Soybean (Glycine max (L.) Merr.) is an important crop that provides a sustainable source of protein and oil worldwide. Soybean cyst nematode (Heterodera glycines Ichinohe) is a microscopic roundworm that feeds on the roots of soybean and is a major constraint to soybean production. This nematode causes more than US$1 billion in yield losses annually in the United States alone, making it the most economically important pathogen on soybean. Although planting of resistant cultivars forms the core management strategy for this pathogen, nothing is known about the nature of resistance. Moreover, the increase in virulent populations of this parasite on most known resistance sources necessitates the development of novel approaches for control. Here we report the map-based cloning of a gene at the Rhg4 (for resistance to Heterodera glycines 4) locus, a major quantitative trait locus contributing to resistance to this pathogen. Mutation analysis, gene silencing and transgenic complementation confirm that the gene confers resistance. The gene encodes a serine hydroxymethyltransferase, an enzyme that is ubiquitous in nature and structurally conserved across kingdoms. The enzyme is responsible for interconversion of serine and glycine and is essential for cellular one-carbon metabolism. Alleles of Rhg4 conferring resistance or susceptibility differ by two genetic polymorphisms that alter a key regulatory property of the enzyme. Our discovery reveals an unprecedented plant resistance mechanism against a pathogen. The mechanistic knowledge of the resistance gene can be readily exploited to improve nematode resistance of soybean, an increasingly important global crop.

Accommodation of donor and acceptor substrates is critical to the catalysis of (thio)phosphoryl group transfer, but there has been no systematic study of donor nucleotide recognition by kinase ribozymes, and there is relatively little known about the structural requirements for phosphorylating internal 2′OH. To address these questions, new self-phosphorylating ribozymes were selected that utilize ATP(gammaS) or GTP(gammaS) for 2′OH (thio)phosphorylation. Eight independent sequence families were identified among 57 sequenced isolates. Kinetics, donor nucleotide recognition and secondary structures were analyzed for representatives from each family. Each ribozyme was highly specific for its cognate donor. Competition assays with nucleotide analogs showed a remarkable convergence of donor recognition requirements, with critical contributions to recognition provided by the Watson–Crick face of the nucleobase, lesser contributions from donor nucleotide ribose hydroxyls, and little or no contribution from the Hoogsteen face. Importantly, most ribozymes showed evidence of significant interaction with one or more donor phosphates, suggesting that—unlike most aptamers—these ribozymes use phosphate interactions to orient the gamma phosphate within the active site for in-line displacement. All but one of the mapped (thio)phosphorylation sites are on unpaired guanosines within internal bulges. Comparative structural analysis identified three loosely-defined consensus structural motifs for kinase ribozyme active sites.

Here, we introduce and compare two new approaches to automatically detect whether the title or abstract of a PubMed publication contains HPI data, and extract the information about organisms and proteins involved in the interaction. The first approach is a feature-based supervised learning method using support vector machines (SVMs). The SVM models are trained on the features derived from the individual sentences. These features include names of the host/pathogen organisms and corresponding proteins or genes, keywords describing HPI-specific information, more general protein-protein interaction information, experimental methods and other statistical information. The language-based method employed a link grammar parser combined with semantic patterns derived from the training examples. The approaches have been trained and tested on manually curated HPI data. When compared to a naïve approach based on the existing protein-protein interaction literature mining method, our approaches demonstrated higher accuracy and recall in the classification task. The most accurate, feature-based, approach achieved 66-73% accuracy, depending on the test protocol.

The H1N1 subtype of influenza A virus has caused two of the four documented pandemics and is responsible for seasonal epidemic outbreaks, presenting a continuous threat to public health. Co-circulating antigenically divergent influenza strains significantly complicates vaccine development and use. Here, by combining evolutionary, structural, functional, and population information about the H1N1 proteome, we seek to answer two questions: (1) do residues on the protein surfaces evolve faster than the protein core residues consistently across all proteins that constitute the influenza proteome? and (2) in spite of the rapid evolution of surface residues in influenza proteins, are there any protein regions on the protein surface that do not evolve? To answer these questions, we first built phylogenetically-aware models of the patterns of surface and interior substitutions. Employing these models, we found a single coherent pattern of faster evolution on the protein surfaces that characterizes all influenza proteins. The pattern is consistent with the events of inter-species reassortment, the worldwide introduction of the flu vaccine in the early 80’s, as well as the differences caused by the geographic origins of the virus. Next, we developed an automated computational pipeline to comprehensively detect regions of the protein surface residues that were 100% conserved over multiple years and in multiple host species. We identified conserved regions on the surface of 10 influenza proteins spread across all avian, swine, and human strains; with the exception of a small group of isolated strains that affected the conservation of three proteins. Surprisingly, these regions were also unaffected by genetic variation in the pandemic 2009 H1N1 viral population data obtained from deep sequencing experiments. Finally, the conserved regions were intrinsically related to the intra-viral macromolecular interaction interfaces. Our study may provide further insights towards the identification of novel protein targets for influenza antivirals.