Smad3 promotes cancer progression by inhibiting E4BP4-mediated NK cell development

published_or_final_versio

Optimizing Functional Network Representation of Multivariate Time Series

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks

In Vivo Functional Genomic Studies of Sterol Carrier Protein-2 Gene in the Yellow Fever Mosquito

A simple and efficient DNA delivery method to introduce extrachromosomal DNA into mosquito embryos would significantly aid functional genomic studies. The conventional method for delivery of DNA into insects is to inject the DNA directly into the embryos. Taking advantage of the unique aspects of mosquito reproductive physiology during vitellogenesis and an in vivo transfection reagent that mediates DNA uptake in cells via endocytosis, we have developed a new method to introduce DNA into mosquito embryos vertically via microinjection of DNA vectors in vitellogenic females without directly manipulating the embryos. Our method was able to introduce inducible gene expression vectors transiently into F0 mosquitoes to perform functional studies in vivo without transgenic lines. The high efficiency of expression knockdown was reproducible with more than 70% of the F0 individuals showed sufficient gene expression suppression (<30% of the controls' levels). At the cohort level, AeSCP-2 expression knockdown in early instar larvae resulted in detectable phenotypes of the expression deficiency such as high mortality, lowered fertility, and distorted sex ratio after induction of AeSCP-2 siRNA expression in vivo. The results further confirmed the important role of AeSCP-2 in the development and reproduction of A. aegypti. In this study, we proved that extrachromosaomal transient expression of an inducible gene from a DNA vector vertically delivered via vitellogenic females can be used to manipulate gene expression in F0 generation. This new method will be a simple and efficient tool for in vivo functional genomic studies in mosquitoes

Sampling constrained probability distributions using Spherical Augmentation

Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA). Bayesian inference involving probability distributions confined to constrained domains could be quite challenging for commonly used sampling algorithms. In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space. By moving freely on the surface of this sphere, sampling algorithms handle constraints implicitly and generate proposals that remain within boundaries when mapped back to the original space. Our proposed method, called {Spherical Augmentation}, provides a mathematically natural and computationally efficient framework for sampling from constrained probability distributions. We show the advantages of our method over state-of-the-art sampling algorithms, such as exact Hamiltonian Monte Carlo, using several examples including truncated Gaussian distributions, Bayesian Lasso, Bayesian bridge regression, reconstruction of quantized stationary Gaussian process, and LDA for topic modeling.Comment: 41 pages, 13 figure

Disparities and risks of sexually transmissible infections among men who have sex with men in China: a meta-analysis and data synthesis.

BACKGROUND: Sexually transmitted infections (STIs), including Hepatitis B and C virus, are emerging public health risks in China, especially among men who have sex with men (MSM). This study aims to assess the magnitude and risks of STIs among Chinese MSM. METHODS: Chinese and English peer-reviewed articles were searched in five electronic databases from January 2000 to February 2013. Pooled prevalence estimates for each STI infection were calculated using meta-analysis. Infection risks of STIs in MSM, HIV-positive MSM and male sex workers (MSW) were obtained. This review followed the PRISMA guidelines and was registered in PROSPERO. RESULTS: Eighty-eight articles (11 in English and 77 in Chinese) investigating 35,203 MSM in 28 provinces were included in this review. The prevalence levels of STIs among MSM were 6.3% (95% CI: 3.5-11.0%) for chlamydia, 1.5% (0.7-2.9%) for genital wart, 1.9% (1.3-2.7%) for gonorrhoea, 8.9% (7.8-10.2%) for hepatitis B (HBV), 1.2% (1.0-1.6%) for hepatitis C (HCV), 66.3% (57.4-74.1%) for human papillomavirus (HPV), 10.6% (6.2-17.6%) for herpes simplex virus (HSV-2) and 4.3% (3.2-5.8%) for Ureaplasma urealyticum. HIV-positive MSM have consistently higher odds of all these infections than the broader MSM population. As a subgroup of MSM, MSW were 2.5 (1.4-4.7), 5.7 (2.7-12.3), and 2.2 (1.4-3.7) times more likely to be infected with chlamydia, gonorrhoea and HCV than the broader MSM population, respectively. CONCLUSION: Prevalence levels of STIs among MSW were significantly higher than the broader MSM population. Co-infection of HIV and STIs were prevalent among Chinese MSM. Integration of HIV and STIs healthcare and surveillance systems is essential in providing effective HIV/STIs preventive measures and treatments. TRIAL REGISTRATION: PROSPERO NO: CRD42013003721

Transgenic CHD1L Expression in Mouse Induces Spontaneous Tumors

Background: Amplification of 1q21 is the most frequent genetic alteration in hepatocellular carcinoma (HCC), which was detected in 58-78% of primary HCC cases by comparative genomic hybridization (CGH). Using chromosome microdissection/ hybrid selection approach we recently isolated a candidate oncogene CHD1L from 1q21 region. Our previous study has demonstrated that CHD1L had strong oncogenic ability, which could be effectively suppressed by siRNA against CHD1L. The molecular mechanism of CHD1L in tumorigenesis has been associated with its role in promoting cell proliferation. Methodology/Principal Findings: To further investigate the in vivo oncogenic role of CHD1L, CHD1L ubiquitous-expression transgenic mouse model was generated. Spontaneous tumor formations were found in 10/41 (24.4%) transgenic mice, including 4 HCCs, but not in their 39 wild-type littermates. In addition, alcohol intoxication was used to induce hepatocyte pathological lesions and results found that overexpression of CHD1L in hepatocytes could promote tumor susceptibility in CHD1L-transgenic mice. To address the mechanism of CHD1L in promoting cell proliferation, DNA content between CHD1Ltransgenic and wildtype mouse embryo fibroblasts (MEFs) was compared by flow cytometry. Flow cytometry results found that CHD1L could facilitate DNA synthesis and G1/ S transition through the up-regulation of Cyclin A, Cyclin D1, Cyclin E, CDK2, and CDK4, and down-regulation of Rb, p27Kip1, and p53. Conclusion/Significance: Taken together, our data strongly support that CHD1L is a novel oncogene and plays an important role in HCC pathogenesis. © 2009 Chen et al.published_or_final_versio

Increased risk of cancer among relatives of patients with lung cancer in China

BACKGROUND: Genetic factors were considered as one of the risk factors for lung cancer or other cancers. The aim of this work was to determine whether a genetic predisposition accounts for such familial aggregation of cancer among relatives of lung cancer probands. METHODS: A case-control study was conducted in 800 case families identified by lung cancer patients (probands), and in 800 control families identified by the probands'spouses. The data were analysed with logistic regression analysis model. RESULTS: The data revealed a significantly greater overall risk of cancer (OR = 1.82, P < 0.01) in the proband group. The relatives of lung cancer probands maintained an increased risk of non-lung cancer (P < 0.05) after adjusting for confounder factors. The crude odds ratio of a proband family having one family member with cancer was 1.67 compared with control families. Proband families were 2.56 times more likely to have two other family members with cancer. For three cancers and four or more cancers, the risk increased to 3.50 and 5.91, respectively. The most striking differences in cancer prevalence between proband and control families were noted for cancer risk among female relatives. The strongest effects were for not only lung cancer in any female relatives (OR 2.17, 95%CI 1.60–3.64) and mothers (OR 2.78, 95%CI 1.23–5.12) and sisters (OR 2.03, 95%CI 1.26–3.97), but also non-lung cancer in females and mothers (OR 2.00, 95%CI 1.26–3.01, and OR 2.34, 95%CI 1.28–4.40, respectively). CONCLUSION: These data support the hypothesis of a genetic susceptibility to cancer in families with lung cancer, and the female genetic susceptibility to cancer might be greater than male

A Graph Algorithmic Approach to Separate Direct from Indirect Neural Interactions

Network graphs have become a popular tool to represent complex systems composed of many interacting subunits; especially in neuroscience, network graphs are increasingly used to represent and analyze functional interactions between neural sources. Interactions are often reconstructed using pairwise bivariate analyses, overlooking their multivariate nature: it is neglected that investigating the effect of one source on a target necessitates to take all other sources as potential nuisance variables into account; also combinations of sources may act jointly on a given target. Bivariate analyses produce networks that may contain spurious interactions, which reduce the interpretability of the network and its graph metrics. A truly multivariate reconstruction, however, is computationally intractable due to combinatorial explosion in the number of potential interactions. Thus, we have to resort to approximative methods to handle the intractability of multivariate interaction reconstruction, and thereby enable the use of networks in neuroscience. Here, we suggest such an approximative approach in the form of an algorithm that extends fast bivariate interaction reconstruction by identifying potentially spurious interactions post-hoc: the algorithm flags potentially spurious edges, which may then be pruned from the network. This produces a statistically conservative network approximation that is guaranteed to contain non-spurious interactions only. We describe the algorithm and present a reference implementation to test its performance. We discuss the algorithm in relation to other approximative multivariate methods and highlight suitable application scenarios. Our approach is a tractable and data-efficient way of reconstructing approximative networks of multivariate interactions. It is preferable if available data are limited or if fully multivariate approaches are computationally infeasible.Comment: 24 pages, 8 figures, published in PLOS On

Pituitary tumor transforming gene-1 haplotypes and risk of pituitary adenoma: a case-control study

<p>Abstract</p> <p>Background</p> <p>It has been suggested that pituitary adenoma results from accumulation of multiple genetic and/or epigenetic aberrations, which may be identified through association studies. As pituitary tumor transforming gene-1 (<it>PTTG1</it>)/securin plays a critical role in promoting genomic instability in pituitary neoplasia, the present study explored the association of <it>PTTG1 </it>haplotypes with the risk of pituitary adenoma.</p> <p>Methods</p> <p>We genotyped five <it>PTTG1 </it>haplotype-tagging SNPs (htSNP) by PCR-RFLP assays in a case-control study, which included 280 Han Chinese patients diagnosed with pituitary adenoma and 280 age-, gender- and geographically matched Han Chinese controls. Haplotypes were reconstructed according to the genotyping data and linkage disequilibrium status of the htSNPs.</p> <p>Results</p> <p>No significant differences in allele and genotype frequencies of the htSNPs were observed between pituitary adenoma patients and controls, indicating that none of the individual <it>PTTG1 </it>SNPs examined in this study is associated with the risk of pituitary adenoma. In addition, no significant association was detected between the reconstructed <it>PTTG1 </it>haplotypes and pituitary adenoma cases or the controls.</p> <p>Conclusions</p> <p>Though no significant association was found between <it>PTTG1 </it>haplotypes and the risk of pituitary adenoma, this is the first report on the association of individual <it>PTTG1 </it>SNPs or <it>PTTG1 </it>haplotypes with the risk of pituitary adenoma based on a solid study; it will provide an important reference for future studies on the association between genetic alterations in <it>PTTG1 </it>and the risk of pituitary adenoma or other tumors.</p