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A soybean cyst nematode resistance gene points to a new mechanism of plant resistance to pathogens (2012)

S. Liu ; P. K. Kandoth ; S. D. Warren ; [et al.]

Nature Publishing Group (NPG)

In: Nature. 492(7428): 256-60. doi: 10.1038/nature11651.

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Publication Date: 2012-12-14

Description: 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.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Liu, Shiming -- Kandoth, Pramod K -- Warren, Samantha D -- Yeckel, Greg -- Heinz, Robert -- Alden, John -- Yang, Chunling -- Jamai, Aziz -- El-Mellouki, Tarik -- Juvale, Parijat S -- Hill, John -- Baum, Thomas J -- Cianzio, Silvia -- Whitham, Steven A -- Korkin, Dmitry -- Mitchum, Melissa G -- Meksem, Khalid -- England -- Nature. 2012 Dec 13;492(7428):256-60. doi: 10.1038/nature11651. Epub 2012 Oct 15.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Plant, Soil and Agricultural Systems, Southern Illinois University, Carbondale, Illinois 62901, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/23235880" target="_blank"〉PubMed〈/a〉

Keywords: Amino Acid Sequence ; Animals ; DNA Mutational Analysis ; Gene Order ; Gene Silencing ; Genetic Complementation Test ; Glycine Hydroxymethyltransferase/genetics/metabolism ; Haplotypes ; *Host-Parasite Interactions ; Models, Molecular ; Molecular Sequence Data ; Nematoda/*physiology ; Plant Proteins/chemistry/*genetics/*metabolism ; Polymorphism, Genetic/genetics ; Protein Structure, Tertiary ; Quantitative Trait Loci/genetics ; Soybeans/enzymology/*genetics/*parasitology

Print ISSN: 0028-0836

Electronic ISSN: 1476-4687

Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics

Published by Nature Publishing Group (NPG)

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Long identical multispecies elements in plant and animal genomes (2012)

Reneker, J. ; Lyons, E. ; Conant, G. C. ; [et al.]

National Academy of Sciences

In: PNAS - Proceedings of the National Academy of Sciences of the United States of America. 2012; 109(19): E1183-E1191. Published 2012 Apr 10. doi: 10.1073/pnas.1121356109.

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Publication Date: 2012-04-10

Print ISSN: 0027-8424

Electronic ISSN: 1091-6490

Topics: Biology , Medicine , Natural Sciences in General

Published by National Academy of Sciences

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Patterns of amino acid conservation in human and animal immunodeficiency viruses (2016)

Voitenko, O. S., Dhroso, A., Feldmann, A., Korkin, D., Kalinina, O. V.

Oxford University Press

In: Bioinformatics

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Publication Date: 2016-09-02

Description: Motivation: Due to their high genomic variability, RNA viruses and retroviruses present a unique opportunity for detailed study of molecular evolution. Lentiviruses, with HIV being a notable example, are one of the best studied viral groups: hundreds of thousands of sequences are available together with experimentally resolved three-dimensional structures for most viral proteins. In this work, we use these data to study specific patterns of evolution of the viral proteins, and their relationship to protein interactions and immunogenicity. Results: We propose a method for identification of two types of surface residues clusters with abnormal conservation: extremely conserved and extremely variable clusters. We identify them on the surface of proteins from HIV and other animal immunodeficiency viruses. Both types of clusters are overrepresented on the interaction interfaces of viral proteins with other proteins, nucleic acids or low molecular-weight ligands, both in the viral particle and between the virus and its host. In the immunodeficiency viruses, the interaction interfaces are not more conserved than the corresponding proteins on an average, and we show that extremely conserved clusters coincide with protein–protein interaction hotspots, predicted as the residues with the largest energetic contribution to the interaction. Extremely variable clusters have been identified here for the first time. In the HIV-1 envelope protein gp120, they overlap with known antigenic sites. These antigenic sites also contain many residues from extremely conserved clusters, hence representing a unique interacting interface enriched both in extremely conserved and in extremely variable clusters of residues. This observation may have important implication for antiretroviral vaccine development. Availability and Implementation: A Python package is available at https://bioinf.mpi-inf.mpg.de/publications/viral-ppi-pred/ Contact: voitenko@mpi-inf.mpg.de or kalinina@mpi-inf.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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Long identical multispecies elements in genomes [Plant Biology] (2012)

Reneker, J., Lyons, E., Conant, G. C., Pires, J. C., Freeling, M., Shyu, C.-R., Korkin, D.

National Academy of Sciences

In: PNAS - Proceedings of the National Academy of Sciences

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Publication Date: 2012-05-09

Description: Ultraconserved elements (UCEs) are DNA sequences that are 100% identical (no base substitutions, insertions, or deletions) and located in syntenic positions in at least two genomes. Although hundreds of UCEs have been found in animal genomes, little is known about the incidence of ultraconservation in plant genomes. Using an alignment-free information-retrieval approach, we have comprehensively identified all long identical multispecies elements (LIMEs), which include both syntenic and nonsyntenic regions, of at least 100 identical base pairs shared by at least two genomes. Among six animal genomes, we found the previously known syntenic UCEs as well as previously undescribed nonsyntenic elements. In contrast, among six plant genomes, we only found nonsyntenic LIMEs. LIMEs can also be classified as either simple (repetitive) or complex (nonrepetitive), they may occur in multiple copies in a genome, and they are often spread across multiple chromosomes. Although complex LIMEs were found in both animal and plant genomes, they differed significantly in their composition and copy number. Further analyses of plant LIMEs revealed their functional diversity, encompassing elements found near rRNA and enzyme-coding genes, as well as those found in transposons and noncoding DNA. We conclude that despite the common presence of LIMEs in both animal and plant lineages, the evolutionary processes involved in the creation and maintenance of these elements differ in the two groups and are likely attributable to several mechanisms, including transfer of genetic material from organellar to nuclear genomes, de novo sequence manufacturing, and purifying selection.

Print ISSN: 0027-8424

Electronic ISSN: 1091-6490

Topics: Biology , Medicine , Natural Sciences in General

Published by National Academy of Sciences

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Fast protein binding site comparisons using visual words representation (2012)

Pang, B., Zhao, N., Korkin, D., Shyu, C.-R.

Oxford University Press

In: Bioinformatics

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Publication Date: 2012-05-10

Description: Motivation: Finding geometrically similar protein binding sites is crucial for understanding protein functions and can provide valuable information for protein–protein docking and drug discovery. As the number of known protein–protein interaction structures has dramatically increased, a high-throughput and accurate protein binding site comparison method is essential. Traditional alignment-based methods can provide accurate correspondence between the binding sites but are computationally expensive. Results: In this article, we present a novel method for the comparisons of p rotein b inding s ites using a ‘visual word s’ representation (PBSword). We first extract geometric features of binding site surfaces and build a vocabulary of visual words by clustering a large set of feature descriptors. We then describe a binding site surface with a high-dimensional vector that encodes the frequency of visual words, enhanced by the spatial relationships among them. Finally, we measure the similarity of binding sites by utilizing metric space operations, which provide speedy comparisons between protein binding sites. Our experimental results show that PBSword achieves a comparable classification accuracy to an alignment-based method and improves accuracy of a feature-based method by 36% on a non-redundant dataset. PBSword also exhibits a significant efficiency improvement over an alignment-based method. Availability: PBSword is available at http://proteindbs.rnet.missouri.edu/pbsword/pbsword.html Contact: shyuc@missouri.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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Literature mining of host-pathogen interactions: comparing feature-based supervised learning and language-based approaches (2012)

Thieu, T., Joshi, S., Warren, S., Korkin, D.

Oxford University Press

In: Bioinformatics

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Publication Date: 2012-03-20

Description: Motivation: In an infectious disease, the pathogen's strategy to enter the host organism and breach its immune defenses often involves interactions between the host and pathogen proteins. Currently, the experimental data on host–pathogen interactions (HPIs) are scattered across multiple databases, which are often specialized to target a specific disease or host organism. An accurate and efficient method for the automated extraction of HPIs from biomedical literature is crucial for creating a unified repository of HPI data. Results: 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. Availability: Both approaches are available through PHILM web-server: http://korkinlab.org/philm.html Contact: korkin@korkinlab.org Supplementary information: Supplementary data are available at Bioinformatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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