E6 Protein Expressed by High-Risk HPV Activates Super-Enhancers of the EGFR and c-MET Oncogenes by Destabilizing the Histone

The high-risk (HR) human papillomaviruses (HPV) are causative agents of anogenital tract dysplasia and cancers and a fraction of head and neck cancers. The HR HPV E6 oncoprotein possesses canonical oncogenic functions, such as p53 degradation and telomerase activation. It is also capable of stimulating expression of several oncogenes, but the molecular mechanism underlying these events is poorly understood. Here, we provide evidence that HPV16 E6 physically interacts with histone H3K4 demethylase KDM5C, resulting in its degradation in an E3 ligase E6AP- and proteasome-dependent manner. Moreover, we found that HPV16-positive cancer cell lines exhibited lower KDM5C protein levels than HPV-negative cancer cell lines. Restoration of KDM5C significantly suppressed the tumorigenicity of CaSki cells, an HPV16-positive cervical cancer cell line. Whole genome ChIP-seq and RNA-seq results revealed that CaSki cells contained super-enhancers in the proto-oncogenes EGFR and c-MET. Ectopic KDM5C dampened these super-enhancers and reduced the expression of proto-oncogenes. This effect was likely mediated by modulating H3K4me3/H3K4me1 dynamics and decreasing bidirectional enhancer RNA transcription. Depletion of KDM5C or HPV16 E6 expression activated these two super-enhancers. These results illuminate a pivotal relationship between the oncogenic E6 proteins expressed by HR HPV isotypes and epigenetic activation of super-enhancers in the genome that drive expression of key oncogenes like EGFR and c-MET. Significance: This study suggests a novel explanation for why infections with certain HPV isotypes are associated with elevated cancer risk by identifying an epigenetic mechanism through which E6 proteins expressed by those isotypes can drive expression of key oncogenes.</p

The fragmentomic property of plasma cell-free DNA enables the non-invasive detection of diabetic nephropathy in patients with diabetes mellitus

BackgroundDiabetic nephropathy (DN) is one of the most prevalent complications of diabetes mellitus (DM). However, there is still a lack of effective methods for non-invasive diagnosis of DN in clinical practice. We aimed to explore biomarkers from plasma cell-free DNA as a surrogate of renal biopsy for the differentiation of DN patients from patients with DM.Materials and methodsThe plasma cell-free DNA (cfDNA) was sequenced from 53 healthy individuals, 53 patients with DM but without DN, and 71 patients with both DM and DN. Multidimensional features of plasma DNA were analyzed to dissect the cfDNA profile in the DM and DN patients and identify DN-specific cfDNA features. Finally, a classification model was constructed by integrating all informative cfDNA features to demonstrate the clinical utility in DN detection.ResultsIn comparison with the DM patients, the DN individuals exhibited significantly increased cfDNA concentration in plasma. The cfDNA from the DN patients showed a distinct fragmentation pattern with an altered size profile and preferred motifs that start with “CC” in the cfDNA ending sites, which were associated with deoxyribonuclease 1 like 3 (DNASE1L3) expression in the kidney. Moreover, patients with DM or DN were found to carry more alterations in whole-genome cfDNA coverage when compared with healthy individuals. We integrated DN-specific cfDNA features (cfDNA concentration, size, and motif) into a classification model, which achieved an area under the receiver operating characteristic curve (AUC) of 0.928 for the differentiation of DN patients from DM patients.ConclusionOur findings showed plasma cfDNA as a reliable non-invasive biomarker for differentiating DN patients from DM patients. The utility of cfDNA in clinical practice in large prospective cohorts is warranted

Characteristic Studies of Micron Zinc Particle Hydrolysis in a Fixed Bed Reactor

Zinc fuel is considered as a kind of promising energy sources for marine propeller. As one of the key steps for zinc marine energy power system, zinc hydrolysis process had been studied experimentally in a fixed bed reactor. In this study, we focus on the characteristics of micron zinc particle hydrolysis. The experimental results suggested that the steam inner diffusion is the controlling step of accumulative zinc particles hydrolysis reaction at a relative lower temperature and a relative higher water partial pressure. In other conditions, the chemical reaction kinetics was the controlling step. And two kinds of chemical reaction kinetics appeared in experiments: the surface reaction and the gas-gas reaction. The latter one occurs usually for larger zinc particles and high reaction temperature. Temperature seems to be one of the most important parameters for the dividing of different reaction mechanisms. Several parameters of the hydrolysis process including heating rate, water partial pressure, the particle size and temperature were also studied in this paper. Results show that the initial reaction temperature of zinc hydrolysis in fixed bed is about 410°C. And the initial reaction temperature increases as the heating rate increases and as the water partial pressure decreases. The total hydrogen yield increases as the heating rate decreases, as the water partial pressure increases, as the zinc particle size decreases, and as the reaction temperature increases. A hydrogen yield of more than 81.5% was obtained in the fixed bed experiments

Hepatocyte-Specific Smad4 Deficiency Alleviates Liver Fibrosis via the p38/p65 Pathway

Liver fibrosis is a wound-healing response caused by the abnormal accumulation of extracellular matrix, which is produced by activated hepatic stellate cells (HSCs). Most studies have focused on the activated HSCs themselves in liver fibrosis, and whether hepatocytes can modulate the process of fibrosis is still unclear. Sma mothers against decapentaplegic homologue 4 (Smad4) is a key intracellular transcription mediator of transforming growth factor-β (TGF-β) during the development and progression of liver fibrosis. However, the role of hepatocyte Smad4 in the development of fibrosis is poorly elucidated. Here, to explore the functional role of hepatocyte Smad4 and the molecular mechanism in liver fibrosis, a CCl4-induced liver fibrosis model was established in mice with hepatocyte-specific Smad4 deletion (Smad4Δhep). We found that hepatocyte-specific Smad4 deficiency reduced liver inflammation and fibrosis, alleviated epithelial-mesenchymal transition, and inhibited hepatocyte proliferation and migration. Molecularly, Smad4 deletion in hepatocytes suppressed the expression of inhibitor of differentiation 1 (ID1) and the secretion of connective tissue growth factor (CTGF) of hepatocytes, which subsequently activated the p38 and p65 signaling pathways of HSCs in an epidermal growth factor receptor-dependent manner. Taken together, our results clearly demonstrate that the Smad4 expression in hepatocytes plays an important role in promoting liver fibrosis and could therefore be a promising target for future anti-fibrotic therapy

Joint Geosequential Preference and Distance Metric Factorization for Point-of-Interest Recommendation

Point-of-interest (POI) recommendation is a valuable service to help users discover attractive locations in location-based social networks (LBSNs). It focuses on capturing users’ movement patterns and location preferences by using massive historical check-in data. In the past decade, matrix factorization has become a mature and widely used technology in POI recommendation. However, the inner product of latent vectors adopted in matrix factorization methods does not satisfy the triangle inequality property, which may limit the expressiveness and lead to suboptimal solutions. Besides, the extreme sparsity of check-in data makes it challenging to capture users’ movement preferences accurately. In this paper, we propose a joint geosequential preference and distance metric factorization framework, called GeoSeDMF, for POI recommendation. First, we introduce a distance metric factorization method that is capable of learning users’ personalized preferences from a position and distance perspective in the metric space. Specifically, we convert the user-POI interaction matrix into a distance matrix and factorize it into user and POI dense embeddings. Additionally, we measure users’ personalized preference for the POI by using the Euclidean distance metric instead of the inner product. Then, we model the users’ geospatial preference by applying a geographic weight coefficient and model the users’ sequential preference by using the Euclidean distance of continuous check-in locations. Moreover, a pointwise loss strategy and AdaGrad algorithm are adopted to optimize the positions and relationships of users and POIs in a metric space. Finally, experimental results on three large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method

Generic consistency of the break-point estimators under specification errors in a multiple-break model

This paper considers the estimation of multiple-structural-break models under specification errors. A common example in economics is that the true model is measured in level, but a linear-log model is estimated. We show that, under specification errors, if there are more than one break points and if a single-break model is estimated, the estimated break point is consistent for one of the true break points. This consistency result applies to models with multiple regressors where some or all of the regressors are misspecified. Another important contribution of this paper is that we have constructed a Sup-Wald test whose limiting distribution is not affected by model misspecification. Using this robust test, we show that the break points can be estimated sequentially one at a time. Simulation evidence and an empirical application are provided. Copyright � 2008 The Author(s). Journal compilation � Royal Economic Society 2008

Enhanced Performance of Zn-Sn/HZSM-5 Catalyst for the Conversion of Methanol to Aromatics

Natural Science Foundation of China [20923004]; Program for Changjiang Scholars and Innovative Research Team in University [IRT1036]The conversion of methanol to aromatics such as benzene, toluene, and xylenes (BTX) was performed over HZSM-5-supported bimetallic Zn-Sn catalysts. The results indicated that Sn species preferentially healed the defects in HZ crystals to create new active sites. The catalyst with Zn species markedly enhanced the aromatization performance but easily produced heavy coke. Thus, an optimized catalyst with 1 wt% Zn and 1 wt% Sn exhibited improved catalytic performance in terms of selectivity and BTX yield compared with a catalyst with a single metal. Consequently, the BTX yield was 64.1 % under the reaction conditions of 0.1 MPa and 0.8 h(-1) methanol weight hourly space velocity at 450 A degrees C