36,502 research outputs found
Cancer Biology Data Curation at the Mouse Tumor Biology Database (MTB)
Many advances in the field of cancer biology have been made using mouse models of human cancer. The Mouse Tumor Biology (MTB, "http://tumor.informatics.jax.org":http://tumor.informatics.jax.org) database provides web-based access to data on spontaneous and induced tumors from genetically defined mice (inbred, hybrid, mutant, and genetically engineered strains of mice). These data include standardized tumor names and classifications, pathology reports and images, mouse genetics, genomic and cytogenetic changes occurring in the tumor, strain names, tumor frequency and latency, and literature citations.

Although primary source for the data represented in MTB is peer-reviewed scientific literature an increasing amount of data is derived from disparate sources. MTB includes annotated histopathology images and cytogenetic assay images for mouse tumors where these data are available from The Jackson Laboratory’s mouse colonies and from outside contributors. MTB encourages direct submission of mouse tumor data and images from the cancer research community and provides investigators with a web-accessible tool for image submission and annotation. 

Integrated searches of the data in MTB are facilitated by the use of several controlled vocabularies and by adherence to standard nomenclature. MTB also provides links to other related online resources such as the Mouse Genome Database, Mouse Phenome Database, the Biology of the Mammary Gland Web Site, Festing's Listing of Inbred Strains of Mice, the JAX® Mice Web Site, and the Mouse Models of Human Cancers Consortium's Mouse Repository. 

MTB provides access to data on mouse models of cancer via the internet and has been designed to facilitate the selection of experimental models for cancer research, the evaluation of mouse genetic models of human cancer, the review of patterns of mutations in specific cancers, and the identification of genes that are commonly mutated across a spectrum of cancers.

MTB is supported by NCI grant CA089713
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The Alliance of Genome Resources: Building a Modern Data Ecosystem for Model Organism Databases.
Model organisms are essential experimental platforms for discovering gene functions, defining protein and genetic networks, uncovering functional consequences of human genome variation, and for modeling human disease. For decades, researchers who use model organisms have relied on Model Organism Databases (MODs) and the Gene Ontology Consortium (GOC) for expertly curated annotations, and for access to integrated genomic and biological information obtained from the scientific literature and public data archives. Through the development and enforcement of data and semantic standards, these genome resources provide rapid access to the collected knowledge of model organisms in human readable and computation-ready formats that would otherwise require countless hours for individual researchers to assemble on their own. Since their inception, the MODs for the predominant biomedical model organisms [Mus sp (laboratory mouse), Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Danio rerio, and Rattus norvegicus] along with the GOC have operated as a network of independent, highly collaborative genome resources. In 2016, these six MODs and the GOC joined forces as the Alliance of Genome Resources (the Alliance). By implementing shared programmatic access methods and data-specific web pages with a unified "look and feel," the Alliance is tackling barriers that have limited the ability of researchers to easily compare common data types and annotations across model organisms. To adapt to the rapidly changing landscape for evaluating and funding core data resources, the Alliance is building a modern, extensible, and operationally efficient "knowledge commons" for model organisms using shared, modular infrastructure
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Unbiased Boolean analysis of public gene expression data for cell cycle gene identification.
Cell proliferation is essential for the development and maintenance of all organisms and is dysregulated in cancer. Using synchronized cells progressing through the cell cycle, pioneering microarray studies defined cell cycle genes based on cyclic variation in their expression. However, the concordance of the small number of synchronized cell studies has been limited, leading to discrepancies in definition of the transcriptionally regulated set of cell cycle genes within and between species. Here we present an informatics approach based on Boolean logic to identify cell cycle genes. This approach used the vast array of publicly available gene expression data sets to query similarity to CCNB1, which encodes the cyclin subunit of the Cdk1-cyclin B complex that triggers the G2-to-M transition. In addition to highlighting conservation of cell cycle genes across large evolutionary distances, this approach identified contexts where well-studied genes known to act during the cell cycle are expressed and potentially acting in nondivision contexts. An accessible web platform enables a detailed exploration of the cell cycle gene lists generated using the Boolean logic approach. The methods employed are straightforward to extend to processes other than the cell cycle
Role of the Fractalkine Receptor in CNS Autoimmune Inflammation: New Approach Utilizing a Mouse Model Expressing the Human CX3CR1
Multiple sclerosis (MS), an inflammatory demyelinating disease of the central nervous system (CNS) is the leading cause of non-traumatic neurological disability in young adults. Immune mediated destruction of myelin and oligodendrocytes is considered the primary pathology of MS, but progressive axonal loss is the major cause of neurological disability. In an effort to understand microglia function during CNS inflammation, our laboratory focuses on the fractalkine/CX3CR1 signaling as a regulator of microglia neurotoxicity in various models of neurodegeneration. Fractalkine (FKN) is a transmembrane chemokine expressed in the CNS by neurons and signals through its unique receptor CX3CR1 present in microglia. During experimental autoimmune encephalomyelitis (EAE), CX3CR1 deficiency confers exacerbated disease defined by severe inflammation and neuronal loss. The CX3CR1 human polymorphism I249/M280 present in ∼20% of the population exhibits reduced adhesion for FKN conferring defective signaling whose role in microglia function and influence on neurons during MS remains unsolved. The aim of this study is to assess the effect of weaker signaling through hCX3CR1I249/M280 during EAE. We hypothesize that dysregulated microglial responses due to impaired CX3CR1 signaling enhance neuronal/axonal damage. We generated an animal model replacing the mouse CX3CR1 locus for the hCX3CR1I249/M280 variant. Upon EAE induction, these mice exhibited exacerbated EAE correlating with severe inflammation and neuronal loss. We also observed that mice with aberrant CX3CR1 signaling are unable to produce FKN and ciliary neurotrophic factor during EAE in contrast to wild type mice. Our results provide validation of defective function of the hCX3CR1I249/M280 variant and the foundation to broaden the understanding of microglia dysfunction during neuroinflammation. © 2018 Cardona et al
XenDB: Full length cDNA prediction and cross species mapping in Xenopus laevis
BACKGROUND: Research using the model system Xenopus laevis has provided critical insights into the mechanisms of early vertebrate development and cell biology. Large scale sequencing efforts have provided an increasingly important resource for researchers. To provide full advantage of the available sequence, we have analyzed 350,468 Xenopus laevis Expressed Sequence Tags (ESTs) both to identify full length protein encoding sequences and to develop a unique database system to support comparative approaches between X. laevis and other model systems. DESCRIPTION: Using a suffix array based clustering approach, we have identified 25,971 clusters and 40,877 singleton sequences. Generation of a consensus sequence for each cluster resulted in 31,353 tentative contig and 4,801 singleton sequences. Using both BLASTX and FASTY comparison to five model organisms and the NR protein database, more than 15,000 sequences are predicted to encode full length proteins and these have been matched to publicly available IMAGE clones when available. Each sequence has been compared to the KOG database and ~67% of the sequences have been assigned a putative functional category. Based on sequence homology to mouse and human, putative GO annotations have been determined. CONCLUSION: The results of the analysis have been stored in a publicly available database XenDB . A unique capability of the database is the ability to batch upload cross species queries to identify potential Xenopus homologues and their associated full length clones. Examples are provided including mapping of microarray results and application of 'in silico' analysis. The ability to quickly translate the results of various species into 'Xenopus-centric' information should greatly enhance comparative embryological approaches. Supplementary material can be found at
Cancer-Associated Fibroblasts Neutralize the Anti-tumor Effect of CSF1 Receptor Blockade by Inducing PMN-MDSC Infiltration of Tumors.
Tumor-associated macrophages (TAM) contribute to all aspects of tumor progression. Use of CSF1R inhibitors to target TAM is therapeutically appealing, but has had very limited anti-tumor effects. Here, we have identified the mechanism that limited the effect of CSF1R targeted therapy. We demonstrated that carcinoma-associated fibroblasts (CAF) are major sources of chemokines that recruit granulocytes to tumors. CSF1 produced by tumor cells caused HDAC2-mediated downregulation of granulocyte-specific chemokine expression in CAF, which limited migration of these cells to tumors. Treatment with CSF1R inhibitors disrupted this crosstalk and triggered a profound increase in granulocyte recruitment to tumors. Combining CSF1R inhibitor with a CXCR2 antagonist blocked granulocyte infiltration of tumors and showed strong anti-tumor effects
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A combined computational-experimental approach to define the structural origin of antibody recognition of sialyl-Tn, a tumor-associated carbohydrate antigen.
Anti-carbohydrate monoclonal antibodies (mAbs) hold great promise as cancer therapeutics and diagnostics. However, their specificity can be mixed, and detailed characterization is problematic, because antibody-glycan complexes are challenging to crystallize. Here, we developed a generalizable approach employing high-throughput techniques for characterizing the structure and specificity of such mAbs, and applied it to the mAb TKH2 developed against the tumor-associated carbohydrate antigen sialyl-Tn (STn). The mAb specificity was defined by apparent KD values determined by quantitative glycan microarray screening. Key residues in the antibody combining site were identified by site-directed mutagenesis, and the glycan-antigen contact surface was defined using saturation transfer difference NMR (STD-NMR). These features were then employed as metrics for selecting the optimal 3D-model of the antibody-glycan complex, out of thousands plausible options generated by automated docking and molecular dynamics simulation. STn-specificity was further validated by computationally screening of the selected antibody 3D-model against the human sialyl-Tn-glycome. This computational-experimental approach would allow rational design of potent antibodies targeting carbohydrates
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