Enhancer Reprogramming Confers Dependence on Glycolysis and IGF Signaling in KMT2D Mutant Melanoma.

Histone methyltransferase KMT2D harbors frequent loss-of-function somatic point mutations in several tumor types, including melanoma. Here, we identify KMT2D as a potent tumor suppressor in melanoma through an in vivo epigenome-focused pooled RNAi screen and confirm the finding by using a genetically engineered mouse model (GEMM) based on conditional and melanocyte-specific deletion of KMT2D. KMT2D-deficient tumors show substantial reprogramming of key metabolic pathways, including glycolysis. KMT2D deficiency aberrantly upregulates glycolysis enzymes, intermediate metabolites, and glucose consumption rates. Mechanistically, KMT2D loss causes genome-wide reduction of H3K4me1-marked active enhancer chromatin states. Enhancer loss and subsequent repression of IGFBP5 activates IGF1R-AKT to increase glycolysis in KMT2D-deficient cells. Pharmacological inhibition of glycolysis and insulin growth factor (IGF) signaling reduce proliferation and tumorigenesis preferentially in KMT2D-deficient cells. We conclude that KMT2D loss promotes tumorigenesis by facilitating an increased use of the glycolysis pathway for enhanced biomass needs via enhancer reprogramming, thus presenting an opportunity for therapeutic intervention through glycolysis or IGF pathway inhibitors

Rentrez 1.0.0

<p>An R package that provides an R interface to the NCBI's EUtils API<br> allowing users to search databases like GenBank and PubMed, process the<br> results of those searches and pull data into their R sessions.</p

Genomics in Neurological Disorders

Neurological disorders comprise a variety of complex diseases in the central nervous system, which can be roughly classified as neurodegenerative diseases and psychiatric disorders. The basic and translational research of neurological disorders has been hindered by the difficulty in accessing the pathological center (i.e., the brain) in live patients. The rapid advancement of sequencing and array technologies has made it possible to investigate the disease mechanism and biomarkers from a systems perspective. In this review, recent progresses in the discovery of novel risk genes, treatment targets and peripheral biomarkers employing genomic technologies will be discussed. Our major focus will be on two of the most heavily investigated neurological disorders, namely Alzheimer’s disease and autism spectrum disorder

The synergistic and interactive effects of slip systems and dynamic recrystallization on the weakening basal texture of Mg-Y-Nd-Zr-Gd magnesium alloy

Magnesium (Mg) alloys are known to exhibit strong basal texture following large plastic deformation. However, recent experimental studies have revealed that the Mg-Y-Nd-Zr-Gd series alloy displays a weak texture after undergoing 60% compression at 500 °C. The overall deformation texture is similar to that of dynamic recrystallization (DRX) subsets, and the (0001) pole figure of the deformed grain subsets does not concentrate entirely in the compression direction due to the activation of non-basal slip systems. It should be noted that DRX and non-basal slip systems cannot independently account for the final deformation texture. In this case, continuous dynamic recrystallization (CDRX) occurs, where the segregation of rare earth (RE) elements at grain boundaries (GB) impedes the movement of existing GBs and prevents the occurrence of discontinuous dynamic recrystallization (DDRX). Low-angle grain boundaries (LAGB), which are subject to local strain-regulated distribution, continue to absorb dislocations and produce new CDRXed grains. Notably, non-basal slip systems play a role in the generation and subsequent orientation behavior of DRXed grains. As DRX progresses, complex crystal rotations occur on both sides of the newly formed DRX interfaces, causing the orientation of the DRXed grains to gradually deviate from that of the deformed parent grains

Scalable Multi-view Clustering with Graph Filtering

With the explosive growth of multi-source data, multi-view clustering has attracted great attention in recent years. Most existing multi-view methods operate in raw feature space and heavily depend on the quality of original feature representation. Moreover, they are often designed for feature data and ignore the rich topology structure information. Accordingly, in this paper, we propose a generic framework to cluster both attribute and graph data with heterogeneous features. It is capable of exploring the interplay between feature and structure. Specifically, we first adopt graph filtering technique to eliminate high-frequency noise to achieve a clustering-friendly smooth representation. To handle the scalability challenge, we develop a novel sampling strategy to improve the quality of anchors. Extensive experiments on attribute and graph benchmarks demonstrate the superiority of our approach with respect to state-of-the-art approaches

Towards Personalized Intervention for Alzheimer’s Disease

AbstractAlzheimer’s disease (AD) remains to be a grand challenge for the international community despite over a century of exploration. A key factor likely accounting for such a situation is the vast heterogeneity in the disease etiology, which involves very complex and divergent pathways. Therefore, intervention strategies shall be tailored for subgroups of AD patients. Both demographic and in-depth information is needed for patient stratification. The demographic information includes primarily APOE genotype, age, gender, education, environmental exposure, life style, and medical history, whereas in-depth information stems from genome sequencing, brain imaging, peripheral biomarkers, and even functional assays on neurons derived from patient-specific induced pluripotent cells (iPSCs). Comprehensive information collection, better understanding of the disease mechanisms, and diversified strategies of drug development would help with more effective intervention in the foreseeable future

Concerted Perturbation Observed in a Hub Network in Alzheimer’s Disease

<div><p>Alzheimer’s disease (AD) is a progressive neurodegenerative disease involving the alteration of gene expression at the whole genome level. Genome-wide transcriptional profiling of AD has been conducted by many groups on several relevant brain regions. However, identifying the most critical dys-regulated genes has been challenging. In this work, we addressed this issue by deriving critical genes from perturbed subnetworks. Using a recent microarray dataset on six brain regions, we applied a heaviest induced subgraph algorithm with a modular scoring function to reveal the significantly perturbed subnetwork in each brain region. These perturbed subnetworks were found to be significantly overlapped with each other. Furthermore, the hub genes from these perturbed subnetworks formed a connected hub network consisting of 136 genes. Comparison between AD and several related diseases demonstrated that the hub network was robustly and specifically perturbed in AD. In addition, strong correlation between the expression level of these hub genes and indicators of AD severity suggested that this hub network can partially reflect AD progression. More importantly, this hub network reflected the adaptation of neurons to the AD-specific microenvironment through a variety of adjustments, including reduction of neuronal and synaptic activities and alteration of survival signaling. Therefore, it is potentially useful for the development of biomarkers and network medicine for AD.</p> </div