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

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

Exploration of the Eucalyptus globulus gene pool

The first Europeans to discover Eucalyptus globulus were French explorers in 1792. Its seed was rapidly spread throughout the world in the 19th century and this was the species by which much of the world first knew the genus. However, it was in the industrial forests of the 20th century that this species, once considered the ‘Prince of Eucalypts’, achieved greatest prominence due to its fast growth and superior pulp qualities. Formal breeding first commenced in 1966 in Portugal and in the late 1980’s large base population trials from open-pollinated seed collections from native stands were established in many countries. These trials have provided unprecedented insights into the quantitative genetic control of numerous traits of economic and ecological importance and how this variation is spatially distributed in the native range of the species. However with large, fully pedigreed breeding populations becoming available for quantitative analysis and the rapidly expanding knowledge of DNA sequence variation, we are now at the threshold of a new understanding of this important eucalypt gene pool. Indications of the significance of non-additive genetic effects are becoming available. The E. globulus chloroplast genome has now been sequenced and several genome maps have been published. Studies of the variation in nuclear microsatellites and the lignin biosynthesis gene CCR confirm the complex, spatially structured nature of the native gene pool. Strong spatial structuring of the chloroplast genome has provided a tool for tracking seed migration and the geographic origin of exotic landraces. Highly divergent lineages of chloroplast DNA have been discovered and studies of the hypervariable JLA+ region argue that some components of the E. globulus gene pool have been assimilated from other species following hybridisation

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

On dynamic network entropy in cancer

The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network to induce a stochastic dynamics on the network, we here demonstrate that cancer cells are characterised by an increase in the dynamic network entropy, compared to cells of normal physiology. Using a fundamental relation between the macroscopic resilience of a dynamical system and the uncertainty (entropy) in the underlying microscopic processes, we argue that cancer cells will be more robust to random gene perturbations. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local dynamic entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local network dynamics. In particular, we also find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in the dynamic network entropy. In summary, our results support the view that the observed increased robustness of cancer cells to perturbation and therapy may be due to an increase in the dynamic network entropy that allows cells to adapt to the new cellular stresses. Conversely, genes that exhibit local flux entropy decreases in cancer may render cancer cells more susceptible to targeted intervention and may therefore represent promising drug targets.Comment: 10 pages, 3 figures, 4 tables. Submitte

Protective mechanisms and current clinical evidence of hypothermic oxygenated machine perfusion (HOPE) in preventing post-transplant cholangiopathy

The development of cholangiopathies after liver transplantation impacts on the quality and duration of graft and patient survival, contributing to higher costs as numerous interventions are required to treat strictures and infections at the biliary tree. Prolonged donor warm ischaemia time in combination with additional cold storage are key risk factors for the development of biliary strictures. Based on this, the clinical implementation of dynamic preservation strategies is a current hot topic in the field of donation after circulatory death (DCD) liver transplantation. Despite various retrospective studies reporting promising results, also regarding biliary complications, there are only a few randomised-controlled trials on machine perfusion. Recently, the group from Groningen has published the first randomised-controlled trial on hypothermic oxygenated perfusion (HOPE), demonstrating a significant reduction of symptomatic ischaemic cholangiopathies with the use of a short period of HOPE before DCD liver implantation. The most likely mechanism for this important effect, also shown in several experimental studies, is based on mitochondrial reprogramming under hypothermic aerobic conditions, e.g. exposure to oxygen in the cold, with a controlled and slow metabolism of ischaemically accumulated succinate and simultaneous ATP replenishment. This unique feature prevents mitochondrial oxidative injury and further downstream tissue inflammation. HOPE treatment therefore supports livers by protecting them from ischaemia-reperfusion injury (IRI), and thereby also prevents the development of post-transplant biliary injury. With reduced IRI-associated inflammation, recipients are also protected from activation of the innate immune system, with less acute rejections seen after HOPE

The Pathway Coexpression Network: Revealing pathway relationships.

A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer's Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at http://pcxn.org/

The IDEAL framework for machine perfusion in liver transplantation

Liver transplantation is challenged by organ scarcity and ageing donors. Machine perfusion is a promising technique to enhance organ preservation and assessment, improving liver utilization and patient outcomes. Here, we discuss current practices in machine perfusion using the IDEAL framework and outline the steps needed to advance this technology clinically

Domain-oriented edge-based alignment of protein interaction networks

Motivation: Recent advances in high-throughput experimental techniques have yielded a large amount of data on protein–protein interactions (PPIs). Since these interactions can be organized into networks, and since separate PPI networks can be constructed for different species, a natural research direction is the comparative analysis of such networks across species in order to detect conserved functional modules. This is the task of network alignment