87 research outputs found

    Earliest life on earth

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    This volume integrates the latest findings on earliest life forms, identified and characterized in some of the oldest rocks on Earth. It places emphasis on the integration of analytical methods with observational techniques and experimental simulations

    Genetic map showing the chromosomal locations of MTAs on chromosomes 1A, 2A, 3A and 4A.

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    <p>Map distances (cM) are presented on the left side, while the corresponding marker ID and the type of trait are listed on the right side of the chromosome.</p

    Genome-wide association study and genetic diversity analysis on nitrogen use efficiency in a Central European winter wheat (<i>Triticum aestivum</i> L.) collection

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    <div><p>To satisfy future demands, the increase of wheat (<i>Triticum aestivum</i> L.) yield is inevitable. Simultaneously, maintaining high crop productivity and efficient use of nutrients, especially nitrogen use efficiency (NUE), are essential for sustainable agriculture. NUE and its components are inherently complex and highly influenced by environmental factors, nitrogen management practices and genotypic variation. Therefore, a better understanding of their genetic basis and regulation is fundamental. To investigate NUE-related traits and their genetic and environmental regulation, field trials were evaluated in a Central European wheat collection of 93 cultivars at two nitrogen input levels across three seasons. This elite germplasm collection was genotyped on DArTseq® genotypic platform to identify loci affecting N-related complex agronomic traits. To conduct robust genome-wide association mapping, the genetic diversity, population structure and linkage disequilibrium were examined. Population structure was investigated by various methods and two subpopulations were identified. Their separation is based on the breeding history of the cultivars, while analysis of linkage disequilibrium suggested that selective pressures had acted on genomic regions bearing loci with remarkable agronomic importance. Besides NUE, genetic basis for variation in agronomic traits indirectly affecting NUE and its components, moreover genetic loci underlying response to nitrogen fertilisation were also determined. Altogether, 183 marker-trait associations (MTA) were identified spreading over almost the entire genome. We found that most of the MTAs were environmental-dependent. The present study identified several associated markers in those genomic regions where previous reports had found genes or quantitative trait loci influencing the same traits, while most of the MTAs revealed new genomic regions. Our data provides an overview of the allele composition of bread wheat varieties anchored to DArTseq® markers, which will facilitate the understanding of the genetic basis of NUE and agronomically important traits.</p></div

    Hierarchical clustering of OsWAK gene expression.

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    <p>Gigante Vercelli (GV) and Vialone Nano (VN) <i>OsWAK</i> gene expression as affected by blast infection in the different treatments (blast or mock inoculated) and biological replicates (R1, R2, R3). Genes called as DEGs are indicated on the right border of the heatmap. Colored bars on the left of the heatmap mark distinct major branches in the clustering tree grouping genes with similar expression pattern. The colour scale indicates the expression value (light blue indicate higher expression value, darker blue indicates lower gene expression values). The heat map was generated with custom scripts based on heatmap.2 function as available in the ‘gplots’ Bioconductor package.</p

    Comparative Transcriptome Profiling of the Early Response to <em>Magnaporthe oryzae</em> in Durable Resistant <em>vs</em> Susceptible Rice (<em>Oryza sativa</em> L.) Genotypes

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    <div><p>Durable resistance to blast, the most significant fungal disease of rice, represents an agronomically relevant character. Gigante Vercelli (GV) and Vialone Nano (VN) are two old temperate <em>japonica</em> Italian rice cultivars with contrasting response to blast infection: GV displays durable and broad resistance while VN is highly susceptible. RNA-seq was used to dissect the early molecular processes deployed during the resistance response of GV at 24 h after blast inoculation. Differential gene expression analysis identified 1,070 and 1,484 modulated genes, of which 726 and 699 were up regulated in response to infection in GV and VN, respectively. Gene ontology (GO) enrichment analyses revealed a set of GO terms enriched in both varieties but, despite this commonality, the gene sets contributing to common GO enriched terms were dissimilar. The expression patterns of genes grouped in GV-specific enriched GO terms were examined in detail including at the transcript isoform level. GV exhibited a dramatic up-regulation of genes encoding diterpene phytoalexin biosynthetic enzymes, flavin-containing monooxygenase, class I chitinase and glycosyl hydrolase 17. The sensitivity and high dynamic range of RNA-seq allowed the identification of genes critically involved in conferring GV resistance during the early steps of defence perception-signalling. These included chitin oligosaccharides sensing factors, wall associated kinases, MAPK cascades and WRKY transcription factors. Candidate genes with expression patterns consistent with a potential role as GV-specific functional resistance (<em>R</em>) gene(s) were also identified. This first application of RNA-seq to dissect durable blast resistance supports a crucial role of the prompt induction of a battery of responses including defence-related genes as well as members of gene families involved in signalling and pathogen-related gene expression regulation.</p> </div

    Additional file 22: of Comparative transcriptome analysis of the interaction between Actinidia chinensis var. chinensis and Pseudomonas syringae pv. actinidiae in absence and presence of acibenzolar-S-methyl

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    Figure S5. Heatmap of the correlation of WGCNA modules with traits (ASM treatment and Psa inoculation). 21 modules were detected and named with colour names. The grey category is not a real module: it collect all the leftover genes not enough correlated with one of the other significant coloured modules. In each square the upper value is kME (module eigengene-based connectivity) while the lower value is the P-value of the correlation. (TIF 1752 kb
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