Determination of Preferred pH for Root-knot Nematode Aggregation Using Pluronic F-127 Gel

Root-knot nematodes (Meloidogyne spp.) are obligate endoparasites of a wide range of plant species. The infective stage is attracted strongly to and enters host roots at the zone of elongation, but the compounds responsible for this attraction have not been identified. We developed a simple assay to investigate nematode response to chemical gradients that uses Pluronic F-127, a synthetic block copolymer that, as a 23% aqueous solution, forms a liquid at low temperature and a gel at room temperature. Test chemicals are put into a modified pipette tip, or ‘chemical dispenser,’ and dispensers are inserted into the gel in which nematodes have been dispersed. Meloidogyne hapla is attracted to pH gradients formed by acetic acid and several other Brønsted acids and aggregates between pH 4.5 and 5.4. While this pH range was attractive to all tested root-knot nematode strains and species, the level of aggregation depended on the species/strain assessed. For actively growing roots, the pH at the root surface is most acidic at the zone of elongation. This observation is consistent with the idea that low pH is an attractant for nematodes. Root-knot nematodes have been reported to be attracted to carbon dioxide, but our experiments suggest that the observed attraction may be due to acidification of solutions by dissolved CO2 rather than to CO2 itself. These results suggest that Pluronic F-127 gel will be broadly applicable for examining responses of a range of organisms to chemical gradients or to each other

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.</p> <p>Results</p> <p>We propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.</p> <p>Conclusions</p> <p>The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.</p> <p>Availability</p> <p>Datasets and software are available at <url>http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.htm</url>.</p