The use of reverse micelles for the simultaneous extraction of oil and proteins from vegetable meal

A method for the simultaneous extraction of oil and proteins from vegetable meals is presented. The method uses hydrocarbon reverse micelles, so that the oil is extracted directly into the hydrocarbon phase and the proteins are solubilized in the water pools of the reverse micelles. The surfactant used is bis (2-ethylhexyl) sodium sulfosuccinate (AOT) in isooctane at variable w(0) values (w(0) measures the amount of water in the system, where w(0) = [H(2)O]/[AOT]). A comparison with the usual extraction methods is offered. It is shown that with the micelle system the extraction of oil is as large as with the usual methods, and it is independent of w(0). However the amount and type of proteins extracted depends strongly on w(0). At w(0) values below 6, no protein and only low molecular weight compounds (i.e. chlorogenic acid) are extracted, at larger water content (i.e. by increasing the dimension of the micelle water pool), also proteins are solubilized in a significant amount and with a molecular weight which increases by increasing W(0). The protein solubilized in the microemulsion system can be recovered into an aqueous phase with a back-transfer step.     

Heterogeneous binding of high mobility group chromosomal proteins to nuclei

A dramatic difference is observed in the intracellular distribution of the high mobility group (HMG) proteins when chicken embryo fibroblasts are fractionated into nucleus and cytoplasm by either mass enucleation of cytochalasin-B-treated cells or by differential centrifugation of mechanically disrupted cells. Nuclei (karyoplasts) obtained by cytochalasin B treatment of cells contain more than 90 percent of the HMG 1, while enucleated cytoplasts contain the remainder. A similar distribution between karyoplasts and cytoplasts is observed for the H1 histones and the nucleosomal core histones as anticipated. The presence of these proteins, in low amounts, in the cytoplast preparation can be accounted for by the small percentage of unenucleated cells present. In contrast, the nuclei isolated from mechanically disrupted cells contain only 30-40 percent of the total HMGs 1 and 2, the remainder being recovered in the cytosol fraction. No histone is observed in the cytosol fraction. Unike the higher molecular weight HMGs, most of the HMGs 14 and 17 sediment with the nuclei after cell lysis by mechanical disruption. The distribution of HMGs is unaffected by incubating cells with cytochalasin B and mechanically fractionating rather than enucleating them. Therefore, the dramatic difference in HMG 1 distribution observed using the two fractionation techniques cannot be explained by a cytochalasin-B-induced redistribution. On reextraction and sedimentation of isolated nuclei obtained by mechanical cell disruption, only 8 percent of the HMG 1 is released to the supernate. Thus, the majority of the HMG 1 originally isolated with these nuclei, representing 35 percent of the total HMG 1, is stably bound, as is all the HMGs 14 and 17. The remaining 65 percent of the HMGs 1 and 2 is unstably bound and leaks to the cytosol fraction under the conditions of mechanical disruption. It is suggested that the unstably bound HMGs form a protein pool capable of equilibrating between cytoplasm and stably bound HMGs.     

Efficiency clustering for low-density microarrays and its application to QPCR

Background  

Pathway-targeted or low-density arrays are used more and more frequently in biomedical research, particularly those arrays that are based on quantitative real-time PCR. Typical QPCR arrays contain 96-1024 primer pairs or probes, and they bring with it the promise of being able to reliably measure differences in target levels without the need to establish absolute standard curves for each and every target. To achieve reliable quantification all primer pairs or array probes must perform with the same efficiency.     

A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature

The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein–protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three kernels are clearly superior to the other methods.     

Inference of surface membrane factors of HIV-1 infection through functional interaction networks

Background

HIV infection affects the populations of T helper cells, dendritic cells and macrophages. Moreover, it has a serious impact on the central nervous system. It is yet not clear whether this list is complete and why specifically those cell types are affected. To address this question, we have developed a method to identify cellular surface proteins that permit, mediate or enhance HIV infection in different cell/tissue types in HIV-infected individuals. Receptors associated with HIV infection share common functions and domains and are involved in similar cellular processes. These properties are exploited by bioinformatics techniques to predict novel cell surface proteins that potentially interact with HIV.

Methodology/Principal Findings

We compiled a set of surface membrane proteins (SMP) that are known to interact with HIV. This set is extended by proteins that have direct interaction and share functional similarity. This resulted in a comprehensive network around the initial SMP set. Using network centrality analysis we predict novel surface membrane factors from the annotated network. We identify 21 surface membrane factors, among which three have confirmed functions in HIV infection, seven have been identified by at least two other studies, and eleven are novel predictions and thus excellent targets for experimental investigation.

Conclusions

Determining to what extent HIV can interact with human SMPs is an important step towards understanding patient specific disease progression. Using various bioinformatics techniques, we generate a set of surface membrane factors that constitutes a well-founded starting point for experimental testing of cell/tissue susceptibility of different HIV strains as well as for cohort studies evaluating patient specific disease progression.     

Adapters, shims, and glue--service interoperability for in silico experiments

MOTIVATION: Computationally, in silico experiments in biology are workflows describing the collaboration of people, data and methods. The Grid and Web services are proposed to be the next generation infrastructure supporting the deployment of bioinformatics workflows. But the growing number of autonomous and heterogeneous services pose challenges to the used middleware w.r.t. composition, i.e. discovery and interoperability of services required within in silico experiments. In the IRIS project, we handle the problem of service interoperability by a semi-automatic procedure for identifying and placing customizable adapters into workflows built by service composition. RESULTS: We show the effectiveness and robustness of the software-aided composition procedure by a case study in the field of life science. In this study we combine different database services with different analysis services with the objective of discovering required adapters. Our experiments show that we can identify relevant adapters with high precision and recall.