Evolution of apoptosis-like programmed cell death in unicellular protozoan parasites

Apoptosis-like programmed cell death (PCD) has recently been described in multiple taxa of unicellular protists, including the protozoan parasites Plasmodium, Trypanosoma and Leishmania. Apoptosis-like PCD in protozoan parasites shares a number of morphological features with programmed cell death in multicellular organisms. However, both the evolutionary explanations and mechanisms involved in parasite PCD are poorly understood. Explaining why unicellular organisms appear to undergo 'suicide' is a challenge for evolutionary biology and uncovering death executors and pathways is a challenge for molecular and cell biology. Bioinformatics has the potential to integrate these approaches by revealing homologies in the PCD machinery of diverse taxa and evaluating their evolutionary trajectories. As the molecular mechanisms of apoptosis in model organisms are well characterised, and recent data suggest similar mechanisms operate in protozoan parasites, key questions can now be addressed. These questions include: which elements of apoptosis machinery appear to be shared between protozoan parasites and multicellular taxa and, have these mechanisms arisen through convergent or divergent evolution? We use bioinformatics to address these questions and our analyses suggest that apoptosis mechanisms in protozoan parasites and other taxa have diverged during their evolution, that some apoptosis factors are shared across taxa whilst others have been replaced by proteins with similar biochemical activities

eGIFT: Mining Gene Information from the Literature

<p>Abstract</p> <p>Background</p> <p>With the biomedical literature continually expanding, searching PubMed for information about specific genes becomes increasingly difficult. Not only can thousands of results be returned, but gene name ambiguity leads to many irrelevant hits. As a result, it is difficult for life scientists and gene curators to rapidly get an overall picture about a specific gene from documents that mention its names and synonyms.</p> <p>Results</p> <p>In this paper, we present eGIFT (<url>http://biotm.cis.udel.edu/eGIFT</url>), a web-based tool that associates informative terms, called <it>i</it>Terms, and sentences containing them, with genes. To associate <it>i</it>Terms with a gene, eGIFT ranks <it>i</it>Terms about the gene, based on a score which compares the frequency of occurrence of a term in the gene's literature to its frequency of occurrence in documents about genes in general. To retrieve a gene's documents (Medline abstracts), eGIFT considers all gene names, aliases, and synonyms. Since many of the gene names can be ambiguous, eGIFT applies a disambiguation step to remove matches that do not correspond to this gene. Another additional filtering process is applied to retain those abstracts that focus on the gene rather than mention it in passing. eGIFT's information for a gene is pre-computed and users of eGIFT can search for genes by using a name or an EntrezGene identifier. <it>i</it>Terms are grouped into different categories to facilitate a quick inspection. eGIFT also links an <it>i</it>Term to sentences mentioning the term to allow users to see the relation between the <it>i</it>Term and the gene. We evaluated the precision and recall of eGIFT's <it>i</it>Terms for 40 genes; between 88% and 94% of the <it>i</it>Terms were marked as salient by our evaluators, and 94% of the UniProtKB keywords for these genes were also identified by eGIFT as <it>i</it>Terms.</p> <p>Conclusions</p> <p>Our evaluations suggest that <it>i</it>Terms capture highly-relevant aspects of genes. Furthermore, by showing sentences containing these terms, eGIFT can provide a quick description of a specific gene. eGIFT helps not only life scientists survey results of high-throughput experiments, but also annotators to find articles describing gene aspects and functions.</p

Molecular Dynamics Simulation of the Complex PBP-2x with Drug Cefuroxime to Explore the Drug Resistance Mechanism of Streptococcus suis R61

Drug resistance of Streptococcus suis strains is a worldwide problem for both humans and pigs. Previous studies have noted that penicillin-binding protein (PBPs) mutation is one important cause of β-lactam antibiotic resistance. In this study, we used the molecular dynamics (MD) method to study the interaction differences between cefuroxime (CES) and PBP2x within two newly sequenced Streptococcus suis: drug-sensitive strain A7, and drug-resistant strain R61. The MM-PBSA results proved that the drug bound much more tightly to PBP2x in A7 (PBP2x-A7) than to PBP2x in R61 (PBP2x-R61). This is consistent with the evidently different resistances of the two strains to cefuroxime. Hydrogen bond analysis indicated that PBP2x-A7 preferred to bind to cefuroxime rather than to PBP2x-R61. Three stable hydrogen bonds were formed by the drug and PBP2x-A7, while only one unstable bond existed between the drug and PBP2x-R61. Further, we found that the Gln569, Tyr594, and Gly596 residues were the key mutant residues contributing directly to the different binding by pair wise energy decomposition comparison. By investigating the binding mode of the drug, we found that mutant residues Ala320, Gln553, and Thr595 indirectly affected the final phenomenon by topological conformation alteration. Above all, our results revealed some details about the specific interaction between the two PBP2x proteins and the drug cefuroxime. To some degree, this explained the drug resistance mechanism of Streptococcus suis and as a result could be helpful for further drug design or improvement

Prediction of Peptide Reactivity with Human IVIg through a Knowledge-Based Approach

The prediction of antibody-protein (antigen) interactions is very difficult due to the huge variability that characterizes the structure of the antibodies. The region of the antigen bound to the antibodies is called epitope. Experimental data indicate that many antibodies react with a panel of distinct epitopes (positive reaction). The Challenge 1 of DREAM5 aims at understanding whether there exists rules for predicting the reactivity of a peptide/epitope, i.e., its capability to bind to human antibodies. DREAM 5 provided a training set of peptides with experimentally identified high and low reactivities to human antibodies. On the basis of this training set, the participants to the challenge were asked to develop a predictive model of reactivity. A test set was then provided to evaluate the performance of the model implemented so far