Semaphorin 4D promotes bone invasion in head and neck squamous cell carcinoma

Head and neck squamous cell carcinomas (HNSCCs) frequently invade the bones of the facial skeleton. Semaphorin 4D (Sema4D) is an axon guidance molecule produced by oligodendrocytes. Sema4D was also identified in the bone microenvironment and many cancer tissues including HNSCC. To date, however, the role of Sema4D in cancer-associated bone disease is still unknown. This is the first study to demonstrate the role of Sema4D in bone invasion of cancer. In the clinical tissue samples of bone lesion of HNSCC, Sema4D was detected at high levels, and its expression was correlated with insulin-like growth factor-I (IGF-I) expression. In vitro experiments showed that IGF-I regulates Sema4D expression and Sema4D increased proliferation, migration and invasion in HNSCC cells. Sema4D also regulated the expression of receptor activator of nuclear factor κβ ligand (RANKL) in osteoblasts, and this stimulated osteoclastgenesis. Furthermore, knockdown of Sema4D in HNSCC cells inhibited tumor growth and decreased the number of osteoclasts in a mouse xenograft model. Taken together, IGF-I-driven production of Sema4D in HNSCCs promotes osteoclastogenesis and bone invasion

Loss of Cardioprotective Effects at the ADAMTS7 Locus as a Result of Gene-Smoking Interactions

BACKGROUND: Common diseases such as coronary heart disease (CHD) are complex in etiology. The interaction of genetic susceptibility with lifestyle factors may play a prominent role. However, gene-lifestyle interactions for CHD have been difficult to identify. Here, we investigate interaction of smoking behavior, a potent lifestyle factor, with genotypes that have been shown to associate with CHD risk. METHODS: We analyzed data on 60 919 CHD cases and 80 243 controls from 29 studies for gene-smoking interactions for genetic variants at 45 loci previously reported to be associated with CHD risk. We also studied 5 loci associated with smoking behavior. Study-specific gene-smoking interaction effects were calculated and pooled using fixed-effects meta-analyses. Interaction analyses were declared to be significant at a P value of <1.0x10(-3) (Bonferroni correction for 50 tests). RESULTS: We identified novel gene-smoking interaction for a variant upstream of the ADAMTS7 gene. Every T allele of rs7178051 was associated with lower CHD risk by 12% in never-smokers (P= 1.3x10(-16)) in comparison with 5% in ever-smokers (P= 2.5x10(-4)), translating to a 60% loss of CHD protection conferred by this allelic variation in people who smoked tobacco (interaction P value= 8.7x10(-5)). The protective T allele at rs7178051 was also associated with reduced ADAMTS7 expression in human aortic endothelial cells and lymphoblastoid cell lines. Exposure of human coronary artery smooth muscle cells to cigarette smoke extract led to induction of ADAMTS7. CONCLUSIONS: Allelic variation at rs7178051 that associates with reduced ADAMTS7 expression confers stronger CHD protection in never-smokers than in ever-smokers. Increased vascular ADAMTS7 expression may contribute to the loss of CHD protection in smokers.Peer reviewe

Causal variant identification performance in simulations.

<p>True positive rate as a function of false positive rate in simulations with all forty replicate datasets combined within each configuration (i.e., each dataset has the same cutoff for calling positives and the number of true and false positives are summed over the datasets).</p

Power of the linear regression and GLM-based test statistics for different co-expression dynamics and sample sizes.

<p>Power of the linear regression and GLM-based test statistics for different co-expression dynamics and sample sizes.</p

BMA 95% central posterior intervals for the number of causal variants in simulations.

<p>Each of the forty replicate datasets within all configurations are shown for BMA A, A/AH and A/D/R. The true value is indicated with a vertical line.</p

Effect type identification accuracy in simulations (weighted with posterior association probability).

<p>Effect type identification accuracy in simulations (weighted with posterior association probability).</p

Estimated correlation coefficients, obtained using the general linear model with different variance-covariance structures, for the five metabolic subsets defined for apolipoprotein B.

<p>A. GLM with metabolic-subset specific correlation coefficients defined by Eqs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150257#pone.0150257.e003" target="_blank">2</a>) and (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150257#pone.0150257.e009" target="_blank">3</a>); B. GLM with common correlation coefficients across the first three metabolic-subsets; C. GLM with no metabolic-subset dependent correlation coefficients, i.e., the null model defined by Eqs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150257#pone.0150257.e003" target="_blank">2</a>) and (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150257#pone.0150257.e011" target="_blank">4</a>).</p

The six simulated co-expression dynamics for a four gene module.

<p>The four genes of the simulated module generate six gene-pair correlations. Each trajectory of dots captures the metabolite-co-expression association for one of the module gene pairs.</p

BMA 95% central posterior intervals for heritability in simulations.

<p>Each of the forty replicate datasets within all configurations are shown for BMA A, A/AH and A/D/R. The true value is indicated with a vertical line.</p

Posterior distributions of heritability for HDL-C and LDL-C.

<p>Median values are 0.08 for all except for LDL-C BMA A/AH, which has a median of 0.09.</p