87 research outputs found

    Urinary Protein Profiles in a Rat Model for Diabetic Complications

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    Diabetes mellitus is estimated to affect ∼24 million people in the United States and more than 150 million people worldwide. There are numerous end organ complications of diabetes, the onset of which can be delayed by early diagnosis and treatment. Although assays for diabetes are well founded, tests for its complications lack sufficient specificity and sensitivity to adequately guide these treatment options. In our study, we employed a streptozotocin- induced rat model of diabetes to determine changes in urinary protein profiles that occur during the initial response to the attendant hyperglycemia (e.g. the first two months) with the goal of developing a reliable and reproducible method of analyzing multiple urine samples as well as providing clues to early markers of disease progression. After filtration and buffer exchange, urinary proteins were digested with a specific protease, and the relative amounts of several thousand peptides were compared across rat urine samples representing various times after administration of drug or sham control. Extensive data analysis, including imputation of missing values and normalization of all data was followed by ANOVA analysis to discover peptides that were significantly changing as a function of time, treatment and interaction of the two variables. The data demonstrated significant differences in protein abundance in urine before observable pathophysiological changes occur in this animal model and as function of the measured variables. These included decreases in relative abundance of major urinary protein precursor and increases in pro-alpha collagen, the expression of which is known to be regulated by circulating levels of insulin and/or glucose. Peptides from these proteins represent potential biomarkers, which can be used to stage urogenital complications from diabetes. The expression changes of a pro-alpha 1 collagen peptide was also confirmed via selected reaction monitoring

    Alternate wiring of a KNOXI genetic network underlies differences in leaf development of A. thaliana and C. hirsuta

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    Two interrelated problems in biology are understanding the regulatory logic and predictability of morphological evolution. Here, we studied these problems by comparing Arabidopsis thaliana, which has simple leaves, and its relative, Cardamine hirsuta, which has dissected leaves comprising leaflets. By transferring genes between the two species, we provide evidence for an inverse relationship between the pleiotropy of SHOOTMERISTEMLESS (STM) and BREVIPEDICELLUS (BP) homeobox genes and their ability to modify leaf form. We further show that cis-regulatory divergence of BP results in two alternative configurations of the genetic networks controlling leaf development. In C. hirsuta, ChBP is repressed by the microRNA164A (MIR164A)/ChCUP-SHAPED COTYLEDON (ChCUC) module and ChASYMMETRIC LEAVES1 (ChAS1), thus creating cross-talk between MIR164A/CUC and AS1 that does not occur in A. thaliana. These different genetic architectures lead to divergent interactions of network components and growth regulation in each species. We suggest that certain regulatory genes with low pleiotropy are predisposed to readily integrate into or disengage from conserved genetic networks influencing organ geometry, thus rapidly altering their properties and contributing to morphological divergence

    DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization

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    <p>Abstract</p> <p>Background</p> <p>High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths.</p> <p>Results</p> <p>We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called D<smcaps>A</smcaps>D<smcaps>A</smcaps>, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that D<smcaps>A</smcaps>D<smcaps>A</smcaps> outperforms existing methods in prioritizing candidate disease genes.</p> <p>Conclusions</p> <p>These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. D<smcaps>A</smcaps>D<smcaps>A</smcaps> is implemented in Matlab and is freely available at <url>http://compbio.case.edu/dada/</url>.</p

    Educational paper: Abusive Head Trauma Part I. Clinical aspects

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    Abusive Head Trauma (AHT) refers to the combination of findings formerly described as shaken baby syndrome. Although these findings can be caused by shaking, it has become clear that in many cases there may have been impact trauma as well. Therefore a less specific term has been adopted by the American Academy of Pediatrics. AHT is a relatively common cause of childhood neurotrauma with an estimated incidence of 14–40 cases per 100,000 children under the age of 1 year. About 15–23% of these children die within hours or days after the incident. Studies among AHT survivors demonstrate that approximately one-third of the children are severely disabled, one-third of them are moderately disabled and one-third have no or only mild symptoms. Other publications suggest that neurological problems can occur after a symptom-free interval and that half of these children have IQs below the 10th percentile. Clinical findings are depending on the definitions used, but AHT should be considered in all children with neurological signs and symptoms especially if no or only mild trauma is described. Subdural haematomas are the most reported finding. The only feature that has been identified discriminating AHT from accidental injury is apnoea. Conclusion: AHT should be approached with a structured approach, as in any other (potentially lethal) disease. The clinician can only establish this diagnosis if he/she has knowledge of the signs and symptoms of AHT, risk factors, the differential diagnosis and which additional investigations to perform, the more so since parents seldom will describe the true state of affairs spontaneously

    Large-scale mapping of human protein–protein interactions by mass spectrometry

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    Mapping protein–protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein–protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24 540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein–protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations

    Crop Updates 2007 - Farming Systems

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    This session covers forty papers from different authors: 1. Quality Assurance and industry stewardship, David Jeffries, Better Farm IQ Manager, Cooperative Bulk Handling 2. Sothis: Trifolium dasyurum (Eastern Star clover), A. Loi, B.J. Nutt and C.K. Revell, Department of Agriculture and Food 3. Poor performing patches of the paddock – to ameliorate or live with low yield? Yvette Oliver1, Michael Robertson1, Bill Bowden2, Kit Leake3and Ashley Bonser3, CSIRO Sustainable Ecosystems1, Department of Food and Agriculture2, Kellerberrin Farmer3 4. What evidence is there that PA can pay? Michael Robertson, CSIRO Floreat, Ian Maling, SilverFox Solutions and Bindi Isbister, Department of Agriculture and Food 5.The journey is great, but does PA pay? Garren Knell, ConsultAg; Alison Slade, Department of Agriculture and Food, CFIG 6. 2007 Seasonal outlook, David Stephens and Michael Meuleners, Department of Agriculture and Food 7. Towards building farmer capacity to better manage climate risk, David Beard and Nicolyn Short, Department of Agriculture and Food 8. A NAR farmers view of his farming system in 2015, Rob Grima, Department of Agriculture and Food 9. Biofuels opportunities in Australia, Ingrid Richardson, Food and Agribusiness Research, Rabobank 10. The groundwater depth on the hydrological benefits of lucerne and the subsequent recharge values, Ruhi Ferdowsian1and Geoff Bee2; 1Department of Agriculture and Food, 2Landholder, Laurinya, Jerramungup 11. Subsoil constraints to crop production in the high rainfall zone of Western Australia, Daniel Evans1, Bob Gilkes1, Senthold Asseng2and Jim Dixon3; 1University of Western Australia, 2CSIRO Plant Industry, 3Department of Agriculture and Food 12. Prospects for lucerne in the WA wheatbelt, Michael Robertson, CSIRO Floreat, Felicity Byrne and Mike Ewing, CRC for Plant-Based Management of Dryland Salinity, Dennis van Gool, Department of Agriculture and Food 13. Nitrous oxide emissions from a cropped soil in the Western Australian grainbelt, Louise Barton1, Ralf Kiese2, David Gatter3, Klaus Butterbach-Bahl2, Renee Buck1, Christoph Hinz1and Daniel Murphy1,1School of Earth and Geographical Sciences, The University of Western Australia, 2Institute for Meteorology and Climate Research, Atmospheric Environmental Research, Garmisch-Partenkirchen, Germany, 3The Department of Agriculture and Food 14. Managing seasonal risk is an important part of farm management but is highly complex and therefore needs a ‘horses for courses’ approach, Cameron Weeks, Planfarm / Mingenew-Irwin Group, Dr Michael Robertson, Dr Yvette Oliver, CSIRO Sustainable Ecosystems and Dr Meredith Fairbanks, Department of Agriculture and Food 15. Novel use application of clopyralid in lupins, John Peirce, and Brad Rayner Department of Agriculture and Food 16. Long season wheat on the South Coast – Feed and grain in a dry year – a 2006 case study, Sandy White, Department of Agriculture and Food 17. Wheat yield response to potassium and the residual value of PKS fertiliser drilled at different depths, Paul Damon1, Bill Bowden2, Qifu Ma1 and Zed Rengel1; Faculty of Natural and Agricultural Sciences, The University of Western Australia1, Department of Agriculture and Food2 18. Saltbush as a sponge for summer rain, Ed Barrett-Lennard and Meir Altman, Department of Agriculture and Food and CRC for Plant-based Management of Dryland Salinity 19. Building strong working relationships between grower groups and their industry partners, Tracey M. Gianatti, Grower Group Alliance 20. To graze or not to graze – the question of tactical grazing of cereal crops, Lindsay Bell and Michael Robertson, CSIRO Sustainable Ecosystems 21. Can legume pastures and sheep replace lupins? Ben Webb and Caroline Peek, Department of Agriculture and Food 22. EverGraze – livestock and perennial pasture performance during a drought year, Paul Sanford, Department of Agriculture and Food, and CRC for Plant-based Management of Dryland Salinity 23. Crop survival in challenging times, Paul Blackwell1, Glen Riethmuller1, Darshan Sharma1and Mike Collins21Department of Agriculture and Food, 2Okura Plantations, Kirikiri New Zealand 24. Soil health constraints to production potential – a precision guided project, Frank D’Emden, and David Hall, Department of Agriculture and Food 25. A review of pest and disease occurrence in 2006, Mangano, G.P. and Severtson, D.L., Department of Agriculture and Food 26. e-weed – an information resource on seasonal weed management issues, Vanessa Stewart and Julie Roche, Department of Agriculture and Food 27. Review of Pesticide Legislation and Policies in Western Australia, Peter Rutherford, BSc (Agric.), Pesticide Legislation Review, Office of the Chief Medical Adviser, WA Department of Health 28. Future wheat yields in the West Australian wheatbelt, Imma Farré and Ian Foster, Department of Agriculture and Food, Stephen Charles, CSIRO Land and Water 29. Organic matter in WA arable soils: What’s active and what’s not, Frances Hoyle, Department of Agriculture and Food, Australia and Daniel Murphy, UWA 30. Soil quality indicators in Western Australian farming systems, D.V. Murphy1, N. Milton1, M. Osman1, F.C. Hoyle2, L.K Abbott1, W.R. Cookson1and S. Darmawanto1; 1UWA, 2Department of Agriculture and Food 31. Impact of stubble on input efficiencies, Geoff Anderson, formerly employed by Department of Agriculture and Food 32. Mixed farming vs All crop – true profit, not just gross margins, Rob Sands and David McCarthy, FARMANCO Management Consultants, Western Australia 33. Evaluation of Local Farmer Group Network – group leaders’ surveys 2005 and 2006, Paul Carmody, Local Farmer Group Network, Network Coordinator, UWA 34. Seeding rate and nitrogen application and timing effects in wheat, J. Russell, Department of Agriculture and Food, J. Eyres, G. Fosbery and A. Roe, ConsultAg, Northam 35. Foliar fungicide application and disease control in barley, J. Russell, Department of Agriculture and Food, J. Eyres, G. Fosbery and A. Roe, ConsultAg, Northam 36. Brown manuring effects on a following wheat crop in the central wheatbelt, , J. Russell, Department of Agriculture and Food, J. Eyres, G. Fosbery and A. Roe, ConsultAg, Northam 37. Management of annual pastures in mixed farming systems – transition from a dry season, Dr Clinton Revell and Dr Phil Nichols; Department of Agriculture and Food 38. The value of new annual pastures in mixed farm businesses of the wheatbelt, Dr Clinton Revell1, Mr Andrew Bathgate2and Dr Phil Nichols1; 1Department of Agriculture and Food, 2Farming Systems Analysis Service, Albany 39. The influence of winter SOI and Indian Ocean SST on WA winter rainfall, Meredith Fairbanks and Ian Foster, Department of Agriculture and Food 40. Market outlook – Grains, Anne Wilkins, Market Analyst, Grains, Department of Agriculture and Foo

    Adjunctive rifampicin for Staphylococcus aureus bacteraemia (ARREST): a multicentre, randomised, double-blind, placebo-controlled trial.

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    BACKGROUND: Staphylococcus aureus bacteraemia is a common cause of severe community-acquired and hospital-acquired infection worldwide. We tested the hypothesis that adjunctive rifampicin would reduce bacteriologically confirmed treatment failure or disease recurrence, or death, by enhancing early S aureus killing, sterilising infected foci and blood faster, and reducing risks of dissemination and metastatic infection. METHODS: In this multicentre, randomised, double-blind, placebo-controlled trial, adults (≥18 years) with S aureus bacteraemia who had received ≤96 h of active antibiotic therapy were recruited from 29 UK hospitals. Patients were randomly assigned (1:1) via a computer-generated sequential randomisation list to receive 2 weeks of adjunctive rifampicin (600 mg or 900 mg per day according to weight, oral or intravenous) versus identical placebo, together with standard antibiotic therapy. Randomisation was stratified by centre. Patients, investigators, and those caring for the patients were masked to group allocation. The primary outcome was time to bacteriologically confirmed treatment failure or disease recurrence, or death (all-cause), from randomisation to 12 weeks, adjudicated by an independent review committee masked to the treatment. Analysis was intention to treat. This trial was registered, number ISRCTN37666216, and is closed to new participants. FINDINGS: Between Dec 10, 2012, and Oct 25, 2016, 758 eligible participants were randomly assigned: 370 to rifampicin and 388 to placebo. 485 (64%) participants had community-acquired S aureus infections, and 132 (17%) had nosocomial S aureus infections. 47 (6%) had meticillin-resistant infections. 301 (40%) participants had an initial deep infection focus. Standard antibiotics were given for 29 (IQR 18-45) days; 619 (82%) participants received flucloxacillin. By week 12, 62 (17%) of participants who received rifampicin versus 71 (18%) who received placebo experienced treatment failure or disease recurrence, or died (absolute risk difference -1·4%, 95% CI -7·0 to 4·3; hazard ratio 0·96, 0·68-1·35, p=0·81). From randomisation to 12 weeks, no evidence of differences in serious (p=0·17) or grade 3-4 (p=0·36) adverse events were observed; however, 63 (17%) participants in the rifampicin group versus 39 (10%) in the placebo group had antibiotic or trial drug-modifying adverse events (p=0·004), and 24 (6%) versus six (2%) had drug interactions (p=0·0005). INTERPRETATION: Adjunctive rifampicin provided no overall benefit over standard antibiotic therapy in adults with S aureus bacteraemia. FUNDING: UK National Institute for Health Research Health Technology Assessment

    A protein interaction between beta-catenin and Dnmt1 regulates Wnt Signaling and DNA methylation in colorectal cancer cells

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    Aberrant activation of the Wnt signaling pathway is an important step in the initiation and progression of tumor development in diverse cancers. The central effector of canonical Wnt signaling, beta-catenin (CTNNB1), is a multifunctional protein, and has been extensively studied with respect to its roles in cell-cell adhesion and in regulation of Wnt-driven transcription. Here, a novel mass spectrometry-based proteomics technique in colorectal cancer cells expressing stabilized beta-catenin, was used to identify a protein-protein interaction between beta-catenin and DNA methyltransferase I (Dnmt1) protein, the primary regulator of DNA methylation patterns in mammalian cells. Dnmt1 and beta-catenin strongly co- localized in the nuclei of colorectal cancer cells, and the interaction is mediated by the central domain of the Dnmt1 protein. Dnmt1 protein abundance is dependent upon the levels of beta-catenin, and is increased in cells expressing stabilized mutant beta-catenin. Conversely, the Dnmt1 regulates the levels of nuclear beta-catenin and beta-catenin/TCF driven transcription. In addition, lysine-specific demethylase 1 (LSD1/KDM1A), a regulator of DNMT1 stability, was identified as a component of the Dnmt1/beta-catenin protein complex and perturbation of the Dnmt1/beta-catenin interaction altered DNA methylation. In summary, a functional protein-protein interaction was identified between two critically important oncoproteins, in turn revealing a link between Wnt signaling and downstream nuclear functions mediated by Dnmt1. Implications: Two critical oncoproteins, Dnmt1 and beta-catenin mutually regulate one another's levels and activities in colorectal cancer cells

    Comparative analysis of the Arabidopsis and rice expressed sequence tag (EST) sets

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    Large numbers of expressed sequence tags (ESTs) have now been generated from a variety of model organisms. In plants, substantial collections of ESTs are available for Arabidopsis and rice, in each case representing significant proportions of the estimated total numbers of genes. Large-scale comparisons of Arabidopsis and rice sequences are especially interesting due to the fact that these two species are representatives of the two subclasses of the flowering plants (Dicotyledonae and Monocotyledonae, respectively). Here we present the results of systematic analysis of the Arabidopsis and rice EST sets. Non-redundant sets of sequences from Arabidopsis and rice were first separately derived and then combined so that gene families in common between the two species could be identified. Our results show that 58% of non-singleton ESTs are derived from genes in gene families common to the two species. These gene families constitute the basis of a core set of higher plant genes

    The bait compatibility index: computational bait selection for interaction proteomics experiments

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    Protein interaction network maps have been generated for multiple species, making use of large-scale methods such as yeast two-hybrid (Y2H) and affinity purification mass spectrometry (AP-MS). These methods take fundamentally different approaches toward characterizing protein networks, and the resulting data sets provide complementary views of the protein interactome. The specific determinants of the outcome of Y2H and AP-MS experiments, in terms of detection of interacting proteins are, however, poorly understood. Here we show that a statistical model built using sequence- and annotation- based features of bait proteins is able to identify bait features that are significant determinants of the outcome of interaction proteomics experiments. We show that bait features are able to explain in part the disparities observed between Y2H and AP-MS constructed networks and can be used to derive the “bait compatibility index”, a numeric score that assesses the compatibility of bait proteins with each technology. Aside from understanding the bias and limitations of interaction proteomics, our approach provides a rational, data-driven method for prioritization of baits for interaction proteomics experiments, an essential requirement for future proteome-wide applications of these technologies
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