Development of a bacteriophage-based biopesticide for fire blight

Fire blight is an economically important disease of apples and pears that is caused by the bacterium Erwinia amylovora. Control of the disease depends on limiting primaly blosson1 infection in the spring, and rapidly removing infected tissue. The possibility of using phages to control E.amylovora populations has been suggested, but previous studies have. failed to show high treatment efficacies. This work describes the development of a phage-based biopesticide that controls E. amylovora populations under field conditions, and significantly reduces the incidence of fire blight. This work reports the first use ofPantoea agglomerans, a non-pathogenic relative ofE. amylovora, as a carrier for E. amylovora.phages. Its role is to support a replicating population of these phages on blossom surfaces during the period when the flowers are most susceptible to infection. Seven phages and one carrier isolate were selected for field trials from existing collections of 56 E. amylovora phages and 249 epiphytic orchard bacteria. Selection of the . /' phages and carrier was based on characteristics relevant to the production and field perfonnance of a biopesticide: host range, genetic diversity, growth under the conditions of large-scale production, and the ability to prevent E. amylovora from infecting pear blossoms. In planta assays showed that both the phages and the carrier make significant contributions to reducirig the development of fire blight symptoms in pear blossoms. Field-scale phage production and purification methods were developed based on the growth characteristics of the phages and bacteria in liquid culture, and on the survival of phages in various liquid media. Six of twelve phage-carrier biopesticide treatments caused statistically signiflcant reductions in disease incidence during orchard trials. Multiplex real-time PCR was used to simultaneously monitor the phage, carrier, and pathogen populations over the course of selected treatments. In all cases. the observed population dynamics of the biocontrol agents and the pathogen were consistent with the success or failure of each treatment to control disease incidence. In treatments exhibiting a significantly reduced incidel1ce of fire blight, the average blossom population ofE.amylovora had been reduced to pre-experiment epiphytic levels. In successful treatments the phages grew on the P. agglomerans carrier for 2 to 3 d after treatment application. The phages then grew preferentially on the pathogen, once it was introduced into this blossom ecosystem. The efficacy of the successful phage-based treatnlents was statistically similar to that of streptomycin, which is the most effective bactericide currently available for fire blight prevention. The in planta behaviour ofE. amylovora was compared to that ofErwinia pyrifoliae, a closely related species that causes fire blight-like synlptoms on pears in southeast Asia. Duplex real-time PCR was used to monitor the population dynamics of both species on single blossonls. E. amylovora exhibited a greater competitive fitness on Bartlett pear blossoms than E. pyrifoliae. The genome ofErwinia phage Ea21-4 was sequenced and annotated. Most of the 8-4.7 kB genome is substantially different from previously described sequences, though some regions are notably similar to Salmonella phage Felix 01 . Putative functions were assigned to approximately 30% of the predicted open reading frames based on amino acid sequence comparisons and N-terminal sequencing of structural proteins

Near-infrared fluorescence imaging as an alternative to bioluminescent bacteria to monitor biomaterial-associated infections

Biomaterial-associated infection is one of the most common complications related with the implantation of any biomedical device. Several in vivo imaging platforms have emerged as powerful diagnostic tools to longitudinally monitor biomaterial-associated infections in small animal models. In this study, we directly compared two imaging approaches: bacteria engineered to produce luciferase to generate bioluminescence and reactive oxygen species (ROS) imaging of the inflammatory response associated with the infected implant. We performed longitudinal imaging of bioluminescence associated with bacteria strains expressing plasmid-integrated luciferase driven by different promoters or a strain with the luciferase gene integrated into the chromosome. These luminescent strains provided adequate signal for acute (0–4 days) monitoring of the infection, but the bioluminescence signal decreased over time and leveled off by 7 days post-implantation. This loss in bioluminescence signal was attributed to changes in the metabolic activity of the bacteria. In contrast, near-infrared fluorescence imaging of ROS associated with inflammation to the implant provided sensitive and dose-dependent signals of biomaterialassociated bacteria. ROS imaging exhibited higher sensitivity than the bioluminescence imaging and was independent of the bacteria strain. Near-infrared fluorescence imaging of inflammatory responses represents a powerful alternative to bioluminescence imaging for monitoring biomaterial-associated bacterial infections.This work was supported by the Ministerio of Economía y Competitividad (BIO2010-21049, 201120E092), the U.S.A. National Institutes of Health grant R21 AI094624 (A.J.G.), the Georgia Tech/Emory Center for the Engineering of Living Tissues, the Atlanta Clinical and Translational Science Institute under PHS Grant UL RR025008 from the Clinical and Translational Science Award Program

Environment Orientation : a structured simulation approach for agent-based complex systems

Complex systems are collections of independent agents interacting with each other and with their environment to produce emergent behaviour. Agent-based computer simulation is one of the main ways of studying complex systems. A naive approach to such simulation can fare poorly, due to large communication overhead, and due to the scope for deadlock between the interacting agents sharing a computational platform. Agent interaction can instead be considered entirely from the point of view of the environment(s) within which the agents interact. Structuring a simulation using such Environment Orientation leads to a simulation that reduces communication overhead, that is effectively deadlock-free, and yet still behaves in the manner required. Additionally the Environment Orientation architecture eases the development of more sophisticated large-scale simulations, with multiple kinds of complex agents, situated in and interacting with multiple kinds of environments. We describe the Environment Orientation simulation architecture. We report on a number of experiments that demonstrate the effectiveness of the Environment Orientation approach: a simple flocking system, a flocking system with multiple sensory environments, and a flocking system in an external environment

BGEM: An In Situ Hybridization Database of Gene Expression in the Embryonic and Adult Mouse Nervous System

This article describes an open-access gene expression database analyzed for more than 2,000 genes on mouse nervous system tissue in the coronal, sagittal, and transverse orientation representing multiple developmental ages

Cancer Yield of Mammography, MR, and US in High-Risk Women: Prospective Multi-Institution Breast Cancer Screening Study

PURPOSE: To prospectively determine cancer yield, callback and biopsy rates, and positive predictive value (PPV) of mammography, magnetic resonance (MR) imaging, and ultrasonography (US) in women at high risk for breast cancer. MATERIALS AND METHODS: The study was approved by the institutional review board and was HIPAA compliant, and informed consent was obtained. We conducted a prospective pilot study of screening mammography, MR, and US in asymptomatic women 25 years of age or older who were genetically at high risk, defined as BRCA1/BRCA2 carriers or with at least a 20% probability of carrying a BRCA1/BRCA2 mutation. All imaging modalities were performed within 90 days of each other. Data were analyzed by using exact confidence intervals (CIs) and the McNemar test. RESULTS: A total of 195 women were enrolled in this study over a 6-month period, and 171 completed all study examinations (mammography, US, and MR). Average age of the 171 participants was 46 years +/- 10.2 (standard deviation). Sixteen biopsies were performed and six cancers were detected, for an overall 3.5% cancer yield. MR enabled detection of all six cancers; mammography, two; and US, one. The diagnostic yields for each test were 3.5% for MR, 0.6% for US, and 1.2% for mammography. MR, US, and mammography findings prompted biopsy in 8.2%, 2.3%, and 2.3% of patients, respectively. None of the pairwise comparisons were statistically significant. The PPV of biopsies performed as a result of MR was 43%. CONCLUSION: Screening MR imaging had a higher biopsy rate but helped detect more cancers than either mammography or US. US had the highest false-negative rate compared with mammography and MR, enabling detection of only one in six cancers in high-risk women

Cost-effectiveness of MRI compared to mammography for breast cancer screening in a high risk population

<p>Abstract</p> <p>Background</p> <p>Breast magnetic resonance imaging (MRI) is a sensitive method of breast imaging virtually uninfluenced by breast density. Because of the improved sensitivity, breast MRI is increasingly being used for detection of breast cancer among high risk young women. However, the specificity of breast MRI is variable and costs are high. The purpose of this study was to determine if breast MRI is a cost-effective approach for the detection of breast cancer among young women at high risk.</p> <p>Methods</p> <p>A Markov model was created to compare annual breast cancer screening over 25 years with either breast MRI or mammography among young women at high risk. Data from published studies provided probabilities for the model including sensitivity and specificity of each screening strategy. Costs were based on Medicare reimbursement rates for hospital and physician services while medication costs were obtained from the Federal Supply Scale. Utilities from the literature were applied to each health outcome in the model including a disutility for the temporary health state following breast biopsy for a false positive test result. All costs and benefits were discounted at 5% per year. The analysis was performed from the payer perspective with results reported in 2006 U.S. dollars. Univariate and probabilistic sensitivity analyses addressed uncertainty in all model parameters.</p> <p>Results</p> <p>Breast MRI provided 14.1 discounted quality-adjusted life-years (QALYs) at a discounted cost of $18,167 while mammography provided 14.0 QALYs at a cost of$4,760 over 25 years of screening. The incremental cost-effectiveness ratio of breast MRI compared to mammography was $179,599/QALY. In univariate analysis, breast MRI screening became <$50,000/QALY when the cost of the MRI was < $315. In the probabilistic sensitivity analysis, MRI screening produced a net health benefit of -0.202 QALYs (95$50,000/QALY. Breast MRI screening was superior in 0%, < $50,000/QALY in 22$50,000/QALY in 34%, and inferior in 44% of trials.</p> <p>Conclusion</p> <p>Although breast MRI may provide health benefits when compared to mammographic screening for some high risk women, it does not appear to be cost-effective even at willingness to pay thresholds above $120,000/QALY.</p

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment