Size-Dependent Surface Plasmon Dynamics in Metal Nanoparticles

We study the effect of Coulomb correlations on the ultrafast optical dynamics of small metal particles. We demonstrate that a surface-induced dynamical screening of the electron-electron interactions leads to quasiparticle scattering with collective surface excitations. In noble-metal nanoparticles, it results in an interband resonant scattering of d-holes with surface plasmons. We show that this size-dependent many-body effect manifests itself in the differential absorption dynamics for frequencies close to the surface plasmon resonance. In particular, our self-consistent calculations reveal a strong frequency dependence of the relaxation, in agreement with recent femtosecond pump-probe experiments.Comment: 8 pages + 4 figures, final version accepted to PR

Discretization Provides a Conceptually Simple Tool to Build Expression Networks

Biomarker identification, using network methods, depends on finding regular co-expression patterns; the overall connectivity is of greater importance than any single relationship. A second requirement is a simple algorithm for ranking patients on how relevant a gene-set is. For both of these requirements discretized data helps to first identify gene cliques, and then to stratify patients

Learning Transcriptional Regulatory Relationships Using Sparse Graphical Models

Understanding the organization and function of transcriptional regulatory networks by analyzing high-throughput gene expression profiles is a key problem in computational biology. The challenges in this work are 1) the lack of complete knowledge of the regulatory relationship between the regulators and the associated genes, 2) the potential for spurious associations due to confounding factors, and 3) the number of parameters to learn is usually larger than the number of available microarray experiments. We present a sparse (L1 regularized) graphical model to address these challenges. Our model incorporates known transcription factors and introduces hidden variables to represent possible unknown transcription and confounding factors. The expression level of a gene is modeled as a linear combination of the expression levels of known transcription factors and hidden factors. Using gene expression data covering 39,296 oligonucleotide probes from 1109 human liver samples, we demonstrate that our model better predicts out-of-sample data than a model with no hidden variables. We also show that some of the gene sets associated with hidden variables are strongly correlated with Gene Ontology categories. The software including source code is available at http://grnl1.codeplex.com

Using a population-based approach to prevent hepatocellular cancer in New South Wales, Australia: effects on health services utilisation

<p>Abstract</p> <p>Background</p> <p>Australians born in countries where hepatitis B infection is endemic are 6-12 times more likely to develop hepatocellular cancer (HCC) than Australian-born individuals. However, a program of screening, surveillance and treatment of chronic hepatitis B (CHB) in high risk populations could significantly reduce disease progression and death related to end-stage liver disease and HCC. Consequently we are implementing the <it>B Positive </it>pilot project, aiming to optimise the management of CHB in at-risk populations in south-west Sydney. Program participants receive routine care, enhanced disease surveillance or specialist referral, according to their stage of CHB infection, level of viral load and extent of liver injury. In this paper we examine the program's potential impact on health services utilisation in the study area.</p> <p>Methods</p> <p>Estimated numbers of CHB infections were derived from Australian Bureau of Statistics data and applying estimates of HBV prevalence rates from migrants' countries of birth. These figures were entered into a Markov model of disease progression, constructing a hypothetical cohort of Asian-born adults with CHB infection. We calculated the number of participants in different CHB disease states and estimated the numbers of GP and specialist consultations and liver ultrasound examinations the cohort would require annually over the life of the program.</p> <p>Results</p> <p>Assuming a 25% participation rate among the 5,800 local residents estimated to have chronic hepatitis B infection, approximately 750 people would require routine follow up, 260 enhanced disease surveillance and 210 specialist care during the first year after recruitment is completed. This translates into 5 additional appointments per year for each local GP, 25 for each specialist and 420 additional liver ultrasound examinations.</p> <p>Conclusions</p> <p>While the program will not greatly affect the volume of local GP consultations, it will lead to a significant increase in demand for specialist services. New models of CHB care may be required to aid program implementation and up scaling the program will need to factor in additional demands on health care utilisation in areas of high hepatitis B sero-prevalence.</p

Bagging Statistical Network Inference from Large-Scale Gene Expression Data

Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository

Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size

Abstract Background Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required. Thus, the performances of co-expression network analysis methods on large co-expression networks consisting of a few thousand nodes, with only a small number of profiles with a single perturbation, which more accurately reflect normal experimental conditions, are generally uncharacterized and unknown. Methods We proposed a novel network inference methods based on Relevance Low order Partial Correlation (RLowPC). RLowPC method uses a two-step approach to select on the high-confidence edges first by reducing the search space by only picking the top ranked genes from an intial partial correlation analysis and, then computes the partial correlations in the confined search space by only removing the linear dependencies from the shared neighbours, largely ignoring the genes showing lower association. Results We selected six co-expression-based methods with good performance in evaluation studies from the literature: Partial correlation, PCIT, ARACNE, MRNET, MRNETB and CLR. The evaluation of these methods was carried out on simulated time-series data with various network sizes ranging from 100 to 3000 nodes. Simulation results show low precision and recall for all of the above methods for large networks with a small number of expression profiles. We improved the inference significantly by refinement of the top weighted edges in the pre-inferred partial correlation networks using RLowPC. We found improved performance by partitioning large networks into smaller co-expressed modules when assessing the method performance within these modules. Conclusions The evaluation results show that current methods suffer from low precision and recall for large co-expression networks where only a small number of profiles are available. The proposed RLowPC method effectively reduces the indirect edges predicted as regulatory relationships and increases the precision of top ranked predictions. Partitioning large networks into smaller highly co-expressed modules also helps to improve the performance of network inference methods. The RLowPC R package for network construction, refinement and evaluation is available at GitHub: https://github.com/wyguo/RLowPC

APOBEC3G-Induced Hypermutation of Human Immunodeficiency Virus Type-1 Is Typically a Discrete “All or Nothing” Phenomenon

The rapid evolution of Human Immunodeficiency Virus (HIV-1) allows studies of ongoing host–pathogen interactions. One key selective host factor is APOBEC3G (hA3G) that can cause extensive and inactivating Guanosine-to-Adenosine (G-to-A) mutation on HIV plus-strand DNA (termed hypermutation). HIV can inhibit this innate anti-viral defense through binding of the viral protein Vif to hA3G, but binding efficiency varies and hypermutation frequencies fluctuate in patients. A pivotal question is whether hA3G-induced G-to-A mutation is always lethal to the virus or if it may occur at sub-lethal frequencies that could increase viral diversification. We show in vitro that limiting-levels of hA3G-activity (i.e. when only a single hA3G-unit is likely to act on HIV) produce hypermutation frequencies similar to those in patients and demonstrate in silico that potentially non-lethal G-to-A mutation rates are ∼10-fold lower than the lowest observed hypermutation levels in vitro and in vivo. Our results suggest that even a single incorporated hA3G-unit is likely to cause extensive and inactivating levels of HIV hypermutation and that hypermutation therefore is typically a discrete “all or nothing” phenomenon. Thus, therapeutic measures that inhibit the interaction between Vif and hA3G will likely not increase virus diversification but expand the fraction of hypermutated proviruses within the infected host

Deaminase-Independent Inhibition of Parvoviruses by the APOBEC3A Cytidine Deaminase

The APOBEC3 proteins form a multigene family of cytidine deaminases with inhibitory activity against viruses and retrotransposons. In contrast to APOBEC3G (A3G), APOBEC3A (A3A) has no effect on lentiviruses but dramatically inhibits replication of the parvovirus adeno-associated virus (AAV). To study the contribution of deaminase activity to the antiviral activity of A3A, we performed a comprehensive mutational analysis of A3A. By mutation of non-conserved residues, we found that regions outside of the catalytic active site contribute to both deaminase and antiviral activities. Using A3A point mutants and A3A/A3G chimeras, we show that deaminase activity is not required for inhibition of recombinant AAV production. We also found that deaminase-deficient A3A mutants block replication of both wild-type AAV and the autonomous parvovirus minute virus of mice (MVM). In addition, we identify specific residues of A3A that confer activity against AAV when substituted into A3G. In summary, our results demonstrate that deaminase activity is not necessary for the antiviral activity of A3A against parvoviruses

Characterization of the Interaction of Full-Length HIV-1 Vif Protein with its Key Regulator CBFβ and CRL5 E3 Ubiquitin Ligase Components

Human immunodeficiency virus-1 (HIV-1) viral infectivity factor (Vif) is essential for viral replication because of its ability to eliminate the host's antiviral response to HIV-1 that is mediated by the APOBEC3 family of cellular cytidine deaminases. Vif targets these proteins, including APOBEC3G, for polyubiquitination and subsequent proteasome-mediated degradation via the formation of a Cullin5-ElonginB/C-based E3 ubiquitin ligase. Determining how the cellular components of this E3 ligase complex interact with Vif is critical to the intelligent design of new antiviral drugs. However, structural studies of Vif, both alone and in complex with cellular partners, have been hampered by an inability to express soluble full-length Vif protein. Here we demonstrate that a newly identified host regulator of Vif, core-binding factor-beta (CBFβ), interacts directly with Vif, including various isoforms and a truncated form of this regulator. In addition, carboxyl-terminal truncations of Vif lacking the BC-box and cullin box motifs were sufficient for CBFβ interaction. Furthermore, association of Vif with CBFβ, alone or in combination with Elongin B/C (EloB/C), greatly increased the solubility of full-length Vif. Finally, a stable complex containing Vif-CBFβ-EloB/C was purified in large quantity and shown to bind purified Cullin5 (Cul5). This efficient strategy for purifying Vif-Cul5-CBFβ-EloB/C complexes will facilitate future structural and biochemical studies of Vif function and may provide the basis for useful screening approaches for identifying novel anti-HIV drug candidates