome 7q11.23. Derived neurons displayed disease-relevant hallmarks and indicated novel aberrant pathways in WS neurons including over-activated Wnt signaling accompanying an incomplete neurogenic commitment. We show that haploinsufficiency of the ATP-dependent chromatin remodeler, BAZ1B , which is deleted in WS, significantly contributes to this differentiation defect. Chromatin-immunoprecipitation (ChIP-seq) revealed BAZ1B target gene functions are enriched for neurogenesis, neuron differentiation and disease-relevant phenotypes. BAZ1B haploinsufficiency caused widespread gene expression changes in neural progenitor cells, and together with BAZ1B ChIP-seq target genes, explained 42% of the transcriptional dysregulation in WS neurons. BAZ1B contributes to regulating the balance between neural precursor self-renewal and differentiation and the differentiation defect caused by BAZ1B haploinsufficiency can be rescued by mitigating over-active Wnt signaling in neural stem cells. Altogether, these results reveal a pivotal role for BAZ1B in neurodevelopment and implicate its haploinsufficiency as a likely contributor to the neurological phenotypes in WS.

Description: Motivation: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein–protein interaction (PPI) is promising to improve the specificity with fewer adverse side-effects. Also it greatly broadens the drug target search space, which makes the drug target discovery difficult. Computational methods are highly desired to efficiently provide candidates for further experiments and hold the promise to greatly accelerate the discovery of novel drug targets. Results: Here, we propose a machine learning method to predict PPI targets in a genomic-wide scale. Specifically, we develop a computational method, named as PrePPItar, to Predict PPIs as drug targets by uncovering the potential associations between drugs and PPIs. First, we survey the databases and manually construct a gold-standard positive dataset for drug and PPI interactions. This effort leads to a dataset with 227 associations among 63 PPIs and 113 FDA-approved drugs and allows us to build models to learn the association rules from the data. Second, we characterize drugs by profiling in chemical structure, drug ATC-code annotation, and side-effect space and represent PPI similarity by a symmetrical S-kernel based on protein amino acid sequence. Then the drugs and PPIs are correlated by Kronecker product kernel. Finally, a support vector machine (SVM), is trained to predict novel associations between drugs and PPIs. We validate our PrePPItar method on the well-established gold-standard dataset by cross-validation. We find that all chemical structure, drug ATC-code, and side-effect information are predictive for PPI target. Moreover, we can increase the PPI target prediction coverage by integrating multiple data sources. Follow-up database search and pathway analysis indicate that our new predictions are worthy of future experimental validation. Conclusion: In conclusion, PrePPItar can serve as a useful tool for PPI target discovery and provides a general heterogeneous data integrative framework. Availability and implementation: PrePPItar is available at http://doc.aporc.org/wiki/PrePPItar . Contact: ycwang@nwipb.cas.cn or ywang@amss.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.