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

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

Morphological characterisation and genetic analysis of a bi-pistil mutant ( bip ) in Medicago truncatula Gaertn.

Published online: 12 March 2008 The original publication can be found at www.springerlink.comA floral organ mutant was observed in transgenic Medicago truncatula Gaertn. plants that had two separate stigmas borne on two separate styles that emerged from a single superior carpel primordium. We propose the name bi-pistil, bip for the mutation. We believe this is the first report of such a mutation in this species. Genetic and molecular analyses of the mutant were conducted. The mutant plant was crossed to a mtapetala plant with a wild-type pistil. Expression of the mutant trait in the F1 and F2 generations indicates that the bi-pistil trait is under the control of a single recessive gene. Other modifying genes may influence its expression. The mutation was associated with the presence of a T-DNA insert consisting of the Alfalfa mosaic virus (AMV) coat protein gene in antisense orientation and the nptII selectable marker gene. It is suggested that the mutation is due to gene disruption because multiple copies of the T-DNA were observed in the mutant. The bi-pistil gene was found to be independent of the male-sterile gene, tap. This novel mutant may assist in understanding pistil development in legumes.Ramakrishnan M. Nair, David M. Peck, Ian S. Dundas, Deborah A. Samac, Adam Moore and John W. Randl

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