Refine
Has Fulltext
- no (7)
Language
- English (7) (remove)
Is part of the Bibliography
- yes (7)
Keywords
- Arabidopsis (1)
- Brassica napus (1)
- Genotypic differences (1)
- Leaf senescence (1)
- Molecular marker (1)
- N efficiency (1)
- Stay-green (1)
- abiotic stress (1)
- chromatin remodeling (1)
- differential network analysis (1)
Institute
High nitrogen (N) efficiency, characterized by high grain yield under N limitation, is an important agricultural trait in Brassica napus L. cultivars related to delayed senescence of older leaves during reproductive growth (a syndrome called stay-green). The aim of this study was thus to identify genes whose expression is specifically altered during N starvation-induced leaf senescence and that can be used as markers to distinguish cultivars at early stages of senescence prior to chlorophyll loss. To this end, the transcriptomes of leaves of two B. napus cultivars differing in stay-green characteristics and N efficiency were analyzed 4 days after the induction of senescence by either N starvation, leaf shading or detaching. In addition to N metabolism genes, N starvation mostly (and specifically) repressed genes related to photosynthesis, photorespiration and cell-wall structure, while genes related to mitochondrial electron transport and flavonoid biosynthesis were predominately up-regulated. A kinetic study over a period of 12 days with four B. napus cultivars differing in their stay-green characteristics confirmed the cultivar-specific regulation of six genes in agreement with their senescence behavior: the senescence regulator ANAC029, the anthocyanin synthesis-related genes ANS and DFR-like1, the ammonium transporter AMT1:4, the ureide transporter UPSS, and SPS1 involved in sucrose biosynthesis. The identified genes represent markers for the detection of cultivar-specific differences in N starvation-induced leaf senescence and can thus be employed as valuable tools in B. napus breeding. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
Leaf senescence is a developmentally controlled process, which is additionally modulated by a number of adverse environmental conditions. Nitrogen shortage is a well-known trigger of precocious senescence in many plant species including crops, generally limiting biomass and seed yield. However, leaf senescence induced by nitrogen starvation may be reversed when nitrogen is resupplied at the onset of senescence. Here, the transcriptomic, hormonal, and global metabolic rearrangements occurring during nitrogen resupply-induced reversal of senescence in Arabidopsis thaliana were analysed. The changes induced by senescence were essentially in keeping with those previously described; however, these could, by and large, be reversed. The data thus indicate that plants undergoing senescence retain the capacity to sense and respond to the availability of nitrogen nutrition. The combined data are discussed in the context of the reversibility of the senescence programme and the evolutionary benefit afforded thereby. Future prospects for understanding and manipulating this process in both Arabidopsis and crop plants are postulated.
Comparative sequence analysis has significantly altered our view on the complexity of genome organization and gene functions in different kingdoms. PLAZA 3.0 is designed to make comparative genomics data for plants available through a user-friendly web interface. Structural and functional annotation, gene families, protein domains, phylogenetic trees and detailed information about genome organization can easily be queried and visualized. Compared with the first version released in 2009, which featured nine organisms, the number of integrated genomes is more than four times higher, and now covers 37 plant species. The new species provide a wider phylogenetic range as well as a more in-depth sampling of specific clades, and genomes of additional crop species are present. The functional annotation has been expanded and now comprises data from Gene Ontology, MapMan, UniProtKB/Swiss-Prot, PlnTFDB and PlantTFDB. Furthermore, we improved the algorithms to transfer functional annotation from well-characterized plant genomes to other species. The additional data and new features make PLAZA 3.0 (http://bioinformatics.psb.ugent.be/plaza/) a versatile and comprehensible resource for users wanting to explore genome information to study different aspects of plant biology, both in model and non-model organisms.
Growth-regulating factors (GRFs) are plant-specific transcription factors that were originally identified for their roles in stem and leaf development, but recent studies highlight them to be similarly important for other central developmental processes including flower and seed formation, root development, and the coordination of growth processes under adverse environmental conditions. The expression of several GRFs is controlled by microRNA miR396, and the GRF-miRNA396 regulatory module appears to be central to several of these processes. In addition, transcription factors upstream of GRFs and miR396 have been discovered, and gradually downstream target genes of GRFs are being unraveled. Here, we review the current knowledge of the biological functions performed by GRFs and survey available molecular data to illustrate how they exert their roles at the cellular level.
Coccolithophores have influenced the global climate for over 200 million years(1). These marine phytoplankton can account for 20 per cent of total carbon fixation in some systems(2). They form blooms that can occupy hundreds of thousands of square kilometres and are distinguished by their elegantly sculpted calcium carbonate exoskeletons (coccoliths), rendering them visible from space(3). Although coccolithophores export carbon in the form of organic matter and calcite to the sea floor, they also release CO2 in the calcification process. Hence, they have a complex influence on the carbon cycle, driving either CO2 production or uptake, sequestration and export to the deep ocean(4). Here we report the first haptophyte reference genome, from the coccolithophore Emiliania huxleyi strain CCMP1516, and sequences from 13 additional isolates. Our analyses reveal a pan genome (core genes plus genes distributed variably between strains) probably supported by an atypical complement of repetitive sequence in the genome. Comparisons across strains demonstrate that E. huxleyi, which has long been considered a single species, harbours extensive genome variability reflected in different metabolic repertoires. Genome variability within this species complex seems to underpin its capacity both to thrive in habitats ranging from the equator to the subarctic and to form large-scale episodic blooms under a wide variety of environmental conditions.
Recent analyses have demonstrated that plant metabolic networks do not differ in their structural properties and that genes involved in basic metabolic processes show smaller coexpression than genes involved in specialized metabolism. By contrast, our analysis reveals differences in the structure of plant metabolic networks and patterns of coexpression for genes in (non)specialized metabolism. Here we caution that conclusions concerning the organization of plant metabolism based on network-driven analyses strongly depend on the computational approaches used.
Molecular phenotyping technologies (e.g., transcriptomics, proteomics, and metabolomics) offer the possibility to simultaneously obtain multivariate time series (MTS) data from different levels of information processing and metabolic conversions in biological systems. As a result, MTS data capture the dynamics of biochemical processes and components whose couplings may involve different scales and exhibit temporal changes. Therefore, it is important to develop methods for determining the time segments in MTS data, which may correspond to critical biochemical events reflected in the coupling of the system's components. Here we provide a novel network-based formalization of the MTS segmentation problem based on temporal dependencies and the covariance structure of the data. We demonstrate that the problem of partitioning MTS data into k segments to maximize a distance function, operating on polynomially computable network properties, often used in analysis of biological network, can be efficiently solved. To enable biological interpretation, we also propose a breakpoint-penalty (BP-penalty) formulation for determining MTS segmentation which combines a distance function with the number/length of segments. Our empirical analyses of synthetic benchmark data as well as time-resolved transcriptomics data from the metabolic and cell cycles of Saccharomyces cerevisiae demonstrate that the proposed method accurately infers the phases in the temporal compartmentalization of biological processes. In addition, through comparison on the same data sets, we show that the results from the proposed formalization of the MTS segmentation problem match biological knowledge and provide more rigorous statistical support in comparison to the contending state-of-the-art methods.