Draft Genome Sequence of Pseudomonas syringae pv. syringae ALF3 Isolated from Alfalfa.

Published onlineWe report here the annotated draft genome sequence of Pseudomonas syringae pv. syringae strain ALF3, isolated in Wyoming. A comparison of this genome sequence with those of closely related strains of P. syringae adapted to other hosts will facilitate research into interactions between this pathogen and alfalfa.Biotechnology and Biological Sciences Research Council (BBSRC) provided funding to James Harrison. Funding was also provided by USDA-ARS CRIS project 5062-12210- 002-00D

Looking at Cell Wall Components with Our Customers in Mind

Fiber digestibility of alfalfa for animal nutrition is a complex system encapsulating animal, plant, and microbe biological traits. Understanding all components within the system is key to predicting forage quality. We investigated the relationship between alfalfa cell wall components and invitro neutral detergent fiber digestibility (IVNDFD) speed (16-hr) and potential (96-hr) of by cattle ruminant microbes. A composite alfalfa (Medicago sativa L.) population from seven commercial cultivars underwent two cycles of bidirectional selection for plants with low or high stem 16-hr IVNDFD and low or high stem 96-hr IVNDFD. The resulting selected populations were then evaluated by near inferred spectrometry for structural cell wall components and thier relationship with IVNDFD. Hemi-cellulose and cellulose components were found to have a greater negative correlation (-0.85 & -0.86) on the speed of digestion (16-hr IVNDFD) than lignin (-0.70). Whereas, for the overall potential of stem digestibility, lignin (-0.89) had the greatest negative correlation. The relationship between cellulose and lignin with IVNDFD was futher supported with the use of a path model. Lignin and 96-hr IVNDFD had the strongest broad sense heritability across the populations (0.74 & 0.70 respectively). Pectin components correlated positively with speed of digestion (0.41) but had limited correlation on the overall digestibility potential. As IVNDFD increased with each breeding cycle, it remained stable across environments along with concentrations of total cell wall components, lignin, hemi-cellulose, and pectin. However, the cellulose concentrations were not stable across environments. Cell wall components such as hemi-cellulose and lignin could be used as selection traits for increased IVNDFD breeding and may be a way to link invitro digestibility to plant trait genes for genomic selection

Using Microbial Community Interactions within Plant Microbiomes to Advance an Evergreen Agricultural Revolution

Innovative plant breeding and technology transfer fostered the Green Revolution (GR), which transformed agriculture worldwide by increasing grain yields in developing countries. The GR temporarily alleviated world hunger, but also reduced biodiversity, nutrient cycling, and carbon (C) sequestration that agricultural lands can provide. Meanwhile, economic disparity and food insecurity within and among countries continues. Subsequent agricultural advances, focused on objectives such as increasing crop yields or reducing the risk of a specific pest, have failed to meet food demands at the local scale or to restore lost ecosystem services. An increasing human population, climate change, growing per capita food and energy demands, and reduced ecosystem potential to provide agriculturally relevant services have created an unrelenting need for improved crop production practices. Meeting this need in a sustainable fashion will require interdisciplinary approaches that integrate plant and microbial ecology with efforts to advance crop production while mitigating effects of a changing climate. Metagenomic advances are revealing microbial dynamics that can simultaneously improve crop production and soil restoration while enhancing crop resistance to environmental change. Restoring microbial diversity to contemporary agroecosystems could establish ecosystem services while reducing production costs for agricultural producers. Our framework for examining plant-microbial interactions at multiple scales, modeling outcomes to broadly explore potential impacts, and interacting with extension and training networks to transfer microbial based agricultural technologies across socioeconomic scales, offers an integrated strategy for advancing agroecosystem sustainability while minimizing potential for the kind of negative ecological and socioeconomic feedbacks that have resulted from many widely adopted agricultural technologies

Discovering study-specific gene regulatory networks

This article has been made available through the Brunel Open Access Publishing Fund.Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets

Nematicidal activity of fervenulin isolated from a nematicidal actinomycete, Streptomyces sp. CMU-MH021, on Meloidogyne incognita

An isolate of the actinomycete, Streptomyces sp. CMU-MH021 produced secondary metabolites that inhibited egg hatch and increased juvenile mortality of the root-knot nematode Meloidogyne incognita in vitro. 16S rDNA gene sequencing showed that the isolate sequence was 99% identical to Streptomyces roseoverticillatus. The culture filtrates form different culture media were tested for nematocidal activity. The maximal activity against M. incognita was obtained by using modified basal (MB) medium. The nematicidal assay-directed fractionation of the culture broth delivered fervenulin (1) and isocoumarin (2). Fervenulin, a low molecular weight compound, shows a broad range of biological activities. However, nematicidal activity of fervenulin was not previously reported. The nematicidal activity of fervenulin (1) was assessed using the broth microdilution technique. The lowest minimum inhibitory concentrations (MICs) of the compound against egg hatch of M. incognita was 30 μg/ml and juvenile mortality of M. incognita increasing was observed at 120 μg/ml. Moreover, at the concentration of 250 μg/ml fervenulin (1) showed killing effect on second-stage nematode juveniles of M. incognita up to 100% after incubation for 96 h. Isocoumarin (2), another bioactive compound produced by Streptomyces sp. CMU-MH021, showed weak nematicidal activity with M. incognita

Using RNA-Seq for gene identification, polymorphism detection and transcript profiling in two alfalfa genotypes with divergent cell wall composition in stems

<p>Abstract</p> <p>Background</p> <p>Alfalfa, [<it>Medicago sativa </it>(L.) sativa], a widely-grown perennial forage has potential for development as a cellulosic ethanol feedstock. However, the genomics of alfalfa, a non-model species, is still in its infancy. The recent advent of RNA-Seq, a massively parallel sequencing method for transcriptome analysis, provides an opportunity to expand the identification of alfalfa genes and polymorphisms, and conduct in-depth transcript profiling.</p> <p>Results</p> <p>Cell walls in stems of alfalfa genotype 708 have higher cellulose and lower lignin concentrations compared to cell walls in stems of genotype 773. Using the Illumina GA-II platform, a total of 198,861,304 expression sequence tags (ESTs, 76 bp in length) were generated from cDNA libraries derived from elongating stem (ES) and post-elongation stem (PES) internodes of 708 and 773. In addition, 341,984 ESTs were generated from ES and PES internodes of genotype 773 using the GS FLX Titanium platform. The first alfalfa (<it>Medicago sativa</it>) gene index (MSGI 1.0) was assembled using the Sanger ESTs available from GenBank, the GS FLX Titanium EST sequences, and the <it>de novo </it>assembled Illumina sequences. MSGI 1.0 contains 124,025 unique sequences including 22,729 tentative consensus sequences (TCs), 22,315 singletons and 78,981 pseudo-singletons. We identified a total of 1,294 simple sequence repeats (SSR) among the sequences in MSGI 1.0. In addition, a total of 10,826 single nucleotide polymorphisms (SNPs) were predicted between the two genotypes. Out of 55 SNPs randomly selected for experimental validation, 47 (85%) were polymorphic between the two genotypes. We also identified numerous allelic variations within each genotype. Digital gene expression analysis identified numerous candidate genes that may play a role in stem development as well as candidate genes that may contribute to the differences in cell wall composition in stems of the two genotypes.</p> <p>Conclusions</p> <p>Our results demonstrate that RNA-Seq can be successfully used for gene identification, polymorphism detection and transcript profiling in alfalfa, a non-model, allogamous, autotetraploid species. The alfalfa gene index assembled in this study, and the SNPs, SSRs and candidate genes identified can be used to improve alfalfa as a forage crop and cellulosic feedstock.</p