Lateral Flow Immunoassay for Rapid Detection of Grapevine Leafroll-Associated Virus.

Grapevine leafroll-associated virus 3 (GLRaV-3) is one of the main pathogens of grapes, causing a significant loss in yield and decrease in quality for this agricultural plant. For efficient widespread control of this infection, rapid and simple analytical techniques of on-site testing are requested as a complementary addition for the currently applied hybridization (PCR) and immunoenzyme (ELISA) approaches. The given paper presents development and approbation of the immunochromatographic assay (ICA) for rapid detection of GLRaV-3. The ICA realizes a sandwich immunoassay format with the obtaining complexes ((antibody immobilized on immunochromatographic membrane)⁻(virus in the sample)⁻(antibody immobilized on gold nanoparticles (GNP)) during sample flow along the membrane compounds of the test strip. Three preparations of GNPs were compared for detection of GLRaV-3 at different dilutions of virus-containing sample. The GNPs with maximal average diameters of 51.0 ± 7.9 nm provide GLRaV-3 detection for its maximal dilutions, being 4 times more than when using GNPs with a diameter of 28.3 ± 3.3 nm, and 8 times more than when using GNPs with a diameter of 18.5 ± 3.3 nm. Test strips have been manufactured using the largest GNPs conjugated with anti-GLRaV-3 antibodies at a ratio of 1070:1. When testing samples containing other grape wine viruses, the test strips have not demonstrated staining in the test zone, which confirms the ICA specificity. The approbation of the manufactured test strips indicated that when using ELISA as a reference method, the developed ICA is characterized by a sensitivity of 100% and a specificity of 92%. If PCR is considered as a reference method, then the sensitivity of ICA is 93% and the specificity is 92%. The proposed ICA can be implemented in one stage without the use of any additional reactants or devices. The testing results can be obtained in 10 min and detected visually. It provides significant improvement in GLRaV-3 detection, and the presented approach can be transferred for the development of test systems for other grape wine pathogens.

Probing-directed identification of novel structured RNAs.

Transcripts often harbor RNA elements, which regulate cell processes co- or post-transcriptionally. The functions of many regulatory RNA elements depend on their structure, thus it is important to determine the structure as well as to scan genomes for structured elements. State of the art ab initio approaches to predict structured RNAs rely on DNA sequence analysis. They use 2 major types of information inferred from a sequence: thermodynamic stability of an RNA structure and evolutionary footprints of base-pair interactions. In recent years, chemical probing of RNA has arisen as an alternative source of structural information. RNA probing experiments detect positions accessible to specific types of chemicals or enzymes indicating their propensity to be in a paired or unpaired state. There exist several strategies to integrate probing data into RNA secondary structure prediction algorithms that substantially improve the prediction quality. However, whether and how probing data could contribute to detection of structured RNAs remains an open question. We previously developed the energy-based approach RNASurface to detect locally optimal structured RNA elements. Here, we integrate probing data into the RNASurface energy model using a general framework. We show that the use of experimental data allows for better discrimination of ncRNAs from other transcripts. Application of RNASurface to genome-wide analysis of the human transcriptome with PARS data identifies previously undetectable segments, with evidence of functionality for some of them.

Comprehensive analysis of draft genomes of two closely related pseudomonas syringae phylogroup 2b strains infecting mono- and dicotyledon host plants.

BACKGROUND: In recent years, the damage caused by bacterial pathogens to major crops has been increasing worldwide. Pseudomonas syringae is a widespread bacterial species that infects almost all major crops. Different P. syringae strains use a wide range of biochemical mechanisms, including phytotoxins and effectors of the type III and type IV secretion systems, which determine the specific nature of the pathogen virulence. RESULTS: Strains 1845 (isolated from dicots) and 2507 (isolated from monocots) were selected for sequencing because they specialize on different groups of plants. We compared virulence factors in these and other available genomes of phylogroup 2 to find genes responsible for the specialization of bacteria. We showed that strain 1845 belongs to the clonal group that has been infecting monocots in Russia and USA for a long time (at least 50 years). Strain 1845 has relatively recently changed its host plant to dicots. CONCLUSIONS: The results obtained by comparing the strain 1845 genome with the genomes of bacteria infecting monocots can help to identify the genes that define specific nature of the virulence of P. syringae strains.

Prediction of long-term treatment outcome in HCV following 24 day PEG-IFN alpha-2b therapy using population pharmacokinetic-pharmacodynamic mixture modeling and classification analysis.

Mathematical models have been widely used for understanding the dynamics of the hepatitis C virus (HCV). We propose a method to predict final clinical outcome for 24 HIV-HCV - coinfected patients with the help of a mathematical model based on the first two weeks of PEG-IFN therapy. Applying a pharmacokinetic-pharmacodynamic (PKPD) approach, together with mixture models, to the adapted model of viral dynamics developed by Neumann et al., we have analyzed the influence of PEG-IFN on the kinetics and interaction of target cells, infected cells and virus mRNA. It was found that PEG-IFN pharmacokinetic parameters were similar in sustained virological responders and nonresponders, while the plasma PEG-IFN concentration that decreases HCV production by 50% (EC50) and the rate of infected cell death were different. The treatment outcome depended mainly on the initial viral mRNA concentration and the rate of infected cell death. The population PKPD approach with a mixture model enabled the determination of individual PKPD parameters and showed high sensitivity (93.5%) and specificity (97.4%) for the prediction of the treatment outcome.

Draft Genome Sequence of Xanthomonas arboricola Strain 3004, a Causal Agent of Bacterial Disease on Barley.

We report here the annotated genome sequence of Xanthomonas arboricola strain 3004, isolated from barley leaves with symptoms of streak and capable of infecting other plant species. We sequenced the genome of X. arboricola strain 3004 to improve the understanding of molecular mechanisms of the pathogenesis and evolution of the genus Xanthomonas.

RNASurface: fast and accurate detection of locally optimal potentially structured RNA segments.

MOTIVATION: During the past decade, new classes of non-coding RNAs (ncRNAs) and their unexpected functions were discovered. Stable secondary structure is the key feature of many non-coding RNAs. Taking into account huge amounts of genomic data, development of computational methods to survey genomes for structured RNAs remains an actual problem, especially when homologous sequences are not available for comparative analysis. Existing programs scan genomes with a fixed window by efficiently constructing a matrix of RNA minimum free energies. A wide range of lengths of structured RNAs necessitates the use of many different window lengths that substantially increases the output size and computational efforts. RESULTS: In this article, we present an algorithm RNASurface to efficiently scan genomes by constructing a matrix of significance of RNA secondary structures and to identify all locally optimal structured RNA segments up to a predefined size. RNASurface significantly improves precision of identification of known ncRNA in Bacillus subtilis. AVAILABILITY AND IMPLEMENTATION: RNASurface C source code is available from http://bioinf.fbb.msu.ru/RNASurface/downloads.html.