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. © 2016 The Author(s)

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. © 2015 Ignatov et al

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. © 2015 Ignatov et al

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. © 2016 The Author(s)

Risk assessment in immunotoxicology. II. Relationships between immune and host resistance tests

We have reported on the design and content of a screening battery using a 'tier' approach for detecting potential immunotoxic compounds in mice (Luster et al., Fundam. Appl. Toxicol., 10, 2-19, 1988). The data base generated from these studies, which consists of over 50 selected compounds, has heen collected and analyzed in an attempt to improve future testing strategies and provide information to aid in developing future quantitative risk assessment for immunotoxicity. In a recent study it was shown that as few as two or three immune parameters were needed to predict immunotoxicants in mice (Luster et al., Fundam. Appl. Toxicol., 18, 200-210, 1992). In particular, enumeration of lymphocyte populations and quantitation of the T-dependent antibody response were particularly beneficial. Furthermore, commonly employed apical measures (e.g., leukocyte counts, lymphoid organ weights) were fairly insensitive. The present analyses focus on the use of this data base to develop statistical models that examine the qualitative and quantitative relationship(s) between the immune function and host resistance tests. The conclusion derived from these analyses are: (1) A good correlation exists between changes in the immune tests and altered host resistance in that there were no instances where host resistance was altered without affecting an immune test(s). However, in some instances immune changes occurred without corresponding changes in host resistance. (2) No single immune test could be identified which was fully predictive for altered host resistance, although most assays were relatively good indicators (i.e., >70%). Several others, such as proliferative response to lipopolysaccharide and leukocyte counts, were found to be relatively poor indicators for host resistance changes. (3) The ability to resist infectious agent challenge is dependent upon the degrees of immunosuppression and the quantity of infectious agent administered. (4) Logistic and standard regression modeling using one extensive chemical data set from the immunosuppressive agent, cyclophosphamide, indicated that most immune function-host resistance relationships followed linear rather than linear-quadratic (threshold-like) models. For most of the relationships this could not be confirmed using a large chemical data set and, thus, a more mechanistically based approach for modeling will need to be developed. (5) Using this limited data set, methods were developed for modeling the precise quantitative relationships between changes in selected immune tests and host resistance tests