Nuclear interaction of Arp2/3 complex and BRAFV600E promotes aggressive behavior and vemurafenib resistance of thyroid cancer.

The presence of mutant BRAF V600E correlates with the risk of recurrence in papillary thyroid cancer (PTC) patients. However, not all PTC patients with BRAF V600E are associated with poor prognosis. Thus, understanding the mechanisms by which certain PTC patients with nuclear BRAF V600E become aggressive and develop resistance to a selective BRAF inhibitor, PLX-4032, is urgently needed. The effect of nuclear localization of BRAFV600E using in vitro studies, xenograft mouse-model and human tissues was evaluated. PTC cells harboring a nuclear localization signal (NLS) of BRAFV600E were established and examined in nude mice implanted with TPC1-NLS-BRAFV600E cells followed by PLX-4032 treatment. Immunohistochemical (IHC) analysis was performed on 100 PTC specimens previously confirmed that they have BRAFV600E mutations. Our results demonstrate that 21 of 100 (21%) PTC tissues stained with specific BRAFV600E antibody had nuclear staining with more aggressive features compared to their cytosolic counterparts. In vitro studies show that BRAFV600E is transported between the nucleus and the cytosol through CRM1 and importin (α/ß) system. Sequestration of BRAFV600E in the cytosol sensitized resistant cells to PLX-4032, whereas nuclear BRAFV600E was associated with aggressive phenotypes and developed drug resistance. Proteomic analysis revealed Arp2/3 complex members, actin-related protein 2 (ACTR2 aliases ARP2) and actin-related protein 3 (ACTR3 aliases ARP3), as the most enriched nuclear BRAFV600E partners. ACTR3 was highly correlated to lymph node stage and extrathyroidal extension and was validated with different functional assays. Our findings provide new insights into the clinical utility of the nuclear BRAFV600E as a prognostic marker for PTC aggressiveness and determine the efficacy of selective BRAFV600E inhibitor treatment which opens new avenues for future treatment options.

Insights into few shot learning approaches for image scene classification.

Image understanding and scene classification are keystone tasks in computer vision. The development of technologies and profusion of existing datasets open a wide room for improvement in the image classification and recognition research area. Notwithstanding the optimal performance of exiting machine learning models in image understanding and scene classification, there are still obstacles to overcome. All models are data-dependent that can only classify samples close to the training set. Moreover, these models require large data for training and learning. The first problem is solved by few-shot learning, which achieves optimal performance in object detection and classification but with a lack of eligible attention in the scene classification task. Motivated by these findings, in this paper, we introduce two models for few-shot learning in scene classification. In order to trace the behavior of those models, we also introduce two datasets (MiniSun; MiniPlaces) for image scene classification. Experimental results show that the proposed models outperform the benchmark approaches in respect of classification accuracy.

Xconnector: Retrieving and visualizing metabolites and pathways information from various database resources.

Metabolomics databases contain crucial information collected from various biological systems and experiments. Developers and scientists performed massive efforts to make the database public and accessible. The diversity of the metabolomics databases arises from the different data types included within the database originating from various sources and experiments can be confusing for biologists and researchers who need further manual investigation for the retrieved data. Xconnector is a software package designed to easily retrieve and visualize metabolomics data from different databases. Xconnector can parse information from Human Metabolome Database (HMDB), Livestock Metabolome Database (LMDB), Yeast Metabolome Database (YMDB), Toxin and Toxin Target Database (T3DB), ReSpect Phytochemicals Database (ReSpectDB), The Blood Exposome Database, Phenol-Explorer Database, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Small Molecule Pathway Database (SMPDB). Using Python language, Xconnector connects the targeted databases, recover requested metabolites from single or different database sources, reformat and repack the data to generate a single Excel CSV file containing all information from the databases, in an application programming interface (API)/ Python dependent manner seamlessly. In addition, Xconnector automatically generates graphical outputs in a time-saving approach ready for publication. SIGNIFICANCE: The powerful ability of Xconnector to summarize metabolomics information from different sources would enable researchers to get a closer glimpse on the nature of potential molecules of interest toward medical diagnostics, better biomarker discovery, and personalized medicine. The software is available as an executable application and as a python package compatible for different operating systems.

Hesitancy of Arab Healthcare Workers towards COVID-19 Vaccination: A Large-Scale Multinational Study.

BACKGROUND: Health care workers (HCWs) are at increased risk of acquiring and transmitting COVID-19 infection. Moreover, they present role models for communities with regards to attitudes towards COVID-19 vaccination. Hence, hesitancy of HCWs towards vaccination can crucially affect the efforts aiming to contain the pandemic. Previously published studies paid little attention to HCWs in Arab countries, which have a population of over 440 million. OBJECTIVES: To assess the rates of COVID-19 vaccine hesitancy in Arabic-speaking HCWs residing in and outside Arab countries, and their perceived barriers towards vaccination. METHODS: A cross-sectional study based on an online survey was conducted from 14-29 January 2021, targeting Arabic-speaking HCWs from all around the world. RESULTS: The survey recruited 5708 eligible participants (55.6% males, 44.4% females, age 30.6 ± 10 years) from 21 Arab countries (87.5%) and 54 other countries (12.5%). Our analysis showed a significant rate of vaccine hesitancy among Arabic-speaking HCWs residing in and outside of Arab countries (25.8% and 32.8%, respectively). The highest rates of hesitancy were among participants from the western regions of the Arab world (Egypt, Morocco, Tunisia, and Algeria). The most cited reasons for hesitancy were concerns about side effects and distrust of the expedited vaccine production and healthcare policies. Factors associated with higher hesitancy included age of 30-59, previous or current suspected or confirmed COVID-19, female gender, not knowing the vaccine type authorized in the participant's country, and not regularly receiving the influenza vaccine. CONCLUSION: This is the first large-scale multinational post-vaccine-availability study on COVID-19 vaccine hesitancy among HCWs. It reveals high rates of hesitancy among Arab-speaking HCWs. Unless addressed properly, this hesitancy can impede the efforts for achieving widespread vaccination and collective immunity.

Aggresomes predict poor outcomes and implicate proteostasis in the pathogenesis of pediatric choroid plexus tumors.

PURPOSE: Protein misfolding and aggregation result in proteotoxic stress and underlie the pathogenesis of many diseases. To overcome proteotoxicity, cells compartmentalize misfolded and aggregated proteins in different inclusion bodies. The aggresome is a paranuclear inclusion body that functions as a storage compartment for misfolded proteins. Choroid plexus tumors (CPTs) are rare neoplasms comprised of three pathological subgroups. The underlying mechanisms of their pathogenesis remain unclear. This study aims to elucidate the prognostic role and the biological effects of aggresomes in pediatric CPTs. METHODS: We examined the presence of aggresomes in 42 patient-derived tumor tissues by immunohistochemistry and we identified their impact on patients' outcomes. We then investigated the proteogenomics signature associated with aggresomes using whole-genome DNA methylation and proteomic analysis to define their role in the pathogenesis of pediatric CPTs. RESULTS: Aggresomes were detected in 64.2% of samples and were distributed among different pathological and molecular subgroups. The presence of aggresomes with different percentages was correlated with patients' outcomes. The ≥ 25% cutoff had the most significant impact on overall and event-free survival (p-value < 0.001) compared to the pathological and the molecular stratifications. CONCLUSIONS: These results support the role of aggresome as a novel prognostic molecular marker for pediatric CPTs that was comparable to the molecular classification in segregating samples into two distinct subgroups, and to the pathological stratification in the prediction of patients' outcomes. Moreover, the proteogenomic signature of CPTs displayed altered protein homeostasis, manifested by enrichment in processes related to protein quality control.

GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis.

Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have provided significant impact on our understanding of biological systems. They contribute to almost all aspects of biomedical research, including studying developmental biology, host-parasite relationships, disease progression and drug effects. However, the high-throughput data generations present challenges for many wet experimentalists to analyze and take full advantage of such rich and complex data. Here we present GeneCloudOmics, an easy-to-use web server for high-throughput gene expression analysis that extends the functionality of our previous ABioTrans with several new tools, including protein datasets analysis, and a web interface. GeneCloudOmics allows both microarray and RNA-Seq data analysis with a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do. In total, GeneCloudOmics provides the user access to 23 different data analytical and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathway enrichments, evolutionary analyses, pathological analyses, and protein-protein interaction (PPI) identifications. Furthermore, GeneCloudOmics allows the direct import of gene expression data from the NCBI Gene Expression Omnibus database. The user can perform all tasks rapidly through an intuitive graphical user interface that overcomes the hassle of coding, installing tools/packages/libraries and dealing with operating systems compatibility and version issues, complications that make data analysis tasks challenging for biologists. Thus, GeneCloudOmics is a one-stop open-source tool for gene expression data analysis and visualization. It is freely available at http://combio-sifbi.org/GeneCloudOmics.

UniprotR: Retrieving and visualizing protein sequence and functional information from Universal Protein Resource (UniProt knowledgebase).

UniprotR is a software package designed to easily retrieve, cluster and visualize protein data from UniProt knowledgebase (UniProtKB) using R language. The package is implemented mainly to process, parse and illustrate proteomics data in a handy and time-saving approach allowing researchers to summarize all required protein information available at UniProtKB in a readable data frame, Excel CSV file, and/or graphical output. UniprotR generates a set of graphics including gene ontology, chromosomal location, protein scoring and status, protein networking, sequence phylogenetic tree, and physicochemical properties. In addition, the package supports clustering of proteins based on primary gene name or chromosomal location, facilitating additional downstream analysis. SIGNIFICANCE: In this work, we implemented a robust package for retrieving and visualizing information from multiple sources such UniProtKB, SWISS-MODEL, and STRING. UniprotR Contains functions that enable retrieving and cluster data in a handy way and visualize data in publishable graphs to facilitate researcher's work and fulfill their needs. UniprotR will aid in saving time for downstream data analysis instead of manual time consuming data analysis. AVAILABILITY AND IMPLEMENTATION: UniprotR released as free open source code under the license of GPLv3, and available in CRAN (The Comprehensive R Archive Network) and GitHub. (https://cran.r-project.org/web/packages/UniprotR/index.html). (https://github.com/Proteomicslab57357/UniprotR).

vhcub: Virus-host codon usage co-adaptation analysis.

Viruses show noticeable evolution to adapt and reproduce within their hosts. Theoretically, patterns and factors that affect the codon usage of viruses should reflect evolutionary changes that allow them to optimize their codon usage to their hosts. Some software tools can analyze the codon usage of organisms; however, their performance has room for improvement, as these tools do not focus on examining the codon usage co-adaptation between viruses and their hosts. This paper describes the vhcub R package, which is a crucial tool used to analyze the co-adaptation of codon usage between a virus and its host, with several implementations of indices and plots. The tool is available from: https://cran.r-project.org/web/packages/vhcub/.