82 research outputs found
<html>Autologous reconstitution of human cancer and immune system <i>in vivo</i></html>
Get PDFCorrelative studies from checkpoint inhibitor trials have indicated that better understanding of human leukocytic trafficking into the human tumor microenvironment can expedite the translation of future immune-oncologic agents. In order to directly characterize signaling pathways that can regulate human leukocytic trafficking into the tumor, we have developed a completely autologous xenotransplantation method to reconstitute the human tumor immune microenvironment in vivo. We were able to genetically mark the engrafted CD34+ bone marrow cells as well as the tumor cells, and follow the endogenous leukocytic infiltration into the autologous tumor. To investigate human tumor intrinsic factors that can potentially regulate the immune cells in our system, we silenced STAT3 signaling in the tumor compartment. As expected, STAT3 signaling suppression in the tumor compartment in these autologously reconstituted humanized mice showed increased tumor infiltrating lymphocytes and reduction of arginase-1 in the stroma, which were associated with slower tumor growth rate. We also used this novel system to characterize human myeloid suppressor cells as well as to screen novel agents that can alter endogenous leukocytic infiltration into the tumor. Taken together, we present a valuable method to study individualized human tumor microenvironments in vivo without confounding allogeneic responses
Methods for visual mining of genomic and proteomic data atlases
Get PDF<p>Abstract</p> <p>Background</p> <p>As the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.</p> <p>Results</p> <p>This paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.</p> <p>Conclusions</p> <p>The mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.</p
Strong synergy with APR-246 and DNA-damaging drugs in primary cancer cells from patients with TP53 mutant High-Grade Serous ovarian cancer
Full text linkRoles of Electrostatics and Conformation in Protein-Crystal Interactions
Get PDFIn vitro studies have shown that the phosphoprotein osteopontin (OPN) inhibits the nucleation and growth of hydroxyapatite (HA) and other biominerals. In vivo, OPN is believed to prevent the calcification of soft tissues. However, the nature of the interaction between OPN and HA is not understood. In the computational part of the present study, we used molecular dynamics simulations to predict the adsorption of 19 peptides, each 16 amino acids long and collectively covering the entire sequence of OPN, to the {100} face of HA. This analysis showed that there is an inverse relationship between predicted strength of adsorption and peptide isoelectric point (P<0.0001). Analysis of the OPN sequence by PONDR (Predictor of Naturally Disordered Regions) indicated that OPN sequences predicted to adsorb well to HA are highly disordered. In the experimental part of the study, we synthesized phosphorylated and non-phosphorylated peptides corresponding to OPN sequences 65–80 (pSHDHMDDDDDDDDDGD) and 220–235 (pSHEpSTEQSDAIDpSAEK). In agreement with the PONDR analysis, these were shown by circular dichroism spectroscopy to be largely disordered. A constant-composition/seeded growth assay was used to assess the HA-inhibiting potencies of the synthetic peptides. The phosphorylated versions of OPN65-80 (IC50 = 1.93 µg/ml) and OPN220-235 (IC50 = 1.48 µg/ml) are potent inhibitors of HA growth, as is the nonphosphorylated version of OPN65-80 (IC50 = 2.97 µg/ml); the nonphosphorylated version of OPN220-235 has no measurable inhibitory activity. These findings suggest that the adsorption of acidic proteins to Ca2+-rich crystal faces of biominerals is governed by electrostatics and is facilitated by conformational flexibility of the polypeptide chain
Cementomimetics—constructing a cementum-like biomineralized microlayer via amelogenin-derived peptides
Get PDFThis is the published version. Copyright 2012 Nature Publishing GroupCementum is the outer-, mineralized-tissue covering the tooth root and an essential part of the system of periodontal tissue that anchors the tooth to the bone. Periodontal disease results from the destructive behavior of the host elicited by an infectious biofilm adhering to the tooth root and left untreated, may lead to tooth loss. We describe a novel protocol for identifying peptide sequences from native proteins with the potential to repair damaged dental tissues by controlling hydroxyapatite biomineralization. Using amelogenin as a case study and a bioinformatics scoring matrix, we identified regions within amelogenin that are shared with a set of hydroxyapatite-binding peptides (HABPs) previously selected by phage display. One 22-amino acid long peptide regions referred to as amelogenin-derived peptide 5 (ADP5) was shown to facilitate cell-free formation of a cementum-like hydroxyapatite mineral layer on demineralized human root dentin that, in turn, supported attachment of periodontal ligament cells in vitro. Our findings have several implications in peptide-assisted mineral formation that mimic biomineralization. By further elaborating the mechanism for protein control over the biomineral formed, we afford new insights into the evolution of protein–mineral interactions. By exploiting small peptide domains of native proteins, our understanding of structure–function relationships of biomineralizing proteins can be extended and these peptides can be utilized to engineer mineral formation. Finally, the cementomimetic layer formed by ADP5 has the potential clinical application to repair diseased root surfaces so as to promote the regeneration of periodontal tissues and thereby reduce the morbidity associated with tooth loss
Mapping genetic variations to three- dimensional protein structures to enhance variant interpretation: a proposed framework
Get PDFThe 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
A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening
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