Autophagic UVRAG Promotes UV-Induced Photolesion Repair by Activation of the CRL4(DDB2) E3 Ligase.

UV-induced DNA damage, a major risk factor for skin cancers, is primarily repaired by nucleotide excision repair (NER). UV radiation resistance-associated gene (UVRAG) is a tumor suppressor involved in autophagy. It was initially isolated as a cDNA partially complementing UV sensitivity in xeroderma pigmentosum (XP), but this was not explored further. Here we show that UVRAG plays an integral role in UV-induced DNA damage repair. It localizes to photolesions and associates with DDB1 to promote the assembly and activity of the DDB2-DDB1-Cul4A-Roc1 (CRL4(DDB2)) ubiquitin ligase complex, leading to efficient XPC recruitment and global genomic NER. UVRAG depletion decreased substrate handover to XPC and conferred UV-damage hypersensitivity. We confirmed the importance of UVRAG for UV-damage tolerance using a Drosophila model. Furthermore, increased UV-signature mutations in melanoma correlate with reduced expression of UVRAG. Our results identify UVRAG as a regulator of CRL4(DDB2)-mediated NER and suggest that its expression levels may influence melanoma predisposition.

Central role of autophagic UVRAG in melanogenesis and the suntan response.

UV-induced cell pigmentation represents an important mechanism against skin cancers. Sun-exposed skin secretes α-MSH, which induces the lineage-specific transcriptional factor MITF and activates melanogenesis in melanocytes. Here, we show that the autophagic tumor suppressor UVRAG plays an integral role in melanogenesis by interaction with the biogenesis of lysosome-related organelles complex 1 (BLOC-1). This interaction is required for BLOC-1 stability and for BLOC-1-mediated cargo sorting and delivery to melanosomes. Absence of UVRAG dispersed BLOC-1 distribution and activity, resulting in impaired melanogenesis in vitro and defective melanocyte development in zebrafish in vivo. Furthermore, our results establish UVRAG as an important effector for melanocytes' response to α-MSH signaling as a direct target of MITF and reveal the molecular basis underlying the association between oncogenic BRAF and compromised UV protection in melanoma.

Novel recurrently mutated genes and a prognostic mutation signature in colorectal cancer.

BACKGROUND: Characterisation of colorectal cancer (CRC) genomes by next-generation sequencing has led to the discovery of novel recurrently mutated genes. Nevertheless, genomic data has not yet been used for CRC prognostication. OBJECTIVE: To identify recurrent somatic mutations with prognostic significance in patients with CRC. METHOD: Exome sequencing was performed to identify somatic mutations in tumour tissues of 22 patients with CRC, followed by validation of 187 recurrent and pathway-related genes using targeted capture sequencing in additional 160 cases. RESULTS: Seven significantly mutated genes, including four reported (APC, TP53, KRAS and SMAD4) and three novel recurrently mutated genes (CDH10, FAT4 and DOCK2), exhibited high mutation prevalence (6-14% for novel cancer genes) and higher-than-expected number of non-silent mutations in our CRC cohort. For prognostication, a five-gene-signature (CDH10, COL6A3, SMAD4, TMEM132D, VCAN) was devised, in which mutation(s) in one or more of these genes was significantly associated with better overall survival independent of tumor-node-metastasis (TNM) staging. The median survival time was 80.4 months in the mutant group versus 42.4 months in the wild type group (p=0.0051). The prognostic significance of this signature was successfully verified using the data set from the Cancer Genome Atlas study. CONCLUSIONS: The application of next-generation sequencing has led to the identification of three novel significantly mutated genes in CRC and a mutation signature that predicts survival outcomes for stratifying patients with CRC independent of TNM staging.

Using convolutional neural networks to decode EEG-based functional brain network with different severity of acrophobia.

Objective.The prevalence of acrophobia is high, especially with the rise of many high-rise buildings. In the recent few years, researchers have begun to analyze acrophobia from the neuroscience perspective, especially to improve the virtual reality exposure therapy (VRET). Electroencephalographic (EEG) is an informative neuroimaging technique, but it is rarely used for acrophobia. The purpose of this study is to evaluate the effectiveness of using EEGs to identify the degree of acrophobia objectively.Approach.EEG data were collected by virtual reality (VR) exposure experiments. We classified all subjects' degrees of acrophobia into three categories, where their questionnaire scores and behavior data showed significant differences. Using synchronization likelihood, we computed the functional connectivity between each pair of channels and then obtained complex networks named functional brain networks (FBNs). Basic topological features and community structure features were extracted from the FBNs. Statistical results demonstrated that FBN features can be used to distinguish different groups of subjects. We trained machine learning (ML) algorithms with FBN features as inputs and trained convolutional neural networks (CNNs) with FBNs directly as inputs.Main results.It turns out that using FBN to identify the severity of acrophobia is feasible. For ML algorithms, the community structure features of some cerebral cortex regions outperform typical topological features of the whole brain, in terms of classification accuracy. The performances of CNN algorithms are better than ML algorithms. The CNN with ResNet performs the best (accuracy reached 98.46 ± 0.42%).Significance.These observations indicate that community structures of certain cerebral cortex regions could be used to identify the degree of acrophobia. The proposed CNN framework can provide objective feedback, which could help build closed-loop VRET portable systems.

Acrophobia Quantified by EEG Based on CNN Incorporating Granger Causality.

The aim of this study is to quantify acrophobia and provide safety advices for high-altitude workers. Considering that acrophobia is a fuzzy quantity that cannot be accurately evaluated by conventional detection methods, we propose a comprehensive solution to quantify acrophobia. Specifically, this study simulates a virtual reality environment called High-altitude Plank Walking Challenge, which provides a safe and controlled experimental environment for subjects. Besides, a method named Granger Causality Convolutional Neural Network (GCCNN) combining convolutional neural network and Granger causality functional brain network is proposed to analyze the subjects' noninvasive scalp EEG signals. Here, the GCCNN method is used to distinguish the subjects with severe acrophobia, moderate acrophobia, and no acrophobia in a three-class classification task or no acrophobia and acrophobia in a two-class classification task. Compared with the mainstream methods, the GCCNN method achieves better classification performance, with an accuracy of 98.74% for the two-class classification task (no acrophobia versus acrophobia) and of 98.47% for the three-class classification task (no acrophobia versus moderate acrophobia versus severe acrophobia). Consequently, our proposed GCCNN method can provide more accurate quantitative results than the comparative methods, making it to be more competitive in further practical applications.

Exploring the fatigue affecting electroencephalography based functional brain networks during real driving in young males.

In recent years, a large proportion of traffic accidents are caused by driver fatigue. The brain has been conceived as a complex network, whose function can be assessed with EEG. Hence, in this research, fourteen subjects participated in the real driving experiments, and a comprehensive EEG-based expert system was designed for detecting driver fatigue. Collected EEG signals were first decomposed into delta-range, theta-range, alpha-range and beta-range by wavelet packet transform (WPT). Unlike other approaches, a multi-channel network construction method based on Phase Lag Index (PLI) was then proposed in this paper. Finally, the functional connectivity between alert state (at the beginning of the drive) and fatigue state (at the end of the drive) in multiple frequency bands were analyzed. The results indicate that functional connectivity of the brain area was significantly different between alert and fatigue states, especially in alpha-range and beta-range. Particularly, the frontal-to-parietal functional connectivity was weakened. Meanwhile, lower clustering coefficient (C) values and higher characteristic path length (L) values were observed in fatigue state in comparison with alert state. Based on this, two new EEG feature selection approaches, C and L in the corresponding sub-frequency range were applied to feature recognition and classification system. Using a support vector machine (SVM) machine learning algorithm, these features were combined to distinguish between alert and fatigue states, achieving an accuracy of 94.4%, precision of 94.3%, sensitivity of 94.6% and false alarm rate of 5.7%. The results suggest that brain network analysis approaches combined with SVM are helpful to alert drivers while being sleepy or even fatigue.

Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness.

A large number of traffic accidents due to driver drowsiness have been under more attention of many countries. The organization of the functional brain network is associated with drowsiness, but little is known about the brain network topology that is modulated by drowsiness. To clarify this problem, in this study, we introduce a novel approach to detect driver drowsiness. Electroencephalogram (EEG) signals have been measured during a simulated driving task, in which participants are recruited to undergo both alert and drowsy states. The filtered EEG signals are then decomposed into multiple frequency bands by wavelet packet transform. Functional connectivity between all pairs of channels for multiple frequency bands is assessed using the phase lag index (PLI). Based on this, PLI-weighted networks are subsequently calculated, from which minimum spanning trees are constructed-a graph method that corrects for comparison bias. Statistical analyses are performed on graph-derived metrics as well as on the PLI connectivity values. The major finding is that significant differences in the delta frequency band for three graph metrics and in the theta frequency band for five graph metrics suggesting network integration and communication between network nodes are increased from alertness to drowsiness. Together, our findings also suggest a more line-like configuration in alert states and a more star-like topology in drowsy states. Collectively, our findings point to a more proficient configuration in drowsy state for lower frequency bands. Graph metrics relate to the intrinsic organization of functional brain networks, and these graph metrics may provide additional insights on driver drowsiness detection for reducing and preventing traffic accidents and further understanding the neural mechanisms of driver drowsiness.

Whole Exome Sequencing Identifies Frequent Somatic Mutations in Cell-Cell Adhesion Genes in Chinese Patients with Lung Squamous Cell Carcinoma.

Lung squamous cell carcinoma (SQCC) accounts for about 30% of all lung cancer cases. Understanding of mutational landscape for this subtype of lung cancer in Chinese patients is currently limited. We performed whole exome sequencing in samples from 100 patients with lung SQCCs to search for somatic mutations and the subsequent target capture sequencing in another 98 samples for validation. We identified 20 significantly mutated genes, including TP53, CDH10, NFE2L2 and PTEN. Pathways with frequently mutated genes included those of cell-cell adhesion/Wnt/Hippo in 76%, oxidative stress response in 21%, and phosphatidylinositol-3-OH kinase in 36% of the tested tumor samples. Mutations of Chromatin regulatory factor genes were identified at a lower frequency. In functional assays, we observed that knockdown of CDH10 promoted cell proliferation, soft-agar colony formation, cell migration and cell invasion, and overexpression of CDH10 inhibited cell proliferation. This mutational landscape of lung SQCC in Chinese patients improves our current understanding of lung carcinogenesis, early diagnosis and personalized therapy.

Inactivated vaccine with adjuvants consisting of pattern recognition receptor agonists confers protection against avian influenza viruses in chickens.

Use of adjuvant containing pathogen pattern recognition receptor agonists is one of the effective strategies to enhance the efficacy of licensed vaccines. In this study, we investigated the efficacy of avian influenza vaccines containing an adjuvant (CVCVA5) which was composed of polyriboinosinic polyribocytidylic, resiquimod, imiquimod, muramyl dipeptide and levomisole. Avian influenza vaccines adjuvanted with CVCVA5 were found to induce significantly higher titers of hemagglutiniton inhibition antibodies (P≤0.01) than those of commercial vaccines at 2-, 3- and 4-week post vaccination in both specific pathogen free (SPF) chickens and field application. Furthermore, virus shedding was reduced in SPF chickens immunized with H9-CVCVA5 vaccine after H9 subtype heterologous virus challenge. The ratios of both CD3(+)CD4(+) and CD3(+)CD8(+) lymphocytes were slowly elevated in chickens immunized with H9-CVCVA5 vaccine. Lymphocytes adoptive transfer study indicates that CD8(+) T lymphocyte subpopulation might have contributed to improved protection against heterologous virus challenge. Results of this study suggest that the adjuvant CVCVA5 was capable of enhancing the potency of existing avian influenza vaccines by increasing humoral and cellular immune response.

Selenylation modification can enhance immune-enhancing activity of Chinese angelica polysaccharide.

On the basis of previous test that selenizing Chinese angelica polysaccharides (sCAPs) with stronger immune-enhancing activity in vitro were picked out, the immune-enhancing activity in vivo of three sCAPs, sCAP2, sCAP6 and sCAP8, at high and low dosage were compared taking the unmodified Chinese angelica polysaccharide (CAP) as control by determination of peripheral lymphocyte proliferation, serum antibody titer, IFN-γ and IL-6 contents in chicken vaccinated with Newcastle Disease vaccine. The results showed that three sCAPs at suitable dosage could significantly promote lymphocyte proliferation, enhance serum antibody titer, IFN-γ and IL-6 contents as compared with unmodified CAP, sCAP2 at low dosage possessed the strongest action. These results indicated that selenylation modification could significantly enhance the immune-enhancing activity of CAP, sCAP2 possessed the best efficacy and would be as a component drug of new-type immunoenhancer.