The evolutionary history of ferns inferred from 25 low-copy nuclear genes.

UNLABELLED: • PREMISE OF THE STUDY: Understanding fern (monilophyte) phylogeny and its evolutionary timescale is critical for broad investigations of the evolution of land plants, and for providing the point of comparison necessary for studying the evolution of the fern sister group, seed plants. Molecular phylogenetic investigations have revolutionized our understanding of fern phylogeny, however, to date, these studies have relied almost exclusively on plastid data.• METHODS: Here we take a curated phylogenomics approach to infer the first broad fern phylogeny from multiple nuclear loci, by combining broad taxon sampling (73 ferns and 12 outgroup species) with focused character sampling (25 loci comprising 35877 bp), along with rigorous alignment, orthology inference and model selection.• KEY RESULTS: Our phylogeny corroborates some earlier inferences and provides novel insights; in particular, we find strong support for Equisetales as sister to the rest of ferns, Marattiales as sister to leptosporangiate ferns, and Dennstaedtiaceae as sister to the eupolypods. Our divergence-time analyses reveal that divergences among the extant fern orders all occurred prior to ∼200 MYA. Finally, our species-tree inferences are congruent with analyses of concatenated data, but generally with lower support. Those cases where species-tree support values are higher than expected involve relationships that have been supported by smaller plastid datasets, suggesting that deep coalescence may be reducing support from the concatenated nuclear data.• CONCLUSIONS: Our study demonstrates the utility of a curated phylogenomics approach to inferring fern phylogeny, and highlights the need to consider underlying data characteristics, along with data quantity, in phylogenetic studies.

Structured Illumination Microscopy and a Quantitative Image Analysis for the Detection of Positive Margins in a Pre-Clinical Genetically Engineered Mouse Model of Sarcoma.

Intraoperative assessment of surgical margins is critical to ensuring residual tumor does not remain in a patient. Previously, we developed a fluorescence structured illumination microscope (SIM) system with a single-shot field of view (FOV) of 2.1 × 1.6 mm (3.4 mm2) and sub-cellular resolution (4.4 μm). The goal of this study was to test the utility of this technology for the detection of residual disease in a genetically engineered mouse model of sarcoma. Primary soft tissue sarcomas were generated in the hindlimb and after the tumor was surgically removed, the relevant margin was stained with acridine orange (AO), a vital stain that brightly stains cell nuclei and fibrous tissues. The tissues were imaged with the SIM system with the primary goal of visualizing fluorescent features from tumor nuclei. Given the heterogeneity of the background tissue (presence of adipose tissue and muscle), an algorithm known as maximally stable extremal regions (MSER) was optimized and applied to the images to specifically segment nuclear features. A logistic regression model was used to classify a tissue site as positive or negative by calculating area fraction and shape of the segmented features that were present and the resulting receiver operator curve (ROC) was generated by varying the probability threshold. Based on the ROC curves, the model was able to classify tumor and normal tissue with 77% sensitivity and 81% specificity (Youden's index). For an unbiased measure of the model performance, it was applied to a separate validation dataset that resulted in 73% sensitivity and 80% specificity. When this approach was applied to representative whole margins, for a tumor probability threshold of 50%, only 1.2% of all regions from the negative margin exceeded this threshold, while over 14.8% of all regions from the positive margin exceeded this threshold.

Live Imaging of Host-Parasite Interactions in a Zebrafish Infection Model Reveals Cryptococcal Determinants of Virulence and Central Nervous System Invasion.

UNLABELLED: The human fungal pathogen Cryptococcus neoformans is capable of infecting a broad range of hosts, from invertebrates like amoebas and nematodes to standard vertebrate models such as mice and rabbits. Here we have taken advantage of a zebrafish model to investigate host-pathogen interactions of Cryptococcus with the zebrafish innate immune system, which shares a highly conserved framework with that of mammals. Through live-imaging observations and genetic knockdown, we establish that macrophages are the primary immune cells responsible for responding to and containing acute cryptococcal infections. By interrogating survival and cryptococcal burden following infection with a panel of Cryptococcus mutants, we find that virulence factors initially identified as important in causing disease in mice are also necessary for pathogenesis in zebrafish larvae. Live imaging of the cranial blood vessels of infected larvae reveals that C. neoformans is able to penetrate the zebrafish brain following intravenous infection. By studying a C. neoformans FNX1 gene mutant, we find that blood-brain barrier invasion is dependent on a known cryptococcal invasion-promoting pathway previously identified in a murine model of central nervous system invasion. The zebrafish-C. neoformans platform provides a visually and genetically accessible vertebrate model system for cryptococcal pathogenesis with many of the advantages of small invertebrates. This model is well suited for higher-throughput screening of mutants, mechanistic dissection of cryptococcal pathogenesis in live animals, and use in the evaluation of therapeutic agents. IMPORTANCE: Cryptococcus neoformans is an important opportunistic pathogen that is estimated to be responsible for more than 600,000 deaths worldwide annually. Existing mammalian models of cryptococcal pathogenesis are costly, and the analysis of important pathogenic processes such as meningitis is laborious and remains a challenge to visualize. Conversely, although invertebrate models of cryptococcal infection allow high-throughput assays, they fail to replicate the anatomical complexity found in vertebrates and, specifically, cryptococcal stages of disease. Here we have utilized larval zebrafish as a platform that overcomes many of these limitations. We demonstrate that the pathogenesis of C. neoformans infection in zebrafish involves factors identical to those in mammalian and invertebrate infections. We then utilize the live-imaging capacity of zebrafish larvae to follow the progression of cryptococcal infection in real time and establish a relevant model of the critical central nervous system infection phase of disease in a nonmammalian model.

Patient beliefs and behaviors about genomic risk for type 2 diabetes: Implications for prevention

Copyright © Taylor & Francis Group, LLC 2015.Type 2 diabetes is a major health burden in the United States, and population trends suggest this burden will increase. High interest in, and increased availability of, testing for genetic risk of type 2 diabetes presents a new opportunity for reducing type 2 diabetes risk for many patients; however, to date, there is little evidence that genetic testing positively affects type 2 diabetes prevention. Genetic information may not fit patients illness representations, which may reduce the chances of risk-reducing behavior changes. The present study aimed to examine illness representations in a clinical sample who are at risk for type 2 diabetes and interested in genetic testing. The authors used the Common Sense Model to analyze survey responses of 409 patients with type 2 diabetes risk factors. Patients were interested in genetic testing for type 2 diabetes risk and believed in its importance. Most patients believed that genetic factors are important to developing type 2 diabetes (67%), that diet and exercise are effective in preventing type 2 diabetes (95%), and that lifestyle changes are more effective than drugs (86%). Belief in genetic causality was not related to poorer self-reported health behaviors. These results suggest that patients interest in genetic testing for type 2 diabetes might produce a teachable moment that clinicians can use to counsel behavior change.

The IGNITE network: a model for genomic medicine implementation and research.

BACKGROUND: Patients, clinicians, researchers and payers are seeking to understand the value of using genomic information (as reflected by genotyping, sequencing, family history or other data) to inform clinical decision-making. However, challenges exist to widespread clinical implementation of genomic medicine, a prerequisite for developing evidence of its real-world utility. METHODS: To address these challenges, the National Institutes of Health-funded IGNITE (Implementing GeNomics In pracTicE; www.ignite-genomics.org ) Network, comprised of six projects and a coordinating center, was established in 2013 to support the development, investigation and dissemination of genomic medicine practice models that seamlessly integrate genomic data into the electronic health record and that deploy tools for point of care decision making. IGNITE site projects are aligned in their purpose of testing these models, but individual projects vary in scope and design, including exploring genetic markers for disease risk prediction and prevention, developing tools for using family history data, incorporating pharmacogenomic data into clinical care, refining disease diagnosis using sequence-based mutation discovery, and creating novel educational approaches. RESULTS: This paper describes the IGNITE Network and member projects, including network structure, collaborative initiatives, clinical decision support strategies, methods for return of genomic test results, and educational initiatives for patients and providers. Clinical and outcomes data from individual sites and network-wide projects are anticipated to begin being published over the next few years. CONCLUSIONS: The IGNITE Network is an innovative series of projects and pilot demonstrations aiming to enhance translation of validated actionable genomic information into clinical settings and develop and use measures of outcome in response to genome-based clinical interventions using a pragmatic framework to provide early data and proofs of concept on the utility of these interventions. Through these efforts and collaboration with other stakeholders, IGNITE is poised to have a significant impact on the acceleration of genomic information into medical practice.