Expression of a functional single chain antibody on the surface of extracellular enveloped vaccinia virus as a step towards selective tumour cell targeting.

Recombinant vaccinia virus with tumour cell specificity may provide a versatile tool either for direct lysis of cancer cells or for the targeted transfer of genes encoding immunomodulatory molecules. We report the expression of a single chain antibody on the surface of extracellular enveloped vaccinia virus. The wild-type haemagglutinin, an envelope glycoprotein which is not required for viral infection and replication, was replaced by haemagglutinin fusion molecules carrying a single chain antibody directed against the tumour-associated antigen ErbB2. ErbB2 is an epidermal growth factor receptor-related tyrosine kinase overexpressed in a high percentage of human adenocarcinomas. Two fusion proteins carrying the single chain antibody at different NH2-terminal positions were expressed and exposed at the envelope of the corresponding recombinant viruses. The construct containing the antibody at the site of the immunoglobulin-like loop of the haemagglutinin was able to bind solubilized ErbB2. This is the first report of replacement of a vaccinia virus envelope protein by a specific recognition structure and represents a first step towards modifying the host cell tropism of the virus.

Enhancing the vertical resolution of surface georadar data

There are far-reaching conceptual similarities between bi-static surface georadar and post-stack, "zero-offset" seismic reflection data, which is expressed in largely identical processing flows. One important difference is, however, that standard deconvolution algorithms routinely used to enhance the vertical resolution of seismic data are notoriously problematic or even detrimental to the overall signal quality when applied to surface georadar data. We have explored various options for alleviating this problem and have tested them on a geologically well-constrained surface georadar dataset. Standard stochastic and direct deterministic deconvolution approaches proved to be largely unsatisfactory. While least-squares-type deterministic deconvolution showed some promise, the inherent uncertainties involved in estimating the source wavelet introduced some artificial "ringiness". In contrast, we found spectral balancing approaches to be effective, practical and robust means for enhancing the vertical resolution of surface georadar data, particularly, but not exclusively, in the uppermost part of the georadar section, which is notoriously plagued by the interference of the direct air- and groundwaves. For the data considered in this study, it can be argued that band-limited spectral blueing may provide somewhat better results than standard band-limited spectral whitening, particularly in the uppermost part of the section affected by the interference of the air- and groundwaves. Interestingly, this finding is consistent with the fact that the amplitude spectrum resulting from least-squares-type deterministic deconvolution is characterized by a systematic enhancement of higher frequencies at the expense of lower frequencies and hence is blue rather than white. It is also consistent with increasing evidence that spectral "blueness" is a seemingly universal, albeit enigmatic, property of the distribution of reflection coefficients in the Earth. Our results therefore indicate that spectral balancing techniques in general and spectral blueing in particular represent simple, yet effective means of enhancing the vertical resolution of surface georadar data and, in many cases, could turn out to be a preferable alternative to standard deconvolution approaches.

Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Human urothelial cells grown on collagen adsorbed to surface-modified polymers.

OBJECTIVES: Tissue engineering methods can be applied to regenerate diseased, or congenitally missing, urinary tract tissues. Urinary tract tissue cell cultures must be established in vitro and adequate matrices, acting as cell carriers, must be developed. Although degradable and nondegradable polymer matrices offer adequate mechanical stability, they are not optimal for cell adherence and growth. To overcome this problem, extracellular matrix proteins, permitting cell adhesion and regulation of cell proliferation and differentiation, can be adsorbed to the surface-modified polymer. METHODS: In this study, nondegradable polymer films, poly(ethylene terephthalate), were used as an experimental model. Films were modified by graft polymerization of acrylic acid to subsequently allow collagen type I and III immobilization. The following adhesion, proliferation of human urothelial cells, and induction of their stratification were analyzed. RESULTS: Collagen adsorption on 0.2 microg/cm2 poly(acrylic acid)-grafted polymer films rendered the matrix apt for human urothelial cell adhesion and proliferation. Furthermore, stratification of urothelial cells was demonstrated on these surface-modified matrices. CONCLUSIONS: These results have shown that surface-modified polymer matrices can be used to act as cell carriers for cultured human urothelial cells. Such a cell-matrix construct could be applied in reparative surgery of the urinary tract.

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

Detachment of cell surface-bound anticarcinoembryonic antigen immune complexes by phospholipase C.

CEA as well as normal cross-reacting antigens (NCA) are fixed to the cell membrane via phosphatidylinositol (PI). To find out whether these antigens are internalized after antibody contact, acid pH desorption was compared to phospholipase C (PLC)-mediated cleavage of the antigen anchor. With the former procedure, marked differences in the desorbability of individual MAbs were noted, while PLC was able to cleave off surface-bound immune complexes irrespective of the MAb involved. From this it is concluded that internalization of MAb complexes of CEA/NCA, if occurring at all, is a low efficiency process.

Calibration of surface contamination monitors for the detection of iodine incorporation in the thyroid gland.

In Switzerland, individuals exposed to the risk of activity intake are required to perform regular monitoring. Monitoring consists in a screening measurement and is meant to be performed using commonly available laboratory instruments. More particularly, iodine intake is measured using a surface contamination monitor. The goal of the present paper is to report the calibration method developed for thyroid screening instruments. It consists of measuring the instrument response to a known activity located in the thyroid gland of a standard neck phantom. One issue of this procedure remains that the iodine radioisotopes have a short half-life. Therefore, the adequacy and limitations to simulate the short-lived radionuclides with so-called mock radionuclides of longer half-life were also evaluated. In light of the results, it has been decided to use only the appropriate iodine sources to perform the calibration.

Redirecting anti-viral CTL against cancer cells by surface targeting of monomeric MHC class I-viral peptide conjugated to antibody fragments.

To combine the advantage of both the tumor targeting capacity of high affinity monoclonal antibodies (mAbs) and the potent killing properties of cytotoxic T lymphocytes (CTL), we investigated the activity of conjugates made by coupling single Fab' fragments, from mAbs specific for tumor cell surface antigens, to monomeric HLA-A2 complexes containing the immunodominant influenza-matrix peptide 58-66. In solution, the monovalent 95 kDa Fab-HLA-A2/Flu conjugates did not activate influenza-specific CTL. However, when targeted to tumor cells expressing the relevant tumor-associated antigen, the conjugates induced CTL activation and efficient tumor cell lysis, as a result of MHC/peptide surface oligomerization. The highly specific and sensitive in vitro cytotoxicity results presented suggest that injection of Fab-MHC/peptide conjugates could represent a new form of immunotherapy, bridging antibody and T lymphocyte attack on cancer cells.

Cell surface expression of the epithelial Na channel and a mutant causing Liddle syndrome: a quantitative approach.

The epithelial amiloride-sensitive sodium channel (ENaC) controls transepithelial Na+ movement in Na(+)-transporting epithelia and is associated with Liddle syndrome, an autosomal dominant form of salt-sensitive hypertension. Detailed analysis of ENaC channel properties and the functional consequences of mutations causing Liddle syndrome has been, so far, limited by lack of a method allowing specific and quantitative detection of cell-surface-expressed ENaC. We have developed a quantitative assay based on the binding of 125I-labeled M2 anti-FLAG monoclonal antibody (M2Ab*) directed against a FLAG reporter epitope introduced in the extracellular loop of each of the alpha, beta, and gamma ENaC subunits. Insertion of the FLAG epitope into ENaC sequences did not change its functional and pharmacological properties. The binding specificity and affinity (Kd = 3 nM) allowed us to correlate in individual Xenopus oocytes the macroscopic amiloride-sensitive sodium current (INa) with the number of ENaC wild-type and mutant subunits expressed at the cell surface. These experiments demonstrate that: (i) only heteromultimeric channels made of alpha, beta, and gamma ENaC subunits are maximally and efficiently expressed at the cell surface; (ii) the overall ENaC open probability is one order of magnitude lower than previously observed in single-channel recordings; (iii) the mutation causing Liddle syndrome (beta R564stop) enhances channel activity by two mechanisms, i.e., by increasing ENaC cell surface expression and by changing channel open probability. This quantitative approach provides new insights on the molecular mechanisms underlying one form of salt-sensitive hypertension.

Cell surface expression of a functional rubella virus E1 glycoprotein by addition of a GPI anchor.

Rubella virus (RV) envelope glycoproteins E1 and E2 are targeted to the Golgi as heterodimers. While E2 contains a transmembrane Golgi retention signal, E1 is arrested in a pre-Golgi compartment in the absence of E2, and appears to require heterodimerization in order to reach the Golgi. Various forms of E1 with deletions in the ectodomain or lacking the cytoplasmic (CT) and transmembrane (TM) domains, as well as the 29 C-terminal amino acid residues of the ectodomain were also retained intracellularly. We therefore investigated the possibility of targetting E1 to the plasma membrane by addition of a glycosylphosphatidylinositol (GPI) anchor. We found that E1GPI was transported to the cell surface where it retained the hemadsorption activity characteristic of the wild-type E1/E2 heterodimer. Furthermore, coexpression of a mammalian GPI-specific phospholipase D (GPI-PLD) resulted in the release of E1GPI and in constitutive expression of a soluble form of E1. This study thus demonstrates that the GPI anchor has a dominant effect over the E1 pre-Golgi retention signal and that E1 is sufficient for hemadsorption.

Synchronous versus asynchronous modeling of gene regulatory networks.

MOTIVATION: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. RESULTS: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. AVAILABILITY: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.