Reconstruction of Network Evolutionary History from Extant Network Topology and Duplication History

Genome-wide protein-protein interaction (PPI) data are readily available thanks to recent breakthroughs in biotechnology. However, PPI networks of extant organisms are only snapshots of the network evolution. How to infer the whole evolution history becomes a challenging problem in computational biology. In this paper, we present a likelihood-based approach to inferring network evolution history from the topology of PPI networks and the duplication relationship among the paralogs. Simulations show that our approach outperforms the existing ones in terms of the accuracy of reconstruction. Moreover, the growth parameters of several real PPI networks estimated by our method are more consistent with the ones predicted in literature.Comment: 15 pages, 5 figures, submitted to ISBRA 201

Viability Assessment in Liver Transplantation—What Is the Impact of Dynamic Organ Preservation?

Based on the continuous increase of donor risk, with a majority of organs classified as marginal, quality assessment and prediction of liver function is of utmost importance. This is also caused by the notoriously lack of effective replacement of a failing liver by a device or intensive care treatment. While various parameters of liver function and injury are well-known from clinical practice, the majority of specific tests require prolonged diagnostic time and are more difficult to assess ex situ. In addition, viability assessment of procured organs needs time, because the development of the full picture of cellular injury and the initiation of repair processes depends on metabolic active tissue and reoxygenation with full blood over several hours or days. Measuring injury during cold storage preservation is therefore unlikely to predict the viability after transplantation. In contrast, dynamic organ preservation strategies offer a great opportunity to assess organs before implantation through analysis of recirculating perfusates, bile and perfused liver tissue. Accordingly, several parameters targeting hepatocyte or cholangiocyte function or metabolism have been recently suggested as potential viability tests before organ transplantation. We summarize here a current status of respective machine perfusion tests, and report their clinical relevance

Impact of dual mTORC1/2 mTOR kinase inhibitor AZD8055 on acquired endocrine resistance in breast cancer in vitro

Introduction: Upregulation of PI3K/Akt/mTOR signalling in endocrine-resistant breast cancer (BC) has identified mTOR as an attractive target alongside anti-hormones to control resistance. RAD001 (everolimus/Afinitor®), an allosteric mTOR inhibitor, is proving valuable in this setting; however, some patients are inherently refractory or relapse during treatment requiring alternative strategies. Here we evaluate the potential for novel dual mTORC1/2 mTOR kinase inhibitors, exemplified by AZD8055, by comparison with RAD001 in ER + endocrine resistant BC cells. Methods: In vitro models of tamoxifen (TamR) or oestrogen deprivation resistance (MCF7-X) were treated with RAD001 or AZD8055 alone or combined with anti-hormone fulvestrant. Endpoints included growth, cell proliferation (Ki67), viability and migration, with PI3K/AKT/mTOR signalling impact monitored by Western blotting. Potential ER cross-talk was investigated by immunocytochemistry and RT-PCR. Results: RAD001 was a poor growth inhibitor of MCF7-derived TamR and MCF7-X cells (IC50 ≥1 μM), rapidly inhibiting mTORC1 but not mTORC2/AKT signalling. In contrast AZD8055, which rapidly inhibited both mTORC1 and mTORC2/AKT activity, was a highly effective (P <0.001) growth inhibitor of TamR (IC50 18 nM) and MCF7-X (IC50 24 nM), and of a further T47D-derived tamoxifen resistant model T47D-tamR (IC50 19 nM). AZD8055 significantly (P <0.05) inhibited resistant cell proliferation, increased cell death and reduced migration. Furthermore, dual treatment of TamR or MCF7-X cells with AZD8055 plus fulvestrant provided superior control of resistant growth versus either agent alone (P <0.05). Co-treating with AZD8055 alongside tamoxifen (P <0.01) or oestrogen deprivation (P <0.05) also effectively inhibited endocrine responsive MCF-7 cells. Although AZD8055 inhibited oestrogen receptor (ER) ser167 phosphorylation in TamR and MCF7-X, it had no effect on ER ser118 activity or expression of several ER-regulated genes, suggesting the mTOR kinase inhibitor impact was largely ER-independent. The capacity of AZD8055 for ER-independent activity was further evidenced by growth inhibition (IC5018 and 20 nM) of two acquired fulvestrant resistant models lacking ER. Conclusions: This is the first report demonstrating dual mTORC1/2 mTOR kinase inhibitors have potential to control acquired endocrine resistant BC, even under conditions where everolimus fails. Such inhibitors may prove of particular benefit when used alongside anti-hormonal treatment as second-line therapy in endocrine resistant disease, and also potentially alongside anti-hormones during the earlier endocrine responsive phase to hinder development of resistance

Oseltamivir in seasonal influenza: cumulative experience in low- and high-risk patients

Seasonal influenza viruses cause annual disease epidemics that affect individuals at low and high risk for secondary illnesses. Influenza vaccines are widely used in high-risk patients to prevent infection, but the protection afforded varies by population; uptake is also limited in some groups. Antiviral drugs for influenza are now readily available. Oseltamivir is the most widely used antiviral for the treatment and prophylaxis of seasonal influenza, and its efficacy and safety are now well established in a variety of populations. In addition to decreasing the severity and duration of the symptoms of influenza, clinical and epidemiological studies demonstrate that oseltamivir significantly reduces the frequency of secondary illnesses and exacerbation of underlying conditions; survival is also significantly improved in seriously ill patients who are hospitalized with severe influenza. Resistant viruses are isolated with a low frequency during oseltamivir treatment (0.33% in adults and 4.0% in children among almost 2000 oseltamivir-treated patients enrolled onto Roche-sponsored clinical trials of oseltamivir treatment during the oseltamivir development programme). However, an oseltamivir-resistant influenza A (H1N1) virus emerged in Europe during the 2007–08 season and circulated in the southern and northern hemispheres in 2008–09. No link with oseltamivir usage could be detected, and the clinical impact of these viruses was limited. Oseltamivir-susceptible pandemic (H1N1) 2009 viruses now predominate in many countries. Oseltamivir is generally well tolerated, with a similar adverse event profile to placebo

Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure

TREEPLAN� - A Genetic Evaluation System for Forest Trees

The TREEPLAN� genetic evaluation system is designed specifically for the efficient and accurate prediction of breeding and other genetic values in trees. TREEPLAN� uses the preferred statistical method of best linear unbiased prediction (BLUP) using an individual tree additive genetic effect. Although BLUP methods are well developed theoretically, other software is suitable only for breeding value estimation and prediction on small and/or highly structured (balanced) data sets. Packages such as ASREML and SAS have hardware and software limitations that make them unsuitable for routine prediction on large data sets with complex pedigree structures and overlapping generations. TREEPLAN� fits a reduced individual tree model for purposes of efficiency. TREEPLAN� can model multiple genetic groups, handle clonal data, fit multi-trait models with more than 50 traits, accommodate heterogeneous variances, fit site specific statistical and genetic models, and also weights information across environments (accounts for genotype by environment interaction) and time (allows for age:age correlations). The Southern Tree Breeding Association (STBA) is routinely using TREEPLAN� for genetic evaluation in Australian tree improvement programs for Pinus radiata, Eucalyptus globulus and E. nitens. TREEPLAN� has allowed data across generations and years to be combined in a multi-trait analysis to produce single lists of breeding values for each trait and environment combination. TREEPLAN� is easy to use and has the �industrial strength� to handle large amounts of unbalanced data with the complex pedigree structures that are usually associated with national or regional tree improvement programs. TREEPLAN� is fully integrated with a web based data management system that efficiently handles data and pedigree information. The analytical power and flexibility of the TREEPLAN� system has made routine genetic evaluation in trees a straightforward process.Papers and abstracts from the 27th Southern Forest Tree Improvement Conference held at Oklahoma State University in Stillwater, Oklahoma on June 24-27, 2003

Invasive Aspergillosis after Pandemic (H1N1) 2009

We report 2 patients with invasive aspergillosis after infection with pandemic (H1N1) 2009. Influenza viruses are known to cause immunologic defects and impair ciliary clearance. These defects, combined with high-dose corticosteroids prescribed during influenza-associated adult respiratory distress syndrome, may be novel risk factors predisposing otherwise immunocompetent patients to invasive aspergillosis

Liver Transplantation because of Acute Liver Failure due to Heme Arginate Overdose in a Patient with Acute Intermittent Porphyria

In acute attacks of acute intermittent porphyria, the mainstay of treatment is glucose and heme arginate administration. We present the case of a 58-year-old patient with acute liver failure requiring urgent liver transplantation after erroneous 6-fold overdose of heme arginate during an acute attack. As recommended in the product information, albumin and charcoal were administered and hemodiafiltration was started, which could not prevent acute liver failure, requiring super-urgent liver transplantation after 6 days. The explanted liver showed no preexisting liver cirrhosis, but signs of subacute liver injury and starting regeneration. The patient recovered within a short time. A literature review revealed four poorly documented cases of potential hepatic and/or renal toxicity of hematin or heme arginate. This is the first published case report of acute liver failure requiring super-urgent liver transplantation after accidental heme arginate overdose. The literature and recommendations in case of heme arginate overdose are summarized. Knowledge of a potentially fatal course is important for the management of future cases. If acute liver failure in case of heme arginate overdose is progressive, super-urgent liver transplantation has to be evaluated

On consensus biomarker selection

<p>Abstract</p> <p>Background</p> <p>Recent development of mass spectrometry technology enabled the analysis of complex peptide mixtures. A lot of effort is currently devoted to the identification of biomarkers in human body fluids like serum or plasma, based on which new diagnostic tests for different diseases could be constructed. Various biomarker selection procedures have been exploited in recent studies. It has been noted that they often lead to different biomarker lists and as a consequence, the patient classification may also vary.</p> <p>Results</p> <p>Here we propose a new approach to the biomarker selection problem: to apply several competing feature ranking procedures and compute a consensus list of features based on their outcomes. We validate our methods on two proteomic datasets for the diagnosis of ovarian and prostate cancer.</p> <p>Conclusion</p> <p>The proposed methodology can improve the classification results and at the same time provide a unified biomarker list for further biological examinations and interpretation.</p