10 research outputs found

    Chloroplast Genome Variation in Upland and Lowland Switchgrass

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    Switchgrass (Panicum virgatum L.) exists at multiple ploidies and two phenotypically distinct ecotypes. To facilitate interploidal comparisons and to understand the extent of sequence variation within existing breeding pools, two complete switchgrass chloroplast genomes were sequenced from individuals representative of the upland and lowland ecotypes. The results demonstrated a very high degree of conservation in gene content and order with other sequenced plastid genomes. The lowland ecotype reference sequence (Kanlow Lin1) was 139,677 base pairs while the upland sequence (Summer Lin2) was 139,619 base pairs. Alignments between the lowland reference sequence and short-read sequence data from existing sequence datasets identified as either upland or lowland confirmed known polymorphisms and indicated the presence of other differences. Insertions and deletions principally occurred near stretches of homopolymer simple sequence repeats in intergenic regions while most Single Nucleotide Polymorphisms (SNPs) occurred in intergenic regions and introns within the single copy portions of the genome. The polymorphism rate between upland and lowland switchgrass ecotypes was found to be similar to rates reported between chloroplast genomes of indica and japonica subspecies of rice which were believed to have diverged 0.2–0.4 million years ago

    Single-locus EST-SSR markers for characterization of population genetic diversity and structure across ploidy levels in switchgrass (\u3ci\u3ePanicum virgatum\u3c/i\u3e L.)

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    Polyploidy, ploidal variation between populations and aneuploidy within some populations complicate population genetic analyses in switchgrass. We report 21 genic-simple sequence repeat marker loci with single-locus disomic segregation in tetraploids and apparently tetrasomic inheritance in octoploids, thus allowing population genetic analyses across ploidy levels. Based on 472 individuals sampled over four tetraploid and eight octoploid cultivars, six to 55 alleles were detected per locus with an average of 24.1. Genetic diversity was greater in octoploids than tetraploids, as expected from polysomic inheritance. One tetraploid cultivar displayed comparable diversity to the least diverse octoploid cultivars, suggesting breeding history or population history in the native stands of origin may have also affected within-cultivar diversity. Amplicon number at each locus and population relationships suggest autopolyploid origin of octoploids within upland tetraploids with significant cultivar differentiation. However, model-based Bayesian clustering of individuals indicated that closely related octoploid cultivars are difficult to identify, possibly due to slowed differentiation by polysomic inheritance. The analysis of the sampling effect indicated addition of loci is more effective for cultivar identification than more individuals sampled per cultivar. Discriminating power of loci tended to correlate with their variability. The eight loci with greatest discriminatory power within tetraploids were equally successful as 20 loci at identifying the four tetraploid cultivars. The set of markers reported in this study are useful for characterization of switchgrass germplasm and identifying population structure for association studies

    Complete Switchgrass Genetic Maps Reveal Subgenome Collinearity, Preferential Pairing and Multilocus Interactions

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    Polyploidy is an important aspect of the evolution of flowering plants. The potential of gene copies to diverge and evolve new functions is influenced by meiotic behavior of chromosomes leading to segregation as a single locus or duplicated loci. Switchgrass (Panicum virgatum) linkage maps were constructed using a full-sib population of 238 plants and SSR and STS markers to access the degree of preferential pairing and the structure of the tetraploid genome and as a step toward identification of loci underlying biomass feedstock quality and yield. The male and female framework map lengths were 1645 and 1376 cM with 97% of the genome estimated to be within 10 cM of a mapped marker in both maps. Each map coalesced into 18 linkage groups arranged into nine homeologous pairs. Comparative analysis of each homology group to the diploid sorghum genome identified clear syntenic relationships and collinear tracts. The number of markers with PCR amplicons that mapped across subgenomes was significantly fewer than expected, suggesting substantial subgenome divergence, while both the ratio of coupling to repulsion phase linkages and pattern of marker segregation indicated complete or near complete disomic inheritance. The proportion of transmission ratio distorted markers was relatively low, but the male map was more extensively affected by distorted transmission ratios and multilocus interactions, associated with spurious linkages

    A Novel Molecular Recognition Motif Necessary for Targeting Photoactivated Phytochrome Signaling to Specific Basic Helix-Loop-Helix Transcription Factors

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    The phytochrome (phy) family of sensory photoreceptors (phyA to phyE) in Arabidopsis thaliana control plant developmental transitions in response to informational light signals throughout the life cycle. The photoactivated conformer of the photoreceptor Pfr has been shown to translocate into the nucleus where it induces changes in gene expression by an unknown mechanism. Here, we have identified two basic helix-loop-helix (bHLH) transcription factors, designated PHYTOCHROME-INTERACTING FACTOR5 (PIF5) and PIF6, which interact specifically with the Pfr form of phyB. These two factors cluster tightly with PIF3 and two other phy-interacting bHLH proteins in a phylogenetic subfamily within the large Arabidopsis bHLH (AtbHLH) family. We have identified a novel sequence motif (designated the active phytochrome binding [APB] motif) that is conserved in these phy-interacting AtbHLHs but not in other noninteractors. Using the isolated domain and site-directed mutagenesis, we have shown that this motif is both necessary and sufficient for binding to phyB. Transgenic expression of the native APB-containing AtbHLH protein, PIF4, in a pif4 null mutant, rescued the photoresponse defect in this mutant, whereas mutated PIF4 constructs with site-directed substitutions in conserved APB residues did not. These data indicate that the APB motif is necessary for PIF4 function in light-regulated seedling development and suggest that conformer-specific binding of phyB to PIF4 via the APB motif is necessary for this function in vivo. Binding assays with the isolated APB domain detected interaction with phyB, but none of the other four Arabidopsis phys. Collectively, the data suggest that the APB domain provides a phyB-specific recognition module within the AtbHLH family, thereby conferring photoreceptor target specificity on a subset of these transcription factors and, thus, the potential for selective signal channeling to segments of the transcriptional network

    High-Density Single Nucleotide Polymorphism Linkage Maps of Lowland Switchgrass using Genotyping-by-Sequencing.

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    Switchgrass (Panicum virgatum L.) is a warm-season perennial grass with promising potential as a bioenergy crop in the United States. However, the lack of genomic resources has slowed the development of plant lines with optimal characteristics for sustainable feedstock production. We generated high-density single nucleotide polymorphism (SNP) linkage maps using a reduced-representation sequencing approach by genotyping 231 F1 progeny of a cross between two parents of lowland ecotype from the cultivars Kanlow and Alamo. Over 350 million reads were generated and aligned, which enabled identification and ordering of 4611 high-quality SNPs. The total lengths of the resulting framework maps were 1770 cM for the Kanlow parent and 2059 cM for the Alamo parent. These maps show collinearity with maps generated with polymerase chain reaction (PCR)-based simple-sequence repeat (SSR) markers, and new SNP markers were identified in previously unpopulated regions of the genome. Transmission segregation distortion affected all linkage groups (LGs) to differing degrees, and ordering of distorted markers highlighted several regions of unequal inheritance. Framework maps were adversely affected by the addition of distorted markers with varying severity, but distorted maps were of higher marker density and provided additional information for analysis. Alignment of these linkage maps with a draft version of the switchgrass genome assembly demonstrated high levels of collinearity and provides greater confidence in the validity of both resources. This methodology has proven to be a rapid and cost-effective way to generate high-quality linkage maps of an outcrossing species

    Additional file 1: of Genomic prediction accuracy for switchgrass traits related to bioenergy within differentiated populations

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    Figure S1. Correllelogram depicting positive (blue) and negative (red) correlations among whole plant traits. Color scale on right indicates Pearson correlation coefficient r. Figure S2. Correllelogram depicting positive (blue) and negative (red) correlations among wall composition traits determined by NIR. Color scale on right indicates Pearson correlation coefficient r. Figure S3. Boxplots of (a) ANT, (b) IVDMD, and (c) YLD for each population. Bottom and top of each box represent the first and third quartiles. Horizontal line represents the median, whiskers extend to the most extreme data point that is no more than 1.5 times the interquartile range from the box. Table S1. kin-BLUP regression statistics from 20 replicates of 5-fold CV. Table S2. Partial Least Squares regression statistics from 20 replicates of 5-fold CV. Table S3. Sparse Partial Least Squares Regression statistics from 20 replicates of 5-fold CV. Table S4. BayesB Regression statistics from 5-fold CV. Using 5000 iterations and a 1500 iteration burn-in period (see Methods Section). Table S5. Variance components for selected traits after partitioning based on dominant principal components 1–3. Table S6: ANOVA of factors influencing prediction accuracy. (DOCX 442 kb
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