Proteomic Analysis of Huntington's Disease Medium Spiny Neurons Identifies Alterations in Lipid Droplets.

Huntington's disease (HD) is a neurodegenerative disease caused by a CAG repeat expansion in the Huntingtin (HTT) gene. The resulting polyglutamine (polyQ) tract alters the function of the HTT protein. Although HTT is expressed in different tissues, the medium-spiny projection neurons (MSNs) in the striatum are particularly vulnerable in HD. Thus, we sought to define the proteome of human HD patient-derived MSNs. We differentiated HD72-induced pluripotent stem cells and isogenic controls into MSNs and carried out quantitative proteomic analysis. Using data-dependent acquisitions with FAIMS for label-free quantification on the Orbitrap Lumos mass spectrometer, we identified 6323 proteins with at least two unique peptides. Of these, 901 proteins were altered significantly more in the HD72-MSNs than in isogenic controls. Functional enrichment analysis of upregulated proteins demonstrated extracellular matrix and DNA signaling (DNA replication pathway, double-strand break repair, G1/S transition) with the highest significance. Conversely, processes associated with the downregulated proteins included neurogenesis-axogenesis, the brain-derived neurotrophic factor-signaling pathway, Ephrin-A:EphA pathway, regulation of synaptic plasticity, triglyceride homeostasis cholesterol, plasmid lipoprotein particle immune response, interferon-γ signaling, immune system major histocompatibility complex, lipid metabolism, and cellular response to stimulus. Moreover, proteins involved in the formation and maintenance of axons, dendrites, and synapses (e.g., septin protein members) were dysregulated in HD72-MSNs. Importantly, lipid metabolism pathways were altered, and using quantitative image analysis, we found that lipid droplets accumulated in the HD72-MSN, suggesting a deficit in the turnover of lipids possibly through lipophagy. Our proteomics analysis of HD72-MSNs identified relevant pathways that are altered in MSNs and confirm current and new therapeutic targets for HD.

The discovery of oxazolones-grafted spirooxindoles via three-component diversity oriented synthesis and their preliminary biological evaluation.

A facile method via 1,3-dipolar cycloaddition of substituted benzylidene-2-phenyloxazolone under mild conditions with azomethine ylides, which were generated in situ by a decarboxylative route from a common set of diverse isatins and amino acid derivatives was developed for a 15-membered library of regio- and stereoselective oxazolones-grafted spirooxindole-pyrrolidine, pyrrolizidines and pyrrolothiazoles. After screening their cytotoxic activities against a spectrum of cell-lines, compound 4h was identified as potent antitumor agent and inducing apoptosis. The present study has provided an effective entry to rapidly construct a chemical library of oxazolones-grafted spirooxindoles and developed a good lead compound for subsequent optimization.

GVVST: Image-Driven Style Extraction From Graph Visualizations for Visual Style Transfer.

Incorporating automatic style extraction and transfer from existing well-designed graph visualizations can significantly alleviate the designer's workload. There are many types of graph visualizations. In this paper, our work focuses on node-link diagrams. We present a novel approach to streamline the design process of graph visualizations by automatically extracting visual styles from well-designed examples and applying them to other graphs. Our formative study identifies the key styles that designers consider when crafting visualizations, categorizing them into global and local styles. Leveraging deep learning techniques such as saliency detection models and multi-label classification models, we develop end-to-end pipelines for extracting both global and local styles. Global styles focus on aspects such as color scheme and layout, while local styles are concerned with the finer details of node and edge representations. Through a user study and evaluation experiment, we demonstrate the efficacy and time-saving benefits of our method, highlighting its potential to enhance the graph visualization design process.

VividGraph: Learning to Extract and Redesign Network Graphs From Visualization Images.

Network graphs are common visualization charts. They often appear in the form of bitmaps in articles, web pages, magazine prints, and designer sketches. People often want to modify graphs because of their poor design, but it is difficult to obtain their underlying data. In this article, we present VividGraph, a pipeline for automatically extracting and redesigning graphs from static images. We propose using convolutional neural networks to solve the problem of graph data extraction. Our method is robust to hand-drawn graphs, blurred graph images, and large graph images. We also present a graph classification module to make it effective for directed graphs. We propose two evaluation methods to demonstrate the effectiveness of our approach. It can be used to quickly transform designer sketches, extract underlying data from existing graphs, and interactively redesign poorly designed graphs.

GraphDecoder: Recovering Diverse Network Graphs from Visualization Images via Attention-Aware Learning.

DNGs are diverse network graphs with texts and different styles of nodes and edges, including mind maps, modeling graphs, and flowcharts. They are high-level visualizations that are easy for humans to understand but difficult for machines. Inspired by the process of human perception of graphs, we propose a method called GraphDecoder to extract data from raster images. Given a raster image, we extract the content based on a neural network. We built a semantic segmentation network based on U-Net. We increase the attention mechanism module, simplify the network model, and design a specific loss function to improve the model's ability to extract graph data. After this semantic segmentation network, we can extract the data of all nodes and edges. We then combine these data to obtain the topological relationship of the entire DNG. We also provide an interactive interface for users to redesign the DNGs. We verify the effectiveness of our method by evaluations and user studies on datasets collected on the Internet and generated datasets.

Visual Recommendation for Peer-To-Peer Accommodation with Online Reviews based on Sentiment Analysis and Topic Models.

Abstract: Peer-to-peer accommodation is developing rapidly in the era of sharing economy, and the visual recommendation of accommodation is also an urgent problem to be solved. Meanwhile, user-generated content is critical in P2P accommodations, because they contain a wealth of information about the opinions and experiences of users, which helps understand consumer decisions and improve products and services better. However, the huge volume of reviews makes it difficult for potential customers to gain useful insights and for managers to track customer opinions. In this paper, we propose a complete pipeline for recommending personalized accommodations for consumers, while also providing insights for managers. First, we use topic modeling techniques to mining opinions from review. Second, we build a deep learning network for review sentiment analysis. Third, we perform sentiment analysis of the reviews at the aspect level to obtain the sentiment vector representation of the accommodation. Finally, we propose a personalized accommodation recommendation method based on the above work. Moreover, we design a visual analytic system with a user-friendly interface to facilitate interactive analysis. Evaluation including user and case studies demonstrates the usefulness and effectiveness of our method and system.

Postnatal Conditional Deletion of Bcl11b in Striatal Projection Neurons Mimics the Transcriptional Signature of Huntington's Disease.

The dysregulation of striatal gene expression and function is linked to multiple diseases, including Huntington's disease (HD), Parkinson's disease, X-linked dystonia-parkinsonism (XDP), addiction, autism, and schizophrenia. Striatal medium spiny neurons (MSNs) make up 90% of the neurons in the striatum and are critical to motor control. The transcription factor, Bcl11b (also known as Ctip2), is required for striatal development, but the function of Bcl11b in adult MSNs in vivo has not been investigated. We conditionally deleted Bcl11b specifically in postnatal MSNs and performed a transcriptomic and behavioral analysis on these mice. Multiple enrichment analyses showed that the D9-Cre-Bcl11btm1.1Leid transcriptional profile was similar to the HD gene expression in mouse and human data sets. A Gene Ontology enrichment analysis linked D9-Cre-Bcl11btm1.1Leid to calcium, synapse organization, specifically including the dopaminergic synapse, protein dephosphorylation, and HDAC-signaling, commonly dysregulated pathways in HD. D9-Cre-Bcl11btm1.1Leid mice had decreased DARPP-32/Ppp1r1b in MSNs and behavioral deficits, demonstrating the dysregulation of a subtype of the dopamine D2 receptor expressing MSNs. Finally, in human HD isogenic MSNs, the mislocalization of BCL11B into nuclear aggregates points to a mechanism for BCL11B loss of function in HD. Our results suggest that BCL11B is important for the function and maintenance of mature MSNs and Bcl11b loss of function drives, in part, the transcriptomic and functional changes in HD.

Unbiased identification of novel transcription factors in striatal compartmentation and striosome maturation.

Many diseases are linked to dysregulation of the striatum. Striatal function depends on neuronal compartmentation into striosomes and matrix. Striatal projection neurons are GABAergic medium spiny neurons (MSNs), subtyped by selective expression of receptors, neuropeptides, and other gene families. Neurogenesis of the striosome and matrix occurs in separate waves, but the factors regulating compartmentation and neuronal differentiation are largely unidentified. We performed RNA- and ATAC-seq on sorted striosome and matrix cells at postnatal day 3, using the Nr4a1-EGFP striosome reporter mouse. Focusing on the striosome, we validated the localization and/or role of Irx1, Foxf2, Olig2, and Stat1/2 in the developing striosome and the in vivo enhancer function of a striosome-specific open chromatin region 4.4 Kb downstream of Olig2. These data provide novel tools to dissect and manipulate the networks regulating MSN compartmentation and differentiation, including in human iPSC-derived striatal neurons for disease modeling and drug discovery.

Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework.

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods.

GAMDB: a web resource to connect microRNAs with autophagy in gerontology.

OBJECTIVES: MicroRNAs (miRNAs) are endogenous ~23 nucleotides (nt) RNAs, regulating gene expression by pairing to the mRNAs of protein-coding genes to direct their post-transcriptional repression. Both in normal and aberrant activities, miRNAs contribute to a recurring paradigm of cellular behaviors in pathological settings, especially in gerontology. Autophagy, a multi-step lysosomal degradation process with function to degrade long-lived proteins and damaged organelles, has significant impact on gerontology. Thus, elucidating how miRNAs participate in autophagy may enlarge the scope of miRNA in autophagy and facilitate researches in gerontology. MATERIALS AND METHODS: Herein, based upon the published studies, predicted targets and gerontology-related diseases, we constructed a web resource named Gerontology-Autophagic-MicroRNA Database (GAMDB) (http://gamdb.liu-lab.com/index.php), which contained 836 autophagy-related miRNAs, 197 targeted genes/proteins and 56 aging-related diseases such as Parkinson' disease, Alzheimer's disease and Huntington's disease. RESULTS AND CONCLUSION: We made use of large amounts of data to elucidate the intricate relationships between microRNA-regulated autophagic mechanisms and gerontology. This database will facilitate better understanding of autophagy regulation network in gerontology and thus promoting gerontology-related therapy in the future.

Review of natural product databases.

OBJECTIVES: Many natural products have pharmacological or biological activities that can be of therapeutic benefit in treating diseases, and are also an important source of inspiration for development of potential novel drugs. The past few decades have witnessed extensive study of natural products for their promising prospects in application of medicinal chemistry, molecular biology and pharmaceutical sciences. MATERIALS AND METHODS: Natural product databases have provided systematic collection of information concerning natural products and their derivatives, including structure, source and mechanisms of action, which significantly support modern drug discovery. RESULTS: Currently, a considerable number of natural product databases, such as TCM Database@Taiwan, TCMID, CEMTDD, SuperToxic and SuperNatural, have been developed, providing data such as integrated medicinal herbs, ingredients, 2D/3D structures of the compounds, related target proteins, relevant diseases, and metabolic toxicity and more. CONCLUSIONS: We focus on an analytical overview of current natural product databases, and further discuss the good, bad or imperfection of current ones, in the hope of better integrating existing relevant outcomes, thus providing new routes for future drug discovery.

Integrated in silico and experimental methods revealed that Arctigenin inhibited angiogenesis and HCT116 cell migration and invasion through regulating the H1F4A and Wnt/ß-catenin pathway.

Arctigenin (ARG) has been previously reported to exert diverse biological activities including anti-proliferation, anti-inflammatory, and antiviral, etc. In the current study, the anti-metastasis and anti-angiogenesis activities of ARG were investigated. To further understand how ARG played these bioactivities, proteomic approaches were used to profile the proteome changes in response to ARG treatment using 2DE-MS/MS. Using these approaches, a total of 50 differentially expressed proteins were identified and clustered. Bioinformatics analysis suggested that multiple signalling pathways were involved. Moreover, ARG induced anti-metastatic and anti-angiogenesis activities were mainly accompanied by a deactivation of the Wnt/ß-catenin pathway in HCT116 cells.

Identification of ULK1 as a novel biomarker involved in miR-4487 and miR-595 regulation in neuroblastoma SH-SY5Y cell autophagy.

Autophagy, referring to an evolutionarily conserved, multi-step lysosomal degradation process, has been well-known to be initiated by Unc-51 like kinase 1 (ULK1) with some links to Parkinson's disease (PD). MicroRNAs (miRNAs), small and non-coding endogenous RNAs 22 ~ 24 nucleotides (nt) in length, have been demonstrated to play an essential role for modulating autophagy. Recently, the relationships between miRNAs and autophagy have been widely reported in PD; however, how microRNAs regulate autophagy still remains in its infancy. Thus, in this study, we computationally constructed the ULK1-regulated autophagic kinase subnetwork in PD and further identified ULK1 able to negatively regulate p70(S6K) in starvation-induced autophagy of neuroblastoma SH-SY5Y cells. Combination of in silico prediction and microarray analyses, we identified that miR-4487 and miR-595 could target ULK1 and experimentally verified they could negatively or positively regulate ULK1-mediated autophagy. In conclusion, these results may uncover the novel ULK1-p70(S6K) autophagic pathway, as well as miR-4487 and miR-595 as new ULK1 target miRNAs. Thus, these findings would provide a clue to explore ULK1 and its target miRNAs as potential biomarkers in the future PD therapy.