SLI-1 Cbl Inhibits the Engulfment of Apoptotic Cells in C. elegans through a Ligase-Independent Function

The engulfment of apoptotic cells is required for normal metazoan development and tissue remodeling. In Caenorhabditis elegans, two parallel and partially redundant conserved pathways act in cell-corpse engulfment. One pathway, which includes the small GTPase CED-10 Rac and the cytoskeletal regulator ABI-1, acts to rearrange the cytoskeleton of the engulfing cell. The CED-10 Rac pathway is also required for proper migration of the distal tip cells (DTCs) during the development of the C. elegans gonad. The second pathway includes the receptor tyrosine kinase CED-1 and might recruit membranes to extend the surface of the engulfing cell. Cbl, the mammalian homolog of the C. elegans E3 ubiquitin ligase and adaptor protein SLI-1, interacts with Rac and Abi2 and modulates the actin cytoskeleton, suggesting it might act in engulfment. Our genetic studies indicate that SLI-1 inhibits apoptotic cell engulfment and DTC migration independently of the CED-10 Rac and CED-1 pathways. We found that the RING finger domain of SLI-1 is not essential to rescue the effects of SLI-1 deletion on cell migration, suggesting that its role in this process is ubiquitin ligase-independent. We propose that SLI-1 opposes the engulfment of apoptotic cells via a previously unidentified pathway.National Cancer Institute (U.S.) (Award K08CA104890

Word add-in for ontology recognition: semantic enrichment of scientific literature

<p>Abstract</p> <p>Background</p> <p>In the current era of scientific research, efficient communication of information is paramount. As such, the nature of scholarly and scientific communication is changing; cyberinfrastructure is now absolutely necessary and new media are allowing information and knowledge to be more interactive and immediate. One approach to making knowledge more accessible is the addition of machine-readable semantic data to scholarly articles.</p> <p>Results</p> <p>The Word add-in presented here will assist authors in this effort by automatically recognizing and highlighting words or phrases that are likely information-rich, allowing authors to associate semantic data with those words or phrases, and to embed that data in the document as XML. The add-in and source code are publicly available at <url>http://www.codeplex.com/UCSDBioLit</url>.</p> <p>Conclusions</p> <p>The Word add-in for ontology term recognition makes it possible for an author to add semantic data to a document as it is being written and it encodes these data using XML tags that are effectively a standard in life sciences literature. Allowing authors to mark-up their own work will help increase the amount and quality of machine-readable literature metadata.</p

Simplified Method to Predict Mutual Interactions of Human Transcription Factors Based on Their Primary Structure

Background: Physical interactions between transcription factors (TFs) are necessary for forming regulatory protein complexes and thus play a crucial role in gene regulation. Currently, knowledge about the mechanisms of these TF interactions is incomplete and the number of known TF interactions is limited. Computational prediction of such interactions can help identify potential new TF interactions as well as contribute to better understanding the complex machinery involved in gene regulation. Methodology: We propose here such a method for the prediction of TF interactions. The method uses only the primary sequence information of the interacting TFs, resulting in a much greater simplicity of the prediction algorithm. Through an advanced feature selection process, we determined a subset of 97 model features that constitute the optimized model in the subset we considered. The model, based on quadratic discriminant analysis, achieves a prediction accuracy of 85.39 % on a blind set of interactions. This result is achieved despite the selection for the negative data set of only those TF from the same type of proteins, i.e. TFs that function in the same cellular compartment (nucleus) and in the same type of molecular process (transcription initiation). Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems. Conclusions: The performance of our predictor compares well to those of much more complex approaches for predicting TF and general protein-protein interactions, particularly when taking the reduced complexity of model utilisation into account

Role of Pleiotropy in the Evolution of a Cryptic Developmental Variation in Caenorhabditis elegans

Using vulval phenotypes in Caenorhabditis elegans, the authors show that cryptic genetic variation can evolve through selection for pleiotropic effects that alter fitness, and identify a cryptic variant that has conferred enhanced fitness on domesticated worms under laboratory conditions

A Dynamic View of Domain-Motif Interactions

Many protein-protein interactions are mediated by domain-motif interaction, where a domain in one protein binds a short linear motif in its interacting partner. Such interactions are often involved in key cellular processes, necessitating their tight regulation. A common strategy of the cell to control protein function and interaction is by post-translational modifications of specific residues, especially phosphorylation. Indeed, there are motifs, such as SH2-binding motifs, in which motif phosphorylation is required for the domain-motif interaction. On the contrary, there are other examples where motif phosphorylation prevents the domain-motif interaction. Here we present a large-scale integrative analysis of experimental human data of domain-motif interactions and phosphorylation events, demonstrating an intriguing coupling between the two. We report such coupling for SH3, PDZ, SH2 and WW domains, where residue phosphorylation within or next to the motif is implied to be associated with switching on or off domain binding. For domains that require motif phosphorylation for binding, such as SH2 domains, we found coupled phosphorylation events other than the ones required for domain binding. Furthermore, we show that phosphorylation might function as a double switch, concurrently enabling interaction of the motif with one domain and disabling interaction with another domain. Evolutionary analysis shows that co-evolution of the motif and the proximal residues capable of phosphorylation predominates over other evolutionary scenarios, in which the motif appeared before the potentially phosphorylated residue, or vice versa. Our findings provide strengthening evidence for coupled interaction-regulation units, defined by a domain-binding motif and a phosphorylated residue

The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text

BACKGROUND: Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them.RESULTS:A total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89 and the best AUC iP/R was 68. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35) the macro-averaged precision ranged between 50 and 80, with a maximum F-Score of 55. CONCLUSIONS: The results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heterogeneity and different granularity of method term concepts encountered in the ontology. However, combining the sophisticated techniques developed by the participants with supporting evidence strings derived from the articles for human interpretation could result in practical modules for biological annotation workflows

Coordinated Regulation of Intestinal Functions in C. elegans by LIN-35/Rb and SLR-2

LIN-35 is the sole C. elegans representative of the pocket protein family, which includes the mammalian Retinoblastoma protein pRb and its paralogs p107 and p130. In addition to having a well-established and central role in cell cycle regulation, pocket proteins have been increasingly implicated in the control of critical and diverse developmental and cellular processes. To gain a greater understanding of the roles of pocket proteins during development, we have characterized a synthetic genetic interaction between lin-35 and slr-2, which we show encodes a C2H2-type Zn-finger protein. Whereas animals harboring single mutations in lin-35 or slr-2 are viable and fertile, lin-35; slr-2 double mutants arrest uniformly in early larval development without obvious morphological defects. Using a combination of approaches including transcriptome profiling, mosaic analysis, starvation assays, and expression analysis, we demonstrate that both LIN-35 and SLR-2 act in the intestine to regulate the expression of many genes required for normal nutrient utilization. These findings represent a novel role for pRb family members in the maintenance of organ function. Our studies also shed light on the mechanistic basis of genetic redundancy among transcriptional regulators and suggest that synthetic interactions may result from the synergistic misregulation of one or more common targets

Bayesian Modeling of the Yeast SH3 Domain Interactome Predicts Spatiotemporal Dynamics of Endocytosis Proteins

A genome-scale specificity and interaction map for yeast SH3 domain-containing proteins reveal how family members show selective binding to target proteins and predicts the dynamic localization of new candidate endocytosis proteins