Polysaccharide-coated thermosets for orthopedic applications: from material characterization to in vivo tests

The long-term stability and success of orthopedic implants depend on the osseointegration process, which is strongly influenced by the biomaterial surface. A promising approach to enhance implant integration involves the modification of the surface of the implant by means of polymers that mimic the natural components of the extracellular matrix, for example, polysaccharides. In this study, methacrylate thermosets (bisphenol A glycidylmethacrylate/triethyleneglycol dimethacrylate), a widely used composition for orthopedic and dental applications, have been coated by electrostatic deposition of a bioactive chitosan-derivative. This polysaccharide was shown to induce osteoblasts aggregation in vitro, to stimulate cell proliferation and to enhance alkaline phosphatase activity. The coating deposition was studied by analyzing the effect of pH and ionic strength on the grafting of the polysaccharide. Contact angle studies show that the functionalized material displays a higher hydrophilic character owing to the increase of surface polar groups. The mechanical properties of the coating were evaluated by nanoindentation studies which point to higher values of indentation hardness and modulus (E) of the polysaccharide surface layer, while the influence of cyclic stress on the construct was assessed by fatigue tests. Finally, in vivo tests in minipigs showed that the polysaccharide-based implant showed a good biocompatibility and an ability for osseointegration at least similar to that of the titanium Ti6Al4V alloy with roughened surface.     

Kinetics of PKCε activating and inhibiting llama single chain antibodies and their effect on PKCε translocation in HeLa cells

Dysregulation of PKCε is involved in several serious diseases such as cancer, type II diabetes and Alzheimer's disease. Therefore, specific activators and inhibitors of PKCε hold promise as future therapeutics, in addition to being useful in research into PKCε regulated pathways. We have previously described llama single chain antibodies (VHHs) that specifically activate (A10, C1 and D1) or inhibit (E6 and G8) human recombinant PKCε. Here we report a thorough kinetic analysis of these VHHs. The inhibiting VHHs act as non-competitive inhibitors of PKCε activity, whereas the activating VHHs have several different modes of action, either increasing V(max) and/or decreasing K(m) values. We also show that the binding of the VHHs to PKCε is conformation-dependent, rendering the determination of affinities difficult. Apparent affinities are in the micromolar range based on surface plasmon resonance studies. Furthermore, the VHHs have no effect on the activity of rat PKCε nor can they bind the rat form of the protein in immunoprecipitation studies despite the 98% identity between the human and rat PKCε proteins. Finally, we show for the first time that the VHHs can influence PKCε function also in cells, since an activating VHH increases the rate of PKCε translocation in response to PMA in HeLa cells, whereas an inhibiting VHH slows down the translocation. These results give insight into the mechanisms of PKCε activity modulation and highlight the importance of protein conformation on VHH binding.     

Quantification of 1H NMR spectra of human cerebrospinal fluid: a protocol based on constrained total-line-shape analysis

A protocol for quantitative ¹H NMR analysis of human cerebrospinal fluid (hCSF) was built up and assessed as based on Constrained Total-Line-Shape (CTLS) fitting. In this method, linear constraints were applied to spectral structures. The ¹H NMR spectra of 45 human CSF samples were measured and quantified using the CTLS method. The quantification strategies based on total-line-shape fitting are discussed. The metabolic model for CTLS includes 31 metabolites covering 85% of the total spectral intensity, excluding the protein contribution. Prior to data analysis, the data was divided into patients with no Alzheimer’s disease (AD), but with a normal AD marker profile (the peptide β-amyloid₄₂ and tau protein) present in CSF, and into controls that do not have an AD marker profile in CSF. Unexpectedly large variations in metabolite concentrations within the two patient groups were detected, but an analysis of variance revealed a significant (P = 0.027) difference only in the concentration of creatinine which was higher in patients that had a normal AD marker profile. Multivariate classification tools such as self-organizing maps (SOM) failed in separation of the two classes.     

Viral population estimation using pyrosequencing

The diversity of virus populations within single infected hosts presents a major difficulty for the natural immune response as well as for vaccine design and antiviral drug therapy. Recently developed pyrophosphate-based sequencing technologies (pyrosequencing) can be used for quantifying this diversity by ultra-deep sequencing of virus samples. We present computational methods for the analysis of such sequence data and apply these techniques to pyrosequencing data obtained from HIV populations within patients harboring drug-resistant virus strains. Our main result is the estimation of the population structure of the sample from the pyrosequencing reads. This inference is based on a statistical approach to error correction, followed by a combinatorial algorithm for constructing a minimal set of haplotypes that explain the data. Using this set of explaining haplotypes, we apply a statistical model to infer the frequencies of the haplotypes in the population via an expectation-maximization (EM) algorithm. We demonstrate that pyrosequencing reads allow for effective population reconstruction by extensive simulations and by comparison to 165 sequences obtained directly from clonal sequencing of four independent, diverse HIV populations. Thus, pyrosequencing can be used for cost-effective estimation of the structure of virus populations, promising new insights into viral evolutionary dynamics and disease control strategies.     

Anticipation of natural stimuli modulates EEG dynamics: physiology and simulation

In everyday life we often encounter situations in which we can expect a visual stimulus before we actually see it. Here, we study the impact of such stimulus anticipation on the actual response to a visual stimulus. Participants were to indicate the sex of deer and cattle on photographs of the respective animals. On some trials, participants were cued on the species of the upcoming animal whereas on other trials this was not the case. Time frequency analysis of the simultaneously recorded EEG revealed modulations by this cue stimulus in two time windows. Early spectral responses displayed strongest stimulus-locking for stimuli that were preceded by a cue if they were sufficiently large. Late responses displayed enhanced amplitudes in response to large stimuli and to stimuli that were preceded by a cue. For late responses, however, no interaction between cue and stimulus size was observed. We were able to explain these results in a simulation by prestimulus gain modulations (early response) and by decreased response thresholds (late response). Thus, it seems plausible, that stimulus anticipation results in a pretuning of local neural populations.     

Time pressure modulates electrophysiological correlates of early visual processing

Background

Reactions to sensory events sometimes require quick responses whereas at other times they require a high degree of accuracy–usually resulting in slower responses. It is important to understand whether visual processing under different response speed requirements employs different neural mechanisms.

Methodology/Principal Findings

We asked participants to classify visual patterns with different levels of detail as real-world or non-sense objects. In one condition, participants were to respond immediately, whereas in the other they responded after a delay of 1 second. As expected, participants performed more accurately in delayed response trials. This effect was pronounced for stimuli with a high level of detail. These behavioral effects were accompanied by modulations of stimulus related EEG gamma oscillations which are an electrophysiological correlate of early visual processing. In trials requiring speeded responses, early stimulus-locked oscillations discriminated real-world and non-sense objects irrespective of the level of detail. For stimuli with a higher level of detail, oscillatory power in a later time window discriminated real-world and non-sense objects irrespective of response speed requirements.

Conclusions/Significance

Thus, it seems plausible to assume that different response speed requirements trigger different dynamics of processing.     

Modeling Mutual Exclusivity of Cancer Mutations

In large collections of tumor samples, it has been observed that sets of genes that are commonly involved in the same cancer pathways tend not to occur mutated together in the same patient. Such gene sets form mutually exclusive patterns of gene alterations in cancer genomic data. Computational approaches that detect mutually exclusive gene sets, rank and test candidate alteration patterns by rewarding the number of samples the pattern covers and by punishing its impurity, i.e., additional alterations that violate strict mutual exclusivity. However, the extant approaches do not account for possible observation errors. In practice, false negatives and especially false positives can severely bias evaluation and ranking of alteration patterns. To address these limitations, we develop a fully probabilistic, generative model of mutual exclusivity, explicitly taking coverage, impurity, as well as error rates into account, and devise efficient algorithms for parameter estimation and pattern ranking. Based on this model, we derive a statistical test of mutual exclusivity by comparing its likelihood to the null model that assumes independent gene alterations. Using extensive simulations, the new test is shown to be more powerful than a permutation test applied previously. When applied to detect mutual exclusivity patterns in glioblastoma and in pan-cancer data from twelve tumor types, we identify several significant patterns that are biologically relevant, most of which would not be detected by previous approaches. Our statistical modeling framework of mutual exclusivity provides increased flexibility and power to detect cancer pathways from genomic alteration data in the presence of noise. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.     

Computational methods for the design of effective therapies against drug resistant HIV strains

The development of drug resistance is a major obstacle to successful treatment of HIV infection. The extraordinary replication dynamics of HIV facilitates its escape from selective pressure exerted by the human immune system and by combination drug therapy. We have developed several computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genomic data.     

Terrestrins A-G: p-terphenyl derivatives from the inedible mushroom Thelephora terrestris

Seven p-terphenyl derivatives named terrestrins A-G together with three known ganbajunin B, thelephantins F and H, were isolated from the methanol extract of fruiting bodies of the Japanese inedible mushroom Thelephora terrestris (Thelephoraceae). Their structures were elucidated by means of high-resolution MS, 2D NMR, IR and UV spectroscopy, and X-ray crystallographic analysis.