RDFScape: Semantic Web meets Systems Biology

<p>Abstract</p> <p>Background</p> <p>The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. As the Semantic Web is being introduced into the Life Sciences, the basis for a distributed knowledge-base that can foster biological data analysis is laid. However, there still is a dichotomy, in tools and methodologies, between the use of ontologies in biological investigation, that is, in relation to experimental observations, and their use as a knowledge-base.</p> <p>Results</p> <p>RDFScape is a plugin that has been developed to extend a software oriented to biological analysis with support for reasoning on ontologies in the semantic web framework. We show with this plugin how the use of ontological knowledge in biological analysis can be extended through the use of inference. In particular, we present two examples relative to ontologies representing biological pathways: we demonstrate how these can be abstracted and visualized as interaction networks, and how reasoning on causal dependencies within elements of pathways can be implemented.</p> <p>Conclusions</p> <p>The use of ontologies for the interpretation of high-throughput biological data can be improved through the use of inference. This allows the use of ontologies not only as annotations, but as a knowledge-base from which new information relevant for specific analysis can be derived.</p

METANNOGEN: compiling features of biochemical reactions needed for the reconstruction of metabolic networks

BACKGROUND: One central goal of computational systems biology is the mathematical modelling of complex metabolic reaction networks. The first and most time-consuming step in the development of such models consists in the stoichiometric reconstruction of the network, i. e. compilation of all metabolites, reactions and transport processes relevant to the considered network and their assignment to the various cellular compartments. Therefore an information system is required to collect and manage data from different databases and scientific literature in order to generate a metabolic network of biochemical reactions that can be subjected to further computational analyses. RESULTS: The computer program METANNOGEN facilitates the reconstruction of metabolic networks. It uses the well-known database of biochemical reactions KEGG of biochemical reactions as primary information source from which biochemical reactions relevant to the considered network can be selected, edited and stored in a separate, user-defined database. Reactions not contained in KEGG can be entered manually into the system. To aid the decision whether or not a reaction selected from KEGG belongs to the considered network METANNOGEN contains information of SWISSPROT and ENSEMBL and provides Web links to a number of important information sources like METACYC, BRENDA, NIST, and REACTOME. If a reaction is reported to occur in more than one cellular compartment, a corresponding number of reactions is generated each referring to one specific compartment. Transport processes of metabolites are entered like chemical reactions where reactants and products have different compartment attributes. The list of compartmentalized biochemical reactions and membrane transport processes compiled by means of METANNOGEN can be exported as an SBML file for further computational analysis. METANNOGEN is highly customizable with respect to the content of the SBML output file, additional data-fields, the graphical input form, highlighting of project specific search terms and dynamically generated Web-links. CONCLUSION: METANNOGEN is a flexible tool to manage information for the design of metabolic networks. The program requires Java Runtime Environment 1.4 or higher and about 100 MB of free RAM and about 200 MB of free HD space. It does not require installation and can be directly Java-webstarted from

Emerging Concepts for Pelvic Organ Prolapse Surgery: What is Cure?

The objective of this review is to discuss emerging concepts in pelvic organ prolapse, in particular, “What is cure?” In a post-trial data analysis of the CARE (Colpopexy and Urinary Reduction Efforts) trial, treatment success varied tremendously depending on the definition used (19.2%–97.2%). Definitions that included the absence of vaginal bulge symptoms had the strongest relationships with the patients’ assessment of overall improvement and treatment success. As demonstrated by this study, there are several challenges in defining cure in prolapse surgery. Additionally, the symptoms of prolapse are variable. The degree of prolapse does not correlate directly with symptoms. There are many surgical approaches to pelvic organ prolapse. Multiple ways to quantify prolapse are used. There is a lack of standardized definition of cure. The data on prolapse surgery outcomes are heterogeneous. The goal of surgical repair is to return the pelvic organs to their original anatomic positions. Ideally, we have four main goals: no anatomic prolapse, no functional symptoms, patient satisfaction, and the avoidance of complications. The impact of transvaginal mesh requires thoughtful investigation. The driving force should be patient symptoms in defining cure of prolapse

IL-21 signaling is essential for optimal host resistance against Mycobacterium tuberculosis infection

IL-21 is produced predominantly by activated CD4(+) T cells and has pleiotropic effects on immunity via the IL-21 receptor (IL-21R), a member of the common gamma chain (gamma(c)) cytokine receptor family. We show that IL-21 signaling plays a crucial role in T cell responses during Mycobacterium tuberculosis infection by augmenting CD8(+) T cell priming, promoting T cell accumulation in the lungs, and enhancing T cell cytokine production. In the absence of IL-21 signaling, more CD4(+) and CD8(+) T cells in chronically infected mice express the T cell inhibitory molecules PD-1 and TIM-3. We correlate these immune alterations with increased susceptibility of IL-21R(-/-) mice, which have increased lung bacterial burden and earlier mortality compared to WT mice. Finally, to causally link the immune defects with host susceptibility, we use an adoptive transfer model to show that IL-21R(-/-) T cells transfer less protection than WT T cells. These results prove that IL-21 signaling has an intrinsic role in promoting the protective capacity of T cells. Thus, the net effect of IL-21 signaling is to enhance host resistance to M. tuberculosis. These data position IL-21 as a candidate biomarker of resistance to tuberculosis.This work was supported by National Institutes of Health Grants R21 AI100766, R01 AI106725, and P01 AI073748

LEDAcrypt: QC-LDPC Code-Based Cryptosystems with Bounded Decryption Failure Rate

We consider the QC-LDPC code-based cryptosystems named LEDAcrypt, which are under consideration by NIST for the second round of the post-quantum cryptography standardization initiative. LEDAcrypt is the result of the merger of the key encapsulation mechanism LEDAkem and the public-key cryptosystem LEDApkc, which were submitted to the first round of the same competition. We provide a detailed quantification of the quantum and classical computational efforts needed to foil the cryptographic guarantees of these systems. To this end, we take into account the best known attacks that can be mounted against them employing both classical and quantum computers, and compare their computational complexities with the ones required to break AES, coherently with the NIST requirements. Assuming the original LEDAkem and LEDApkc parameters as a reference, we introduce an algorithmic optimization procedure to design new sets of parameters for LEDAcrypt. These novel sets match the security levels in the NIST call and make the C reference implementation of the systems exhibit significantly improved figures of merit, in terms of both running times and key sizes. As a further contribution, we develop a theoretical characterization of the decryption failure rate (DFR) of LEDAcrypt cryptosystems, which allows new instances of the systems with guaranteed low DFR to be designed. Such a characterization is crucial to withstand recent attacks exploiting the reactions of the legitimate recipient upon decrypting multiple ciphertexts with the same private key, and consequentially it is able to ensure a lifecycle of the corresponding key pairs which can be sufficient for the wide majority of practical purposes

Inferring predominant pathways in cellular models of breast cancer using limited sample proteomic profiling

<p>Abstract</p> <p>Background</p> <p>Molecularly targeted drugs inhibit aberrant signaling within oncogenic pathways. Identifying the predominant pathways at work within a tumor is a key step towards tailoring therapies to the patient. Clinical samples pose significant challenges for proteomic profiling, an attractive approach for identifying predominant pathways. The objective of this study was to determine if information obtained from a limited sample (i.e., a single gel replicate) can provide insight into the predominant pathways in two well-characterized breast cancer models.</p> <p>Methods</p> <p>A comparative proteomic analysis of total cell lysates was obtained from two cellular models of breast cancer, BT474 (HER2+/ER+) and SKBR3 (HER2+/ER-), using two-dimensional electrophoresis and MALDI-TOF mass spectrometry. Protein interaction networks and canonical pathways were extracted from the Ingenuity Pathway Knowledgebase (IPK) based on association with the observed pattern of differentially expressed proteins.</p> <p>Results</p> <p>Of the 304 spots that were picked, 167 protein spots were identified. A threshold of 1.5-fold was used to select 62 proteins used in the analysis. IPK analysis suggested that metabolic pathways were highly associated with protein expression in SKBR3 cells while cell motility pathways were highly associated with BT474 cells. Inferred protein networks were confirmed by observing an up-regulation of IGF-1R and profilin in BT474 and up-regulation of Ras and enolase in SKBR3 using western blot.</p> <p>Conclusion</p> <p>When interpreted in the context of prior information, our results suggest that the overall patterns of differential protein expression obtained from limited samples can still aid in clinical decision making by providing an estimate of the predominant pathways that underpin cellular phenotype.</p

Proteome Regulation during Olea europaea Fruit Development

Widespread in the Mediterranean basin, Olea europaea trees are gaining worldwide popularity for the nutritional and cancer-protective properties of the oil, mechanically extracted from ripe fruits. Fruit development is a physiological process with remarkable impact on the modulation of the biosynthesis of compounds affecting the quality of the drupes as well as the final composition of the olive oil. Proteomics offers the possibility to dig deeper into the major changes during fruit development, including the important phase of ripening, and to classify temporal patterns of protein accumulation occurring during these complex physiological processes.In this work, we started monitoring the proteome variations associated with olive fruit development by using comparative proteomics coupled to mass spectrometry. Proteins extracted from drupes at three different developmental stages were separated on 2-DE and subjected to image analysis. 247 protein spots were revealed as differentially accumulated. Proteins were identified from a total of 121 spots and discussed in relation to olive drupe metabolic changes occurring during fruit development. In order to evaluate if changes observed at the protein level were consistent with changes of mRNAs, proteomic data produced in the present work were compared with transcriptomic data elaborated during previous studies.This study identifies a number of proteins responsible for quality traits of cv. Coratina, with particular regard to proteins associated to the metabolism of fatty acids, phenolic and aroma compounds. Proteins involved in fruit photosynthesis have been also identified and their pivotal contribution in oleogenesis has been discussed. To date, this study represents the first characterization of the olive fruit proteome during development, providing new insights into fruit metabolism and oil accumulation process

Annotation Error in Public Databases: Misannotation of Molecular Function in Enzyme Superfamilies

Due to the rapid release of new data from genome sequencing projects, the majority of protein sequences in public databases have not been experimentally characterized; rather, sequences are annotated using computational analysis. The level of misannotation and the types of misannotation in large public databases are currently unknown and have not been analyzed in depth. We have investigated the misannotation levels for molecular function in four public protein sequence databases (UniProtKB/Swiss-Prot, GenBank NR, UniProtKB/TrEMBL, and KEGG) for a model set of 37 enzyme families for which extensive experimental information is available. The manually curated database Swiss-Prot shows the lowest annotation error levels (close to 0% for most families); the two other protein sequence databases (GenBank NR and TrEMBL) and the protein sequences in the KEGG pathways database exhibit similar and surprisingly high levels of misannotation that average 5%–63% across the six superfamilies studied. For 10 of the 37 families examined, the level of misannotation in one or more of these databases is >80%. Examination of the NR database over time shows that misannotation has increased from 1993 to 2005. The types of misannotation that were found fall into several categories, most associated with “overprediction” of molecular function. These results suggest that misannotation in enzyme superfamilies containing multiple families that catalyze different reactions is a larger problem than has been recognized. Strategies are suggested for addressing some of the systematic problems contributing to these high levels of misannotation

The state of the art in the analysis of two-dimensional gel electrophoresis images

Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis experiments. Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments. We cover the process starting with the imaging of 2-D gels, quantitation of spots, creation of expression profiles to statistical expression analysis followed by the presentation of results. Challenges for analysis software as well as good practices are highlighted. We emphasize image warping and related methods that are able to overcome the difficulties that are due to varying migration positions of spots between gels. Spot detection, quantitation, normalization, and the creation of expression profiles are described in detail. The recent development of consensus spot patterns and complete expression profiles enables one to take full advantage of statistical methods for expression analysis that are well established for the analysis of DNA microarray experiments. We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field